In this paper, we propose a new decoding algorithm for extremely skewed distribution, so that it can omit reading unnecessary bits and thus improve decoding performance. In particular, the proposed method does not require additional space to store the encoded stream. Specifically, we first reorder the compressed bit sequence as in [1], so that to support direct access without extra space. Then, we propose a labeling method to generate a temporary label corresponding to the encountered block, in order to indicate whether the length of the codeword stored in that block is determined. If it is determined, we can identify where to read the necessary bits for decoding the desired symbol. Otherwise, we read bits from the later blocks to update the labels until the codeword lengths are determined.

Range coding is a type of entropy coding widely used in modern data compressors. However, its compression and decompression processes involve multiple range adjustments, and the bitstream can only be read sequentially during decoding, resulting in quite high latency. In addition, existing input-interleaved fast implementations demand additional computational and memory overhead for the post-compression byte-swizzling step, which leads to increased compression time. In this paper, we propose a parallel range coding method that employs multiple encoders and decoders without the need for the swizzling step. It is achieved by designing a sliding window mechanism to interleave the outputs of multiple encoders, so that the positions of each encoder's outputs in the bitstream follow a predictable and ordered pattern. This design reduces encoding latency and enables each decoder to pre-locate the data it needs to read during decoding, thereby improving both compression and decompression performance. The simulation results indicate that compared to the traditional range coding and existing multi-way input-interleaved implementations (which require a large amount of memory overhead during encoding), our proposal achieves an average throughput increase of 48.03%/81.08% and 74.01%/9.14% during encoding/decoding, respectively, with almost the same compression ratios.

Reversible data hiding (RDH) has been extensively studied in the field of multimedia security. Embedding capacity is an important metric for RDH performance evaluation. However, the embedding capacity of existing methods for independent and identically distributed (i.i.d.) gray-scale signals is still not good enough. In this paper, we propose a high-capacity RDH code construction method that employs asymmetric numeral systems (ANS) coding as the underlying coding framework. Based on the proposed framework, two RDH methods are presented. First, we propose a static RDH method that takes the constant host probability mass function (PMF) as input parameters and offers high embedding performance. Then, we give a dynamic RDH method that can eliminate the need for transmitting the host PMF in advance by designing a reversible dynamic probability calculator. The simulation results on discrete normally distributed signals demonstrate that the performance of the proposed static method is very close to the expected rate-distortion bound, and the proposed dynamic method can achieve satisfactory embedding capacity without prior knowledge of host PMF at the cost of slightly sacrificing steganographic data quality. Moreover, the experimental results on gray-scale images show that the proposed static method provides higher peak signal-to-noise ratio (PSNR) values and larger embedding capacities than some state-of-the-art methods, e.g., the embedding capacity of image Lena is as high as 3.571 bits per pixel.

User-tag profiling is an effective way of mining user attributes in modern recommender systems. However, prior researches fail to extract users' precise preferences for tags in the items due to their incomplete feature-input patterns. To convert user-item interactions to user-tag preferences, we propose a novel feature-based framework named Coalition Tag Multi-View Mapping (CTMVM), which identifies and investigates two special features, Coalition Feature and Privileged Feature. The former indicates decisive tags in each click where relationships between tags in one item are treated as a coalition game. The latter represents highly informative features that only occur during training. For the coalition feature, we adopt Shapley Value based Empowerment (SVE) to model the tags in items with a game-theoretic paradigm and charge the network to straight master user preferences for essential tags. For the privileged feature, we present Privileged Knowledge Mapping (PKM) to explicitly distill privileged feature knowledge for each tag into one single embedding, which assists the model in predicting user-tag preferences at a more fine-grained level. However, the barren capacity of single embeddings limits the diverse relations between each tag and different privileged features. Therefore, we further propose Adaptive Multi-View Mapping (AMVM) model to enhance effect by handling multiple mapping networks. Excellent offline experiment results on two public and one private datasets show the out-standing performance of CTMVM. After the deployment on Alibaba large-scale recommendation systems, CTMVM achieved improvement by 10.81% and 6.74% in terms of Theme-CTR and Item-CTR respectively, which validates the effectiveness of taking in the two particular features for training.