Previous anti-spoofing methods have used either pseudo maps or user-defined labels, and the performance of each approach depends on the accuracy of the third party networks generating pseudo maps and the way in which the users define the labels. In this paper, we propose a liveness score-based regression network for overcoming the dependency on third party networks and users. First, we introduce a new labeling technique, called pseudo-discretized label encoding for generating discretized labels indicating the amount of information related to real images. Secondly, we suggest the expected liveness score based on a regression network for training the difference between the proposed supervision and the expected liveness score. Finally, extensive experiments were conducted on four face anti-spoofing benchmarks to verify our proposed method on both intra-and cross-dataset tests. The experimental results show our approach outperforms previous methods.

Federated learning (FL) is a promising approach for enhancing data privacy preservation, particularly for authentication systems. However, limited round communications, scarce representation, and scalability pose significant challenges to its deployment, hindering its full potential. In this paper, we propose ‘ProtoFL’, Prototypical Representation Distillation based unsupervised Federated Learning to enhance the representation power of a global model and reduce round communication costs. Additionally, we introduce a local one-class classifier based on normalizing flows to improve performance with limited data. Our study represents the first investigation of using FL to improve one-class classification performance. We conduct extensive experiments on five widely used benchmarks, namely MNIST, CIFAR-10, CIFAR-100, ImageNet-30, and Keystroke-Dynamics, to demonstrate the superior performance of our proposed framework over previous methods in the literature.

Backchannel (BC), a short reaction signal of a listener to a speaker's utterances, helps to improve the quality of the conversation.Several studies have been conducted to predict BC in conversation; however, the utilization of advanced natural language processing techniques using lexical information presented in the utterances of a speaker has been less considered.To address this limitation, we present a BC prediction model called BPM_MT (Backchannel prediction model with multitask learning), which utilizes KoBERT, a pre-trained language model.The BPM_MT simultaneously carries out two tasks at learning: 1) BC category prediction using acoustic and lexical features, and 2) sentiment score prediction based on sentiment cues.BPM_MT exhibited 14.24% performance improvement compared to the existing baseline in the four BC categories: continuer, understanding, empathic response, and No BC.In particular, for empathic response category, a performance improvement of 17.14% was achieved.

Diet planning is a basic and regular human activity. Previous studies have considered diet planning a combinatorial optimization problem to generate solutions that satisfy a diet's nutritional requirements. However, this approach does not consider the composition of diets, which is critical for diet recipients' to accept and enjoy menus with high nutritional quality. Without this consideration, feasible solutions for diet planning could not be provided in practice. This suggests the necessity of diet planning with machine learning, which extracts implicit composition patterns from real diet data and applies these patterns when generating diets. This work is original research that defines diet planning as a machine learning problem; we describe diets as sequence data and solve a controllable sequence generation problem. Specifically, we develop the Teacher-forced REINFORCE algorithm to connect neural machine translation and reinforcement learning for composition compliance with nutrition enhancement in diet generation. Through a real-world application to diet planning for children, we validated the superiority of our work over the traditional combinatorial optimization and modern machine learning approaches, as well as human (i.e., professional dietitians) performance. In addition, we construct and open the databases of menus and diets to motivate and promote further research and development of diet planning with machine learning. We believe this work with data science will contribute to solving economic and social problems associated with diet planning.

String pattern matching with finite automata (FAs) is a well-established method across many areas in computer science. Until now, data dependencies inherent in the pattern matching algorithm have hampered effective parallelization. To overcome the dependency-constraint between subsequent matching steps, simultaneous deterministic finite automata (SFAs) have been recently introduced. Although an SFA facilitates parallel FA matching, SFA construction itself is limited by the exponential state-growth problem, which makes sequential SFA construction intractable for all but the smallest problem sizes.In this paper, we propose several optimizations to leverage parallelism, improve cache and memory utilization and greatly reduce the processing steps required to construct an SFA. We introduce fingerprints and hashing for efficient comparisons of SFA states. Kernels of ×86 SIMD-instructions facilitate cache-locality and leverage data-parallelism with the construction of SFA states. Our parallelization for shared-memory multicores employs lock-free synchronization to minimize cache-coherence overhead. Our dynamic work-partitioning scheme employs work-stealing with thread-local work-queues. The structural properties of FAs allow efficient compression of SFA states. Our construction algorithm dynamically switches to in-memory compression of SFA states for problem sizes which approach the main memory size limit of a given system.We evaluate our approach with patterns from the PROSITE protein database. We achieve speedups of up to 312× on a 64-core AMD system and 193× on a 44-core (88 hyperthreads) Intel system. Our SFA construction algorithm shows scalability on both evaluation platforms.

Summary The JPEG format employs Huffman codes to compress the entropy data of an image. Huffman codewords are of variable length, which makes parallel entropy decoding a difficult problem. To determine the start position of a codeword in the bitstream, the previous codeword must be decoded first. We present JParEnt, a new approach to parallel entropy decoding for JPEG decompression on heterogeneous multicores. JParEnt conducts JPEG decompression in two steps: (1) an efficient sequential scan of the entropy data on the CPU to determine the start‐positions (boundaries) of coefficient blocks in the bitstream, followed by (2) a parallel entropy decoding step on the graphics processing unit (GPU). The block boundary scan constitutes a reinterpretation of the Huffman‐coded entropy data to determine codeword boundaries in the bitstream. We introduce a dynamic workload partitioning scheme to account for GPUs of low compute power relative to the CPU. This configuration has become common with the advent of SoCs with integrated graphics processors (IGPs). We leverage additional parallelism through pipelined execution across CPU and GPU. For systems providing a unified address space between CPU and GPU, we employ zero‐copy to completely eliminate the data transfer overhead. Our experimental evaluation of JParEnt was conducted on six heterogeneous multicore systems: one server and two desktops with dedicated GPUs, one desktop with an IGP, and two embedded systems. For a selection of more than 1000 JPEG images, JParEnt outperforms the SIMD–implementation of the libjpeg‐turbo library by up to a factor of 4.3×, and the previously fastest JPEG decompression method for heterogeneous multicores by up to a factor of 2.2×. JParEnt's entropy data scan consumes 45% of the entropy decoding time of libjpeg‐turbo on average. Given this new ratio for the sequential part of JPEG decompression, JParEnt achieves up to 97% of the maximum attainable speedup (95% on average). On the IGP‐based desktop platform, JParEnt achieves energy savings of up to 45% compared to libjpeg‐turbo's SIMD‐implementation.

In many agricultural applications, PolSAR data are widely used because they can be decomposed into various scattering components, which can be of help in observing the characteristics of agricultural areas. Recently, studies have been conducted to find suitable polarimetric parameters for specific applications. This paper tried to find appropriate polarimetric parameters for line extraction from agricultural areas as the line features are among the basic features of the surface. Towards this end, various polarimetric parameters were produced using polarimetric decomposition methods. LSD was used to extract lines from each polarimetric parameter image over agricultural areas, without any threshold value selection. The comparison of the line extraction result from each polarimetric parameter with the others was conducted through quantitative evaluation. Through this process, three parameters of the Pauli decomposition is found to be the suitable polarimetric parameters for line extraction from agricultural areas.

The crop boundary data provide the basic information for agricultural management. In this study, line-based boundary enhancement and extraction methods are proposed to delineate detailed paddy boundaries. Line extraction is performed based on the boundary enhancement result and Hough line extraction using RapidEye satellite image. The proposed line-based method is efficient in detecting the paddy boundaries while preserving linearity. In addition, the proposed method adopts an automated boundary detection process except for line editing. Therefore, the proposed method economically provides information on the paddy boundaries that can be utilized as the basic spatial unit for yield analysis, ownership management, and precision farming.