The Internet of Things (IoT) is poised to transform human life and unleash enormous economic benefit. However, inadequate data security and trust of current IoT are seriously limiting its adoption. ...Blockchain, a distributed and tamper-resistant ledger, maintains consistent records of data at different locations, and has the potential to address the data security concern in IoT networks. While providing data security to the IoT, Blockchain also encounters a number of critical challenges inherent in the IoT, such as a huge number of IoT devices, non-homogeneous network structure, limited computing power, low communication bandwidth, and error-prone radio links. This paper presents a comprehensive survey on existing Blockchain technologies with an emphasis on the IoT applications. The Blockchain technologies which can potentially address the critical challenges arising from the IoT and hence suit the IoT applications are identified with potential adaptations and enhancements elaborated on the Blockchain consensus protocols and data structures. Future research directions are collated for effective integration of Blockchain into the IoT networks.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Image captioning is a challenging task. Meanwhile, it is important for the machine to understand the meaning of an image better. In recent years, the image captioning usually use the ...long-short-term-memory (LSTM) as the decoder to generate the sentence, and these models show excellent performance. Although the LSTM can memorize dependencies, the LSTM structure has complicated and inherently sequential across time problems. To address these issues, recent works have shown benefits of the Transformer for machine translation. Inspired by their success, we develop a Captioning Transformer (CT) model with stacked attention modules. We attempt to introduce the Transformer to the image captioning task. The CT model contains only attention modules without the dependencies of the time. It not only can memorize dependencies between the sequence but also can be trained in parallel. Moreover, we propose the multi-level supervision to make the Transformer achieve better performance. Extensive experiments are carried out on the challenging MSCOCO dataset and the proposed Captioning Transformer achieves competitive performance compared with some state-of-the-art methods.
Machine learning plays an important role in building intrusion detection systems. However, with the increase of data capacity and data dimension, the ability of shallow machine learning is becoming ...more limited. In this paper, we propose a fuzzy aggregation approach using the modified density peak clustering algorithm (MDPCA) and deep belief networks (DBNs). To reduce the size of the training set and the imbalance of the samples, MDPCA is used to divide the training set into several subsets with similar sets of attributes. Each subset is used to train its own sub-DBNs classifier. These sub-DBN classifiers can learn and explore high-level abstract features, automatically reduce data dimensions, and perform classification well. According to the nearest neighbor criterion, the fuzzy membership weights of each test sample in each sub-DBNs classifier are calculated. The output of all sub-DBNs classifiers is aggregated based on fuzzy membership weights. Experimental results on the NSL-KDD and UNSW-NB15 datasets show that our proposed model has higher overall accuracy, recall, precision and F1-score than other well-known classification methods. Furthermore, the proposed model achieves better performance in terms of accuracy, detection rate and false positive rate compared to the state-of-the-art intrusion detection methods.
Shilling attacks have been a significant vulnerability of collaborative filtering (CF) recommender systems, and trust in CF recommender algorithms has been proven to be helpful for improving the ...accuracy of system recommendations. As a few studies have been devoted to trust in this area, we explore the benefits of using trust to resist shilling attacks. Rather than simply using user-generated trust values, we propose the genre trust degree, which differ in terms of the genres of items and take both trust value and user credibility into consideration. This paper introduces different types of shilling attack methods in an attempt to study the impact of users’ trust values and behavior features on defending against shilling attacks. Meanwhile, it improves the approach used to calculate user similarities to form a recommendation model based on genre trust degrees. The performance of the genre trust-based recommender system is evaluated on the Ciao dataset. Experimental results demonstrated the superior and comparable genre trust degrees recommended for defending against different types of shilling attacks.
As an environmentally friendly and resource-rich energy, hydrogen is recognized as an ideal alternative to conventional fossil fuels. Among various methods for hydrogen production, electrochemical ...water splitting is one of the most promising approaches, for which hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) are crucial for determining the performance. Recently, much research has shown heterostructure catalysts to possess competitive electrocatalytic performance toward HER and OER. However, compared with their theoretical activities, many heterostructure catalysts remain somewhat unsatisfactory and have a long way to go. With the aim of ultimately enhancing electrocatalytic performance, recent approaches for the modification of heterostructure catalysts are summarized in this review. Typical synthetic strategies, such as design of nanostructure, chemical doping, and heterostructure-based hybrids synthesis, are discussed, and their advantages are highlighted. Finally, perspectives on the future direction of heterostructure electrocatalysts toward water splitting are presented.
Display omitted
Achieving optimum catalytic performance via the rational regulation of heterostructure catalysts has attracted extensive attention from the catalysis community recently. In this review, we comprehensively summarize recent approaches in the modification of heterostructure catalysts and their mechanism toward improved water splitting.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The main challenge of user personality recognition is low accuracy resulting from small sample size and severe sample distribution imbalance. This paper analyzes the impact of imbalanced data ...distribution and positive and negative sample overlap on the machine learning classification model. The classification model is based on the data resampling technique, which can improve the classification accuracy. These problems can be solved once the data are effectively resampled. We present a personality prediction method based on particle swarm optimization (PSO) and synthetic minority oversampling technique+Tomek Link (SMOTETomek)resampling (PSO-SMOTETomek), which, apart from effective SMOTETomek resampling of data samples, is able to execute PSO feature optimization for each set of feature combinations. Validated by simulation, our analysis reveals that the PSO-SMOTETomek method is efficient under a small dataset, and the accuracy of personality recognition is improved by up to around 10%. The results are better than those of previous similar studies. The average accuracies of the plain text dataset and the non-plain text dataset are 75.34% and 78.78%, respectively. The average accuracies of the short text dataset and the long text dataset are 75.34% and 64.25%, respectively. From the experimental results, we found that short text has a better classification effect than long text. Plain text data can still have high personality discrimination accuracy, but there is no relevant external information. The proposed model is able to facilitate the design and implementation of a personality recognition system, and the model significantly outperforms existing state-of-the-art models.
Connected vehicular cloud computing (CVCC) and the VANET can realize real-time monitoring and intelligent adjustment of traffic conditions. With the data collected by vehicles and the guidance ...provided by cloud computing platforms, the current traffic is facing new opportunities. CVCC is a mobile computing model, which extends the fixed nodes of traditional cloud infrastructure into mobile nodes composed of vehicles. Thus, compared to the traditional cloud infrastructures, CVCC requires a more complex mechanism to ensure the secure and efficient information transmission in both cloud platform-to-vehicle communication and vehicle-to-vehicle communication. Channel capacity, as the key parameter to measure the channel utilization, plays an important role in ensuring the reliability of CVCC service and the integrity of transmission data. In recent decades, the existing calculation methods could not solve the channel capacities problem in multi-participant VANETs. Different from the traditional calculation methods, we propose a novel calculation method in this article, which combines the core concepts of game theory and information theory, to calculate the channel capacities of multiple vehicular networks. The proposed method refreshes the concept of communication, which has potential applications in different services of CVCC.
Biological epidemic models, widely used to model computer virus propagations, suffer from either limited scalability to large networks, or accuracy loss resulting from simplifying approximations. In ...this paper, a discrete-time absorbing Markov process is constructed to precisely characterize virus propagations. Conducting eigenvalue analysis and Jordan decomposition to the process, we prove that the virus extinction rate, i.e., the rate at which the Markov process converges to a virus-free absorbing state, is bounded. The bounds, depending on the infection and curing probabilities, and the minimum degree of the network topology, have closed forms. We also reveal that the minimum curing probability for a given extinction rate requirement, specified through the upper bound, is independent of the explicit size of the network. As a result, we can interpret the extinction rate requirement of a large network with that of a much smaller one, evaluate its minimum curing requirement, and achieve simplifications with negligible loss of accuracy. Simulation results corroborate the effectiveness of the interpretation, as well as its analytical accuracy in large networks.
The privacy of users must be considered as the utmost priority in distributed networks. To protect the identities of users, attribute-based encryption (ABE) was presented by Sahai et al. ABE has been ...widely used in many scenarios, particularly in cloud computing. In this paper, public key encryption with equality test is concatenated with key-policy ABE (KP-ABE) to present KP-ABE with equality test (KP-ABEwET). The proposed scheme not only offers fine-grained authorization of ciphertexts but also protects the identities of users. In contrast to ABE with keyword search, KP-ABEwET can test whether the ciphertexts encrypted by different public keys contain the same information. Moreover, the authorization process of the presented scheme is more flexible than that of Ma et al. 's scheme. Furthermore, the proposed scheme achieves one-way against chosen-ciphertext attack based on the bilinear Diffie-Hellman (BDH) assumption. In addition, a new computational problem called the twin-decision BDH problem (tDBDH) is proposed in this paper. tDBDH is proved to be as hard as the decisional BDH problem. Finally, for the first time, the security model of authorization is provided, and the security of authorization based on the tDBDH assumption is proven in the random oracle model.
Zinc‐air batteries (ZABs) are promising electrochemical energy storage devices, but the inherent semi‐open configuration and catalytically dependent working principle make their performance ...vulnerable to temperature. Herein, a tunable multi‐site electrocatalyst is manufactured as the cathode for wide‐temperature adaptive aqueous ZABs, comprising Cu–Co dual metal–nitrogen–carbon‐coupled with metal nanoparticles (CuCo‐NC/NPs). The multi‐components synergistically optimize the electronic structure of active sites in CuCo‐NC/NPs, which endows them with low apparent activation energy (Ea) and high activity for oxygen reduction reaction. Moreover, the CuCo‐NC/NPs‐based aqueous ZABs demonstrate satisfactory stability over 540 h, and a high specific capacity of 806 mAh gzn−1 at 10 mA cm−2 at room temperature, outperforming that of Pt/C and many recent report catalysts based ZABs. Even at −30 and 60 °C, the assembled ZABs can deliver more than 88.1% and 95.5% of its room‐temperature specific capacity, as well as superior cycling stability, paving the way for practical applications of aqueous ZABs under extreme conditions.
A tunable multicomponent electrocatalyst comprising Cu–Co dual metal–nitrogen–carbon coupled with metal nanoparticles (CuCo‐NC/NPs) is reported. The CuCo‐NC/NPs exhibit low apparent activation energy for oxygen reduction reaction, enabling an aqueous zinc‐air battery with high peak power density, outstanding specific capacity, superior cycling stability, and excellent temperature adaptability from −30 to 60 °C.
Full text
Available for:
FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
PDF
