In recent years, generative adversarial networks (GANs) have demonstrated enormous promise in areas connected to image generation. As the model generation performance continues to improve and the generated images become more realistic, it is difficult to effectively distinguish between the real image and the generated image. Therefore, the problem of discriminating and optimizing the generated images (adversarial discrimination) has become necessary, and subsequent optimization plans are proposed based on the discrimination strategy. However, due to the nature of convolution, the two-dimensional power spectrum curve of the generated image is low overall; that is, compared with the real image, there is energy loss at each frequency (without other processing), and the curve drops rapidly and approaches zero, which is obviously different from the real image. In particular, the curve of the image generated by transposed convolution has a clear upward trend at the very high-frequency part, which is contrary to the characteristic of the real image, which is that the energy decreases with increasing frequency. By analyzing the characteristics and causes of the two-dimensional power spectrum curve of generated images, we propose a discrimination approach that leverages high-frequency curve warping and energy loss. This approach enhances the ability to discriminate generated images and enables effective distinction between real and generated images. Based on this, we present the power spectrum loss function to improve the upward warping characteristics of the very high-frequency part of the two-dimensional power spectrum curve without degrading the quality of the generated image and the high-frequency feature loss function to improve the quality of the generated image. The value and efficiency of the proposed discrimination approach in this study are demonstrated on multiple GANs models, including WGAN, WGAN-GP, and SAGAN, with the dataset CelebA, and the GANs model with encoder-decoder as the generator with the dataset CelebA-HQ. The two loss functions proposed are also demonstrated on multiple GANs models, including WGAN, WGAN-GP, and SAGAN with the dataset FFHQ. After adding the high-frequency feature loss, the Fréchet inception distance (FID) decreases by 6.97, 6.15, and 5.56, respectively. After adding the power spectrum loss, the above models can improve the upward warping characteristics of the two-dimensional power spectrum curve in the very high-frequency part of the generated image to a certain extent. The FID decreases by 17.4, 11.55, and 12.27 when the weight is fixed, and 12.66, 8.15, and 4.46 when the weight is variable, respectively.

Legal Judgment Prediction (LJP) seeks to predict case outcomes given available case information, offering practical value for both legal professionals and laypersons. However, a key limitation of existing LJP models is their limited adaptability to statutory revisions. Current SOTA models are neither designed nor evaluated for statutory revisions. To bridge this gap, we introduce LawShift, a benchmark dataset for evaluating LJP under statutory revisions. Covering 31 fine-grained change types, LawShift enables systematic assessment of SOTA models' ability to handle legal changes. We evaluate five representative SOTA models on LawShift, uncovering significant limitations in their response to legal updates. Our findings show that model architecture plays a critical role in adaptability, offering actionable insights and guiding future research on LJP in dynamic legal contexts.

ABSTRACT This paper considers a downlink multiple input single output (MISO) non‐orthogonal multiple access (NOMA) system over Nakagami‐m fading channels, where a multi‐antenna base station (BS) serves several single‐antenna users with the statistical channel state information (CSI) of each user. We propose a novel low‐complexity transmit antenna selection by head user (TAS‐head) strategy for the first time to exploit the spatial diversity of multiple antennas. Based on our proposed TAS‐head strategy, we derive a closed‐form expression of the exact outage probability (OP). We further analyse the asymptotic OP and diversity order in high signal‐to‐noise ratio (SNR) regime. Finally, we formulate a power allocation optimization problem to maximize sum throughput under outage constraints. We also design an Adam algorithm in combination with numerical differentiation method to obtain a suboptimal solution. Monte Carlo (MC) simulations verify the accuracy of our derived exact OP. Results show that our proposed TAS‐head strategy is more effective than its benchmarks (TAS‐near/far and TAS‐maj). Furthermore, we prove that PA‐TDR criterion achieves better performance than PA‐ACG in scenarios where the descending order of target data rate is the same with that of channel condition. Our designed Adam algorithm turns out to be more effective in comparison with genetic algorithm (GA) in multi‐user case. Results indicate that our proposed TAS‐head strategy is an efficient method to meet users' QoS requirements, especially in low SNR (or transmit power) regime.

Trajectory data analysis is crucial in various applications but presents significant privacy risks, as location data can reveal sensitive information. Existing privacy protection methods, such as spatiotemporal K-anonymity and L-diversity, are vulnerable to semantic inference attacks, where public data is exploited to re-identify users. To address these challenges, we propose dummy-trajectory synthesis (DTS), an efficient privacy protection scheme for location-based social networks (LBSNs). DTS enhances privacy by leveraging users’ frequent behavioral sequences to generate synthetic dummy trajectories. Unlike traditional approaches, DTS considers both geographic and semantic data by segmenting historical trajectories into time periods using the OPTICS clustering algorithm. This enables the identification of regions with specific semantic attributes and the mining of semantic trajectory sequences. DTS optimizes dummy trajectory generation by combining Euclidean distances and semantic similarity, ranking historical points and establishing transition relationships. Experimental results show that DTS significantly improves privacy protection and performance compared to existing methods, without compromising service quality. DTS offers a robust solution for protecting trajectory data privacy in LBSNs against transition probability attack for joint time periods.

Zhiwei Fei, Xiaoyu Shen, Dawei Zhu, Fengzhe Zhou, Zhuo Han, Alan Huang, Songyang Zhang, Kai Chen, Zhixin Yin, Zongwen Shen, Jidong Ge, Vincent Ng. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.

The novel fog computing can save substantial resources for resource-constrained mobile users with computation offloading. However, in the existing on-demand schemes, the fog node cannot satisfy the users' demands during peak time due to its limited resources. Therefore, an efficientallocation scheme is desirable, in which the users' priority is evaluated and sorted based on their features, e.g., trustworthiness. In terms of the trust, allocating resources and motivating users to behave cooperatively are important for network efficiency and fairness. In this paper, a long-term trust-based offloading scheme (LTOS) is proposed with a resource allocation scheme and a non-cooperative game. Firstly, a long-term trust evaluation scheme is designed considering the users' behaviors in the task assignment process. In the offloading process, the computation and transmission resources are allocated to users based on their trust values. In addition, a non-cooperative offloading game is formulated to maximize users' utilities, which considers the joint optimization of energy cost and delay. The simulation results demonstrate that our proposed long-term trust-based offloading scheme is more efficient than existing on-demand methods in terms of energy cost and delay. Moreover, the task acceptance and trust level of users can be improved with the proposed LTOS.

To make full use of the resources of multi-core CPU and improve the system performance, most processors adopt multithreaded technology. However, how to achieve the energy-efficient offloading strategy for multithreaded computing remains an open problem. In this paper, we provide a collaborative cloud and edge computing offloading strategy to reduce energy consumption. Firstly, we formulate a joint optimizing offloading decision and computation resource allocation problem. Then, we design a collaborative cloud and edge computing search offloading (CCESO) algorithm. Based on this, the energy consumption minimization problem of a single thread application is transformed into a convex optimization problem through the time allocation strategy to achieve the optimal solution of the objective function. Secondly, for multithreaded applications, the cooperation scheme and offloading strategy between multi-thread are given to reduce the energy consumption of multithreaded. Experimental data show that the proposed collaborative cloud and edge computing offloading strategy can effectively reduce energy consumption.

Nowadays, advanced smart mobile devices equipped with multi‐core central processing units for handling multithreaded (MT) applications. However, existing research mainly uses single‐thread (ST) computing to deal with applications, which limits the performance of mobile computing. To make full use of multi‐core resources, this study proposes a fine‐grained MT offloading strategy to solve the offloading problem of MT application. The strategy jointly schedules cloud computing resources, as well as local multi‐core computing and communication resources. Precisely, the authors first formulate the minimum energy consumption problem for ST offloading. Then, they prove that the problem is convex and solve it by standard convex optimisation technique. Thirdly, they extend the optimisation goals from ST applications to MT applications, and design calculation rules for MT applications to reduce computing costs. Finally, based on these calculation rules and the optimal solution for ST offloading, they develop a MT offloading strategy to solve the computation offloading problem of MT applications. Simulation results show that the proposed fine‐grained MT offloading strategy effectively reduces the minimum delay requirement of mobile computing.

Modern multi‐core mobile devices are the main application objects of mobile cloud computing (MCC). In previous works, researchers have formulated various heuristic algorithms to solve the NP problem. This work combines MCC with multi‐threaded computing (MTC) of multi‐core mobile devices to avoid NP problems and proposes an MTC‐based MCC offloading strategy. First, the authors design an MTC strategy for the application model of cloud computing. Then, they use the data transmission scheme that is dynamically adjusted according to the fading channel state. Finally, based on the MTC strategy and the optimal data transmission scheme, they obtain the MTC‐based MCC offloading strategy through a linear time searching algorithm. Simulation results show that compared with the local MTC strategy and the single‐threaded MCC offloading strategy, the MTC‐based MCC offloading strategy can significantly reduce energy consumption and improve the computing ability in multi‐threaded applications.

This paper presents a mobile computation offloading strategy, a novel framework which combines the static information and the dynamic partition to achieve low latency and energy cost. Previous works can lead to either high resource cost or inaccurate offloading decisions. In the proposed method, the static information is introduced into the strategy establishment process. In static information extraction, two offline strategies are established with the best and worst predicted communication quality. Then, strategies are compared with each other to find the same decisions, and every component is labeled as non-removable, removal or removable; in dynamic partition, the removable components are allocated into mobile terminal and cloud with the practical communication condition. Additionally, a linear time search method is proposed to find the optimal partition of application. To evaluate the strategy performance, three applications are used to test the efficiency of the strategy. The experiment demonstrates that the proposed strategy enables more resource saving in energy cost and latency than existing methods.

Understanding semantic similarity among images is the core of a wide range of computer graphics and computer vision applications. However, the visual context of images is often ambiguous as images that can be perceived with emphasis on different attributes. In this paper, we present a method for learning the semantic visual similarity among images, inferring their latent attributes and embedding them into multi-spaces corresponding to each latent attribute. We consider the multi-embedding problem as an optimization function that evaluates the embedded distances with respect to qualitative crowdsourced clusterings. The key idea of our approach is to collect and embed qualitative pairwise tuples that share the same attributes in clusters. To ensure similarity attribute sharing among multiple measures, image classification clusters are presented to, and solved by users. The collected image clusters are then converted into groups of tuples, which are fed into our group optimization algorithm that jointly infers the attribute similarity and multi-attribute embedding. Our multi-attribute embedding allows retrieving similar objects in different attribute spaces. Experimental results show that our approach outperforms state-of-the-art multi-embedding approaches on various datasets, and demonstrate the usage of the multi-attribute embedding in image retrieval application.

A quasi-passive dynamic walking robot is built to study natural and energy-efficient biped walking. The robot is actuated by MACCEPA actuators. A reinforcement learning based control method is proposed to enhance the robustness and stability of the robot's walking. The proposed method first learns the desired gait for the robot's walking on a flat floor. Then a fuzzy advantage learning method is used to control it to walk on uneven floor. The effectiveness of the method is verified by simulation results.