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Abstract Diabetic retinopathy (DR) is a common complication of diabetes and one of the major causes of blindness in the active population. Many of the complications of DR can be prevented by blood glucose control and timely treatment. Since the varieties and the complexities of DR, it is really difficult for DR detection in the time-consuming manual diagnosis. This paper is to attempt towards finding an automatic way to classify a given set of fundus images. We bring convolutional neural networks (CNNs) power to DR detection, which includes 3 major difficult challenges: classification, segmentation and detection. Coupled with transfer learning and hyper-parameter tuning, we adopt AlexNet, VggNet, GoogleNet, ResNet, and analyze how well these models do with the DR image classification. We employ publicly available Kaggle platform for training these models. The best classification accuracy is 95.68% and the results have demonstrated the better accuracy of CNNs and transfer learning on DR image classification. [ABSTRACT FROM AUTHOR]

In the Industrial Internet of Things (IIoT) era, we have billions of Internet-connected devices (also referred to as "things"), which range from unmanned/autonomous vehicles (e.g., unmanned aerial vehicle - UAV) to domestic robots to traffic sensors. The round-based blockchain time-stamping scheme is also designed to be scalable, in the sense that the constant number of bytes embedded into a blockchain is independent from the number of input documents. In conclusion, the papers included in this special section have contributed to different aspects of the blockchain literature, and hopefully benefit the readers interested in the potential application of blockchain. [Extracted from the article]

• We drew the lattice-based cryptographic primitive called NTRU into OT implementation for the first time, which assured the high computational performance and post-quantum property of our scheme. Moreover, since the parameters used in NTRU are technically configured, our protocol is scalable to some extent. • According to the IND-CCA2 model, we argued that the fastest oblivious transfer protocol known today [25] is fragile under active adaptive attack. We also found that this protocol is not semantic secure under decisional Diffie-Hellman assumption over the bilinear cyclic group. Contrarily, the above security flaws are eliminated in our scheme as proved under universal composability frame. • Our scheme is more applicable to heterogeneous and distributed environments thanks to the reusability of encoded data set. In addition, both the computation and communication performances of our protocol are preferable than that of Chou's scheme. The experiment results also imply that our protocol is adaptive to resource-constrained equipment. Security is a key concern in any IoT deployment, particularly if we have to take into consideration future attacks facilitated by the use of quantum computers. Therefore, in this paper, we present a post-quantum lightweight 1-out- n oblivious transfer (OT) protocol, based on the NTRU cryptographic primitive. Compared to the OT scheme proposed by Chou and Orlandi in LATINCRYPT 2015, our protocol is more suitable for deployment in heterogeneous and distributed environment, due to the reusability of encoded data set. Findings from the performance evaluation indicate that the proposed protocol outperforms that of Chou and Orlandi [13] protocol, in terms of computation and communication costs. [ABSTRACT FROM AUTHOR]

• This paper describes approaches of caption generation that help to integrate a form of attention with variants. There are two types of variants: a soft attention technique and a hard attention technique. • Due to recent advancements in caption generation and employment of attention in machine translation, we further investigate those models, which involve salient part of a video and will generate its caption. • By visualizing what and where the attention should be focused, we interpret the results of this framework. • We also validate the advantages of attention in caption generation by using state of art performance on benchmark dataset. The study of Natural Language Generation (NLG), especially how human beings narrate the world, assists in understanding of the visual world for surveillance. Our research proposes an effective technique to axiomatically develop multi-line textual description of visual data by exploiting deep Convolution Neural Networks (CNN). Textual description of visual data aids in providing textual tags for visual information. A human can retrieve elected videos from a repository based on visual tags. Videos contain more complex and detailed information than images and provide more language data. The proposed feats-rich model encodes the visual contents to visual and facial features using CNN architecture. Encoded features are passed to two layer LSTM units with attention mechanism, reducing the number of parameters by encompassing relevant details. Experimental results on Trecvid 2016 and UET-Surveillance dataset depict that model outperforms state-of-the-art methods by scoring BLEU score of 0.35 and 0.52, respectively. [ABSTRACT FROM AUTHOR]

Due to the surging bandwidth demand for applications like 360 ° video streaming, hypertext transfer protocol (HTTP) adaptive video streaming has been extended to fetch contents from a single server to multiple servers. However, when players share a bottleneck link, it fails to hold three critical properties: fairness , efficiency , and stability. Effective algorithms have been proposed to tackle the problem in the single-server case. We revisit the three properties and show that they degenerate in multi-server streaming. In this paper, we propose MultiFast , a protocol that offers the best user experience by achieving fair, efficient, and stable multi-server video streaming. MultiFast consists of (1) a bitrate selection algorithm that ensures fairness with concurrent connections; (2) a connection management algorithm that ensures efficiency and stability by managing connections based on network conditions. Our real-world experiments show that MultiFast achieves the best performance in fairness, efficiency, and stability metrics, outperforming its closest alternative. [Display omitted] • A comprehensive bitrate selection algorithm achieves fairness. • A connection management algorithm achieves efficiency and stability simultaneously. • An in-depth theoretical analysis guarantees the correctness of our design. • Real-world experiments demonstrate the effectiveness. [ABSTRACT FROM AUTHOR]