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This paper designs a texture-hidden QR code to prevent the illegal copying of a QR code due to its lack of anti-counterfeiting ability. Combining random texture patterns and a refined QR code, the code is not only capable of regular coding but also has a strong anti-copying capability. Based on the proposed code, a quality assessment algorithm (MAF) and a dual feature detection algorithm (DFDA) are also proposed. The MAF is compared with several current algorithms without reference and achieves a 95% and 96% accuracy for blur type and blur degree, respectively. The DFDA is compared with various texture and corner methods and achieves an accuracy, precision, and recall of up to 100%, and also performs well on attacked datasets with reduction and cut. Experiments on self-built datasets show that the code designed in this paper has excellent feasibility and anti-counterfeiting performance.

A large amount of data transmission is one of the challenges faced by communication systems. In this paper, we revisit the intelligent receiver consisting of a neural network, and we find that the intelligent receiver can reduce the data at the transmitting end while improving the decoding accuracy. Specifically, we first construct a smart receiver model, and then design two ways to reduce the data at the transmitter side, namely, end-of-transmitter data trimming and equal-interval data trimming, to investigate the decoding performance of the receiver under the different trimming methods. The simulation results show that the receiver still has an accurate decoding performance with a small amount of trimming at the end of the transmitter data, while the decoding performance of the smart receiver is better than that of the conventional receiver with complete data when the data is trimmed at equal intervals. Moreover, the receiver with equally-spaced data cropping has a lower BER when the data at the transmitter side is reduced by the same data length. This paper provides a new solution to reduce the amount of data at the transmitter side.

This paper attempts to highlight the decoding capabilities of MDP convolutional codes over the erasure channel by defining them as discrete linear dynamical systems, with which the controllability property and the observability characteristics of linear system theory can be applied, in particular those of output observability, easily described using matrix language. Those are viewed against the decoding capabilities of MDS block codes over the same channel. Not only is the time complexity better but the decoding capabilities are also increased with this approach because convolutional codes are more flexible in handling variable-length data streams than block codes, where they are fixed-length and less adaptable to varying data lengths without padding or other adjustments.

Neural encoder-decoder models for language generation can be trained to predict words directly from linguistic or non-linguistic inputs. When generating with these so-called end-to-end models, however, the NLG system needs an additional decoding procedure that determines the output sequence, given the infinite search space over potential sequences that could be generated with the given vocabulary. This survey paper provides an overview of the different ways of implementing decoding on top of neural network-based generation models. Research into decoding has become a real trend in the area of neural language generation, and numerous recent papers have shown that the choice of decoding method has a considerable impact on the quality and various linguistic properties of the generation output of a neural NLG system. This survey aims to contribute to a more systematic understanding of decoding methods across different areas of neural NLG. We group the reviewed methods with respect to the broad type of objective that they optimize in the generation of the sequence—likelihood, diversity, and task-specific linguistic constraints or goals—and discuss their respective strengths and weaknesses.

In general, the problem of building optimal convolutional codes under a certain criteria is hard, especially when size field restrictions are applied. In this paper, we confront the challenge of constructing an optimal 2D convolutional code when communicating over an erasure channel. We propose a general construction method for these codes. Specifically, we provide an optimal construction where the decoding method presented in the bibliography is considered.

This paper investigates the linguistic interdependence of Grade 3 children studying in government primary schools in northern Nigeria who are learning to read in Hausa (L1) and English (L2) simultaneously. There are few studies in the African context that consider linguistic interdependence and the bidirectional influences of literacy skills in multilingual contexts. A total of 2328 Grade 3 children were tested on their Hausa and English letter sound knowledge (phonemes) and reading decoding skills (word) after participating in a two-year English structured reading intervention programme as part of their school day. In Grade 4, these children will become English immersion learners, with English becoming the medium of instruction. Carrying out bivariate correlations, we find a large and strongly positively significant correlation between L1 and L2 test scores. Concerning bidirectionality, a feedback path model illustrates that the L1 word score predicts the L2 word score and vice versa. Multi-level modelling is then used to consider the variation in test scores. Almost two thirds of the variation in the word score is attributable to the pupil level and one third to the school level. The Hausa word score is significantly predicted through Hausa sound and English word score. English word score is significantly predicted through Hausa word and English sound score. The findings have implications for language policy and classroom instruction, showing the importance of cross-language transfer between reading skills. The overall results support bidirectionality and linguistic interdependence.

With the development of various information and communication technologies, the amount of big data has increased, and distributed file systems have emerged to store them stably. The replication technique divides the original data into blocks and writes them on multiple servers for redundancy and fault tolerance. However, there is a symmetrical space efficiency problem that arises from the need to store blocks larger than the original data. When storing data, the Erasure Coding (EC) technique generates parity blocks through encoding calculations and writes them separately on each server for fault tolerance and data recovery purposes. Even if a specific server fails, original data can still be recovered through decoding calculations using the parity blocks stored on the remaining servers. However, matrices generated during encoding and decoding are redundantly generated during data writing and recovery, which leads to unnecessary overhead in distributed file systems. This paper proposes a cache-based matrix technique that uploads the matrices generated during encoding and decoding to cache memory and reuses them, rather than generating new matrices each time encoding or decoding occurs. The design of the cache memory applies the Weighting Size and Cost Replacement Policy (WSCRP) algorithm to efficiently upload and reuse matrices to cache memory using parameters known as weights and costs. Furthermore, the cache memory table can be managed efficiently because the weight–cost model sorts and updates matrices using specific parameters, which reduces replacement cost. The experiment utilized the Hadoop Distributed File System (HDFS) as the distributed file system, and the EC volume was composed of Reed–Solomon code with parameters (6, 3). As a result of the experiment, it was possible to reduce the write, read, and recovery times associated with encoding and decoding. In particular, for up to three node failures, systems using WSCRP were able to reduce recovery time by about 30 s compared to regular HDFS systems.

In this paper, a joint early stopping criterion based on cross entropy (CE), named joint CE criterion, is presented for double-protograph low-density parity-check (DP-LDPC) codes-based joint source-channel coding (JSCC) systems in images transmission to reduce the decoding complexity and decrease the decoding delay. The proposed early stopping criterion adopts the CE from the output likelihood ratios (LLRs) of the joint decoder. Moreover, a special phenomenon named asymmetry oscillation-like convergence (AOLC) in the changing process of CE is uncovered in the source decoder and channel decoder of this system meanwhile, and the proposed joint CE criterion can reduce the impact from the AOLC phenomenon. Comparing to the counterparts, the results show that the joint CE criterion can perform well in the decoding complexity and decoding latency in the low–moderate signal-to-noise ratio (SNR) region and achieve performance improvement in the high SNR region with appropriate parameters, which also demonstrates that this system with joint CE is a low-latency and low-power system.