DATA-WA: Demand-based Adaptive Task Assignment with Dynamic Worker Availability Windows

With the rapid advancement of mobile networks and the widespread use of mobile devices, spatial crowdsourcing, which involves assigning location-based tasks to mobile workers, has gained significant attention. However, most existing research focuses on task assignment at the current moment, overlooking the fluctuating demand and supply between tasks and workers over time. To address this issue, we introduce an adaptive task assignment problem, which aims to maximize the number of assigned tasks by dynamically adjusting task assignments in response to changing demand and supply. We develop a spatial crowdsourcing framework, namely demand-based adaptive task assignment with dynamic worker availability windows, which consists of two components including task demand prediction and task assignment. In the first component, we construct a graph adjacency matrix representing the demand dependency relationships in different regions and employ a multivariate time series learning approach to predict future task demands. In the task assignment component, we adjust tasks to workers based on these predictions, worker availability windows, and the current task assignments, where each worker has an availability window that indicates the time periods they are available for task assignments. To reduce the search space of task assignments and be efficient, we propose a worker dependency separation approach based on graph partition and a task value function with reinforcement learning. Experiments on real data demonstrate that our proposals are both effective and efficient.

Preventing the Popular Item Embedding Based Attack in Federated Recommendations

Privacy concerns have led to the rise of federated recommender systems (FRS), which can create personalized models across distributed clients. However, FRS is vulnerable to poisoning attacks, where malicious users manipulate gradients to promote their target items intentionally. Existing attacks against FRS have limitations, as they depend on specific models and prior knowledge, restricting their real-world applicability. In our exploration of practical FRS vulnerabilities, we devise a model-agnostic and prior-knowledge-free attack, named PIECK (Popular Item Embedding based Attack). The core module of PIECK is popular item mining, which leverages embedding changes during FRS training to effectively identify the popular items. Built upon the core module, PIECK branches into two diverse solutions: The PIECKIPE solution employs an item popularity enhancement module, which aligns the embeddings of targeted items with the mined popular items to increase item exposure. The PIECKUEA further enhances the robustness of the attack by using a user embedding approximation module, which approximates private user embeddings using mined popular items. Upon identifying PIECK, we evaluate existing federated defense methods and find them ineffective against PIECK, as poisonous gradients inevitably overwhelm the cold target items. We then propose a novel defense method by introducing two regularization terms during user training, which constrain item popularity enhancement and user embedding approximation while preserving FRS performance. We evaluate PIECK and its defense across two base models, three real datasets, four top-tier attacks, and six general defense methods, affirming the efficacy of both PIECK and its defense.

L3TC: Leveraging RWKV for Learned Lossless Low-Complexity Text Compression

Learning-based probabilistic models can be combined with an entropy coder for data compression. However, due to the high complexity of learning-based models, their practical application as text compressors has been largely overlooked. To address this issue, our work focuses on a low-complexity design while maintaining compression performance. We introduce a novel Learned Lossless Low-complexity Text Compression method (L3TC). Specifically, we conduct extensive experiments demonstrating that RWKV models achieve the fastest decoding speed with a moderate compression ratio, making it the most suitable backbone for our method. Second, we propose an outlier-aware tokenizer that uses a limited vocabulary to cover frequent tokens while allowing outliers to bypass the prediction and encoding. Third, we propose a novel high-rank reparameterization strategy that enhances the learning capability during training without increasing complexity during inference. Experimental results validate that our method achieves 48% bit saving compared to gzip compressor. Besides, L3TC offers compression performance comparable to other learned compressors, with a 50x reduction in model parameters. More importantly, L3TC is the fastest among all learned compressors, providing real-time decoding speeds up to megabytes per second. Our code is available at https://github.com/alipay/L3TC-leveraging-rwkv-for-learned-lossless-low-complexity-text-compression.git.

MILLION: A General Multi-Objective Framework with Controllable Risk for Portfolio Management

Portfolio management is an important yet challenging task in AI for FinTech, which aims to allocate investors' budgets among different assets to balance the risk and return of an investment. In this study, we propose a general Multi-objectIve framework with controLLable rIsk for pOrtfolio maNagement (MILLION), which consists of two main phases, i.e., return-related maximization and risk control. Specifically, in the return-related maximization phase, we introduce two auxiliary objectives, i.e., return rate prediction, and return rate ranking, combined with portfolio optimization to remit the overfitting problem and improve the generalization of the trained model to future markets. Subsequently, in the risk control phase, we propose two methods, i.e., portfolio interpolation and portfolio improvement, to achieve fine-grained risk control and fast risk adaption to a user-specified risk level. For the portfolio interpolation method, we theoretically prove that the risk can be perfectly controlled if the to-be-set risk level is in a proper interval. In addition, we also show that the return rate of the adjusted portfolio after portfolio interpolation is no less than that of the min-variance optimization, as long as the model in the reward maximization phase is effective. Furthermore, the portfolio improvement method can achieve greater return rates while keeping the same risk level compared to portfolio interpolation. Extensive experiments are conducted on three real-world datasets. The results demonstrate the effectiveness and efficiency of the proposed framework.

Efficient Data-aware Distance Comparison Operations for High-Dimensional Approximate Nearest Neighbor Search

High-dimensional approximate $K$ nearest neighbor search (AKNN) is a fundamental task for various applications, including information retrieval. Most existing algorithms for AKNN can be decomposed into two main components, i.e., candidate generation and distance comparison operations (DCOs). While different methods have unique ways of generating candidates, they all share the same DCO process. In this study, we focus on accelerating the process of DCOs that dominates the time cost in most existing AKNN algorithms. To achieve this, we propose an \underline{D}ata-\underline{A}ware \underline{D}istance \underline{E}stimation approach, called \emph{DADE}, which approximates the \emph{exact} distance in a lower-dimensional space. We theoretically prove that the distance estimation in \emph{DADE} is \emph{unbiased} in terms of data distribution. Furthermore, we propose an optimized estimation based on the unbiased distance estimation formulation. In addition, we propose a hypothesis testing approach to adaptively determine the number of dimensions needed to estimate the \emph{exact} distance with sufficient confidence. We integrate \emph{DADE} into widely-used AKNN search algorithms, e.g., \emph{IVF} and \emph{HNSW}, and conduct extensive experiments to demonstrate the superiority.

Conditional Lagrangian Wasserstein Flow for Time Series Imputation

Time series imputation is important for numerous real-world applications. To overcome the limitations of diffusion model-based imputation methods, e.g., slow convergence in inference, we propose a novel method for time series imputation in this work, called Conditional Lagrangian Wasserstein Flow. The proposed method leverages the (conditional) optimal transport theory to learn the probability flow in a simulation-free manner, in which the initial noise, missing data, and observations are treated as the source distribution, target distribution, and conditional information, respectively. According to the principle of least action in Lagrangian mechanics, we learn the velocity by minimizing the corresponding kinetic energy. Moreover, to incorporate more prior information into the model, we parameterize the derivative of a task-specific potential function via a variational autoencoder, and combine it with the base estimator to formulate a Rao-Blackwellized sampler. The propose model allows us to take less intermediate steps to produce high-quality samples for inference compared to existing diffusion methods. Finally, the experimental results on the real-word datasets show that the proposed method achieves competitive performance on time series imputation compared to the state-of-the-art methods.

Heterogeneity-Aware Coordination for Federated Learning via Stitching Pre-trained blocks

Federated learning (FL) coordinates multiple devices to collaboratively train a shared model while preserving data privacy. However, large memory footprint and high energy consumption during the training process excludes the low-end devices from contributing to the global model with their own data, which severely deteriorates the model performance in real-world scenarios. In this paper, we propose FedStitch, a hierarchical coordination framework for heterogeneous federated learning with pre-trained blocks. Unlike the traditional approaches that train the global model from scratch, for a new task, FedStitch composes the global model via stitching pre-trained blocks. Specifically, each participating client selects the most suitable block based on their local data from the candidate pool composed of blocks from pre-trained models. The server then aggregates the optimal block for stitching. This process iterates until a new stitched network is generated. Except for the new training paradigm, FedStitch consists of the following three core components: 1) an RL-weighted aggregator, 2) a search space optimizer deployed on the server side, and 3) a local energy optimizer deployed on each participating client. The RL-weighted aggregator helps to select the right block in the non-IID scenario, while the search space optimizer continuously reduces the size of the candidate block pool during stitching. Meanwhile, the local energy optimizer is designed to minimize energy consumption of each client while guaranteeing the overall training progress. The results demonstrate that compared to existing approaches, FedStitch improves the model accuracy up to 20.93%. At the same time, it achieves up to 8.12% speedup, reduces the memory footprint up to 79.5%, and achieves 89.41% energy saving at most during the learning procedure.

AI-Driven Strategies for Reducing Student Withdrawal -- A Study of EMU Student Stopout

Not everyone who enrolls in college will leave with a certificate or degree, but the number of people who drop out or take a break is much higher than experts previously believed. In December 2013, there were 29 million people with some college education but no degree. That number jumped to 36 million by December of 2018, according to a new report from the National Student Clearinghouse Research Center[1]. It is imperative to understand the underlying factors contributing to student withdrawal and to assist decision-makers to identify effective strategies to prevent it. By analyzing the characteristics and educational pathways of the stopout student population, our aim is to provide actionable insights that can benefit institutions facing similar challenges. Eastern Michigan University (EMU) faces significant challenges in student retention, with approximately 55% of its undergraduate students not completing their degrees within six years. As an institution committed to student success, EMU conducted a comprehensive study of student withdrawals to understand the influencing factors. And the paper revealed a high correlation between certain factors and withdrawals, even in the early stages of university attendance. Based on these findings, we developed a predictive model that employs artificial intelligence techniques to assess the potential risk that students abandon their studies. These models enable universities to implement early intervention strategies, support at-risk students, and improve overall higher education success.

Momentum Capture and Prediction System Based on Wimbledon Open2023 Tournament Data

There is a hidden energy in tennis, which cannot be seen or touched. It is the force that controls the flow of the game and is present in all types of matches. This mysterious force is Momentum. This study introduces an evaluation model that synergizes the Entropy Weight Method (EWM) and Gray Relation Analysis (GRA) to quantify momentum's impact on match outcomes. Empirical validation was conducted through Mann-Whitney U and Kolmogorov-Smirnov tests, which yielded p values of 0.0043 and 0.00128,respectively. These results underscore the non-random association between momentum shifts and match outcomes, highlighting the critical role of momentum in tennis. Otherwise, our investigation foucus is the creation of a predictive model that combines the advanced machine learning algorithm XGBoost with the SHAP framework. This model enables precise predictions of match swings with exceptional accuracy (0.999013 for multiple matches and 0.992738 for finals). The model's ability to identify the influence of specific factors on match dynamics,such as bilateral distance run during points, demonstrates its prowess.The model's generalizability was thoroughly evaluated using datasets from the four Grand Slam tournaments. The results demonstrate its remarkable adaptability to different match scenarios,despite minor variations in predictive accuracy. It offers strategic insights that can help players effectively respond to opponents' shifts in momentum,enhancing their competitive edge.

Towards Realistic Emotional Voice Conversion using Controllable Emotional Intensity

Realistic emotional voice conversion (EVC) aims to enhance emotional diversity of converted audios, making the synthesized voices more authentic and natural. To this end, we propose Emotional Intensity-aware Network (EINet), dynamically adjusting intonation and rhythm by incorporating controllable emotional intensity. To better capture nuances in emotional intensity, we go beyond mere distance measurements among acoustic features. Instead, an emotion evaluator is utilized to precisely quantify speaker's emotional state. By employing an intensity mapper, intensity pseudo-labels are obtained to bridge the gap between emotional speech intensity modeling and run-time conversion. To ensure high speech quality while retaining controllability, an emotion renderer is used for combining linguistic features smoothly with manipulated emotional features at frame level. Furthermore, we employ a duration predictor to facilitate adaptive prediction of rhythm changes condition on specifying intensity value. Experimental results show EINet's superior performance in naturalness and diversity of emotional expression compared to state-of-the-art EVC methods.