Miracle-dz/UniFAR-FTUs huggingface dataset:
CARPG: Cross-City Knowledge Transfer for Traffic Accident Prediction via Attentive Region-Level Parameter Generation

Traffic accident prediction is a crucial problem for public safety, emergency treatment, and urban management. Existing works leverage extensive data collected from city infrastructures to achieve encouraging performance based on various machine learning techniques but cannot achieve a good performance in situations with limited data (i.e., data scarcity). Recent developments in transfer learning bring a new opportunity to solve the data scarcity problem. In this paper, we design a novel cross-city transfer learning framework named CARPG for predicting traffic accidents in data-scarce cities. We address the unique challenge of predicting traffic accidents caused by its two fundamental characteristics, i.e., spatial heterogeneity and inherent rareness, which result in the biased performance of the state-of-the-art transfer learning methods. Specifically, we build cross-city region connections by jointly learning the spatial region representations for both source and target cities with an inter-city global graph knowledge transfer process. Further, we design an efficient attention-based parameter-generating mechanism to learn region-specific traffic accident patterns, while controlling the total number of parameters. Built upon that, we ensure that only relevant patterns are transferred to each target region during the knowledge transfer process and further to be fine-tuned. We conduct extensive experiments on three real-world datasets, and the evaluation results demonstrate the superiority of our framework compared with state-of-the-art baseline models.
Practice on Effectively Extracting NLP Features for Click-Through Rate Prediction

Click-through rate (CTR) prediction is critical for industrial applications such as recommendation system and online advertising. Practically, there are a series of research proved that Natural Language Processing (NLP) features are helpful to improve CTR task performance. As these works show, there are different ways to extract NLP features. For example, keywords of item title are extracted as open-box feature by term frequency? inverse document frequency (tf-idf) method while item semantic embedding is extracted as black-box feature by shallow models (\emphe.g., word2vec) or deep learning models (e.g., BERT). However, these NLP models are pre-trained on NLP task, which is very different from the CTR task. Then it leads to the limited improvement of Area Under the ROC Curve (AUC) in CTR task. On the other hand, traditional NLP models for CTR task only consider open-box feature or black-box feature separately, which also leads to the discounted effect. Lastly, many NLP models are mainly used to extract semantic features only on item side. These methods take little account of user side information, or only IDs related features (\emphe.g., item's IDs) in user behavior sequence are introduced. In our work, we proposed a new network named BERT Attention method based on both Item and User information (BAIU). The target of BAIU is consistent with the CTR task, which is helpful to extract more effective NLP features. Also, both open-box and black-box features are simultaneously extracted by this network, which makes the model to learn more useful NLP features for CTR task and also makes the model more interpretable. Extensive experiments on both public data and our commercial data validate the effectiveness of our approach. Finally, the online experiment brings 2.2% gain of CTR on recommendation.
Joint Rebalancing and Charging for Shared Electric Micromobility Vehicles with Energy-informed Demand

Shared electric micromobility (e.g., shared electric bikes and electric scooters), as an emerging way of urban transportation, has been increasingly popular in recent years. However, managing thousands of micromobility vehicles in a city, such as rebalancing and charging vehicles to meet spatial-temporally varied demand, is challenging. Existing management frameworks generally consider demand as the number of requests without the energy consumption of these requests, which can lead to less effective management. To address this limitation, we design RECOMMEND, a rebalancing and charging framework for shared electric micromobility vehicles with energy-informed demand to improve the system revenue. Specifically, we first re-define the demand from the perspective of energy consumption and predict the future energy-informed demand based on the state-of-the-art spatial-temporal prediction method. Then we fuse the predicted energy-informed demand into different components of a rebalancing and charging framework based on reinforcement learning. We evaluate the RECOMMEND system with 2-month real-world electric micromobility system operation data. Experimental results show that our method can be easily integrated into a general RL framework and outperform state-of-the-art baselines by at least 26.89% in terms of net revenue.
Simplifying Temporal Heterogeneous Network for Continuous-Time Link prediction

Temporal heterogeneous networks (THNs) investigate the structural interactions and their evolution over time in graphs with multiple types of nodes or edges. Existing THNs describe evolving networks as a sequence of graph snapshots and adopt mechanisms from static heterogeneous networks to capture the spatial-temporal correlation. However, these works are confined to the discrete-time setting and the implementation of stacked mechanisms often introduces a high level of complexity, both conceptually and computationally. Here, we conduct comprehensive examinations and propose STHN, a simplifying THN for continuous-time link prediction. Concretely, to integrate continuous dynamics, we maintain a historical interaction memory for each node. A link encoder that incorporates two components - type encoding and relative time encoding - is introduced to encapsulate implicit heterogeneous characteristics of interaction and extract the most informative temporal information. We further propose to use a patching technique that assists with Transformer feature extractor to support the interaction sequence with long histories. Extensive experiments on three real-world datasets empirically demonstrate that STHN outperforms state-of-the-art methods with competitive task accuracy and predictive efficiency on both transductive and inductive settings.
Exploring Locomotion Methods with Upright Redirected Views for VR Users in Reclining & Lying Positions

Using VR in reclining & lying positions is getting common for users, but upward views caused by posture have to be redirected to be parallel to the ground as when users are standing. This affects users’ locomotion performances in VR due to potential physical restrictions, and the visual-vestibular-proprioceptive conflict. This paper is among the first to investigate the suited locomotion methods and how reclining & lying positions and redirection affect them in such conditions. A user-elicitation study was carried out to construct a set of locomotion methods based on users’ preferences when they were in different reclining & lying positions. A second study developed user-preferred ’tapping’ and ’chair rotating’ gestures, by evaluating their performances at various body reclining angles, we measured the general impacts of posture and redirection. The results showed that these methods worked effectively, but exposed some shortcomings, and users performed worst at 45° reclining angles. Finally, four upgraded methods were designed and verified to improve the locomotion performances.
MDCC: A Multimodal Dynamic Dataset for Donation-based Crowdfunding Campaigns

Crowdfunding platforms have become pivotal financial support avenues for diverse causes, yet the success rates are surprisingly low. Previous research has largely focused on reward-based crowdfunding, leaving donation-based platforms under-studied. In addition, the roles of multimodal data (e.g., textual descriptions and visual photos) and dynamic elements (e.g., sequences of donations, project updates, and comments) in influencing campaign success have been largely overlooked. This paper introduces MDCC, a Multimodal Dynamic dataset for donation-based Crowdfunding Campaigns, collected from 14,961 projects on GoFundMe, incorporates multimodal project information and captures project dynamics, thus providing a comprehensive tool for analyzing donation-based crowdfunding. The dataset is expected to inspire innovative methodologies and facilitate understanding of project success determinants. Our preliminary experiments demonstrate the significance of multimodal and dynamic crowdfunding data on predicting the success of donation-based projects.
Encapsulated Functions: Fortifying Rust's FFI in Embedded Systems

Memory-safe languages like Rust are increasingly popular for systems development. Nonetheless, practical systems must interact with code written in memory-unsafe languages. This is especially true in security and safety-critical embedded systems, where subsystems such as cryptographic implementations are subject to industrial and governmental certification requirements. Direct interactions with such libraries, however, expose memory-safe languages to significant risks: Any single bug in either the foreign code or the cross-language interactions may arbitrarily violate the memory safety of the wrapping language. We present Encapsulated Functions, a framework for safely invoking untrusted code in a memory-safe system with minimal overheads. Encapsulated Functions combines hardware-based memory protection mechanisms with a set of Rust type abstractions to facilitate safe interactions with untrusted and unmodified third-party libraries.
Combating Ad Fatigue via Frequency-Recency Features in Online Advertising Systems

Online advertising is a driving force of Internet services today. One of the main challenges in advertising systems is finding the right balance between user experience and overall revenue. In this paper we address one of the problems that negatively impacts the user experience, specifically, the repeated display of identical ads to the same user. This problem leads to the phenomenon called "ad fatigue", characterized by diminished interest in the ad, resulting in a decrease in the click-through rate (CTR) as users encounter the same ad repeatedly. Naive solutions such as placing a hard limit on the number of times a specific ad is displayed to a specific user, usually come at the cost of reduced revenue. To address the ad fatigue problem, we introduce a new family of features, called FoRI (Frequency over Recent Intervals). FoRI features integrate information about frequency and recency of previous user-ad interactions within the CTR prediction model. This approach involves allocating these interactions to unevenly distributed time intervals, enabling a higher emphasis on more recent interactions. Furthermore, we introduce new metrics to assess ad fatigue in terms of the repetitiveness and novelty of the displayed ads. We conducted a comprehensive large-scale online evaluation which shows that integrating FoRI features into our CTR prediction model offers two-fold benefits. Firstly, it improves user experience by reducing the occurrence of repeated ads by 15%, and increasing the exposure to unseen ads by 5% (ads not previously displayed to the user), leading to a substantial boost in CTR. Secondly, it significantly increases revenue.
Water retention and volumetric characteristics of intact and re-compacted loess

A laboratory testing program was conducted to investigate the effects of microstructure on the water retention curve (WRC) and wetting–drying induced volume change in loess. The axis translation and vapor equilibrium techniques were adopted to control suction in the range of 0–400 kPa and 4–140 MPa, respectively. Hysteresis in the WRC of loess was observed for the entire range of suction studied. Compared to re-compacted loess, intact loess exhibits a more pronounced hysteresis in the suction range below 20 kPa, which can be explained by the ink-bottle pore neck effect or constricted pores. The hypothesis is supported by microstructural evidence of mercury intrusion porosimetry and scanning electron microscopy tests. However, re-compacted loess exhibits larger hysteresis than intact loess for suctions above 30 kPa. A conceptual model was introduced, which links WRC to the corresponding pore-size density (PSD) function. Regarding volume change, more noticeable drying-induced shrinkage, but yielding at a lo.
Comparative case study of change in the electricity industries in New Zealand and The Gambia

1 Chapter 1 Introduction . 2 1.1 Overview . 2 1.2 Research Design . 4 1.3 Significance of the Study . 5 1.4 The Gambia and New Zealand Electricity Industries . 7 1.5 Overview of the Thesis . 9 Chapter 2 Research Methodology . -. 12 2.
PCENet: Psychological Clues Exploration Network for Multimodal Personality Assessment

Multimodal personality assessment aims to identify and express human personality traits in videos. Existing methods primarily focus on multimodal fusion while ignoring the inherent psychological clues essential for this interdisciplinary task. Modality clues: personality traits are stable over time due to their genetic and environmental origins, resulting in stable personality traits in the multimodal data. Trait clues: multiple traits often co-occur with non-negligible correlations, which can collectively aid trait identification. To simultaneously capture the above psychological clues, we propose a novel Psychological Clues Exploration Network (PCENet) for multimodal personality assessment, which is a human-like judgment paradigm with more generalization capability. Specifically, we first devise a multimodal hierarchical disentanglement, which clearly aligns stable representations among different modalities and separates the mutability of each modality. Subsequently, a Transformer-backbone decoder equipped with modality-to-trait attention is exploited to adaptively generate a tailored representation for each trait with the guidance of trait semantics. The trait semantics are obtained by exploiting trait correlations through self-attention. Extensive experiments on the First Impression V2 dataset demonstrate that our PCENet outperforms the state-of-the-art methods for multimodal personality assessment.
Engraft: An API for Live, Rich, and Composable Programming

Live & rich tools can support a diversity of domain-specific programming tasks, from visualization authoring to data wrangling. Real-world programming, however, requires performing multiple tasks in concert, calling for the use of multiple tools alongside conventional code. Programmers lack environments capable of composing live & rich tools to support these situations. To enable this composition, we contribute Engraft, a component-based API that allows live & rich tools to be embedded within larger environments like computational notebooks. Through recursive embedding of components, Engraft enables several new forms of composition: not only embedding tools inside environments, but also embedding environments within each other and embedding tools and environments in the outside world, including conventional codebases. We demonstrate Engraft with examples from diverse domains, including web-application development, command-line scripting, and physics education. By providing composability, Engraft can help cultivate a cycle of use and innovation in live & rich programming.
MORPHER: Structural Transformation of Ill-formed Rows

Open data portals contain a plethora of data files, with comma-separated value (CSV) files being particularly popular with users and businesses due to their flexible standard. However, this flexibility comes with much responsibility for data consumers, as many files contain various structural problems, e.g., a different number of cells across data rows, multiple value formats within the same column, different variants of quoted fields due to user specifications, etc. We refer to rows that contain such structural inconsistencies as ill-formed. Consequently, ingesting them into a host system, such as a database or an analytics platform, often requires prior data preparation steps. We propose to demonstrate MORPHER, a desktop-based system that incorporates our state-of-the-art error detection system, SURAGH and extends it to also clean the files at hand. MORPHER facilitates ingesting CSV files by automatically identifying and cleaning ill-formed rows while preserving all data. It comprises three key components: 1) The pattern modeler, which generates syntax-based patterns for each row of the input file. The system uses these patterns to classify rows into ill-formed and well-formed. 2) The pattern classifier obtains row patterns for ill-formed rows and uses them to distinguish ill-formed but wanted rows from ill-formed unwanted rows. 3) The pattern wrangler transforms the identified wanted rows into well-formed rows, effectively repairing a wide range of formatting problems.
HRTF Estimation in the Wild

Head Related Transfer Functions (HRTFs) play a crucial role in creating immersive spatial audio experiences. However, HRTFs differ significantly from person to person, and traditional methods for estimating personalized HRTFs are expensive, time-consuming, and require specialized equipment. We imagine a world where your personalized HRTF can be determined by capturing data through earbuds in everyday environments. In this paper, we propose a novel approach for deriving personalized HRTFs that only relies on in-the-wild binaural recordings and head tracking data. By analyzing how sounds change as the user rotates their head through different environments with different noise sources, we can accurately estimate their personalized HRTF. Our results show that our predicted HRTFs closely match ground-truth HRTFs measured in an anechoic chamber. Furthermore, listening studies demonstrate that our personalized HRTFs significantly improve sound localization and reduce front-back confusion in virtual environments. Our approach offers an efficient and accessible method for deriving personalized HRTFs and has the potential to greatly improve spatial audio experiences.
Enhanced Template-Free Reaction Prediction with Molecular Graphs and Sequence-based Data Augmentation

Retrosynthesis and forward synthesis prediction are fundamental challenges in organic synthesis, computer-aided synthesis planning (CASP), and computer-aided drug design (CADD). The objective is to predict plausible reactants for a given target product and its corresponding inverse task. With the rapid development of deep learning, numerous approaches have been proposed to solve this problem from various perspectives. The methods based on molecular graphs benefit from their rich features embedded inside but face difficulties in applying existing sequence-based data augmentations due to the permutation invariance of graph structures. In this work, we propose SeqAGraph, a template-free approach that annotates input graphs with its root atom index to ensure compatibility with sequence-based data augmentation. The matrix product for global attention in graph encoders is implemented by indexing, elementwise product, and aggregation to fuse global attention with local message passing without graph padding. Experiments demonstrate that SeqAGraph fully benefits from molecular graphs and sequence-based data augmentation and achieves state-of-the-art accuracy in template-free approaches.
AutoBuild: Automatic Community Building Labeling for Last-mile Delivery

Fine-grained community-building information, such as building names and accurate geographical coordinates, is critical for a range of practical applications like navigation and door-to-door services (e.g., on-demand delivery and last-mile delivery). A common practice of traditional methods to gather community-building information usually relies on manual collection, which is typically labor-intensive and time-consuming. To address these issues, we utilize the massive data generated from e-commerce delivery services and design a framework, AutoBuild, for fine-grained large-scale community-building labeling. AutoBuild consists of two main components: (i) a Location Candidate Detection Module that identifies potential building names and coordinates from multi-source delivery data, and (ii) a Progressive Building Matching Model that employs trajectory modeling, human behavior analysis, and heterogeneous graph alignment to match building names and coordinates. To evaluate the performance of AutoBuild, we applied it to two real-world multi-modal datasets from Beijing City and Chengdu City. The results reveal that AutoBuild significantly outperforms multiple baseline models by 50-meter accuracy of 81.8% and 100-meter accuracy of 95.9% in Beijing City. More importantly, we conduct a real-world case study to demonstrate the practical impact of AutoBuild in last-mile delivery.
Front Row: Automatically Generating Immersive Audio Representations of Tennis Broadcasts for Blind Viewers

Blind and low-vision (BLV) people face challenges watching sports due to the lack of accessibility of sports broadcasts. Currently, BLV people rely on descriptions from TV commentators, radio announcers, or their friends to understand the game. These descriptions, however, do not allow BLV viewers to visualize the action by themselves. We present Front Row, a system that automatically generates an immersive audio representation of sports broadcasts, specifically tennis, allowing BLV viewers to more directly perceive what is happening in the game. Front Row first recognizes gameplay from the video feed using computer vision, then renders players’ positions and shots via spatialized (3D) audio cues. User evaluations with 12 BLV participants show that Front Row gives BLV viewers a more accurate understanding of the game compared to TV and radio, enabling viewers to form their own opinions on players’ moods and strategies. We discuss future implications of Front Row and illustrate several applications, including a Front Row plug-in for video streaming platforms to enable BLV people to visualize the action in sports videos across the Web.
Computational Modeling of Hyperpolarizing Astrocytic Influence on Cortical Up-Down State Transitions

The Up-Down dynamics seen in cortical structures during non-rapid-eye-movement (NREM) sleep, anesthetized states, and quiet wakefulness is the spontaneous alternation between phases of heightened firing activity (referred to as the Up state) and periods of neuronal inactivity (termed the Down state) within neural networks. By leveraging bistable dynamics imposed by a depolarising astrocyte population, in the current paper, we introduced a hyperpolarising astrocyte population to an existing model of Up-Down dynamics to account for biological relevance. We created a computational rate model that includes populations of depolarizing and hyperpolarizing astrocytes and neurons. To optimize model parameters, we used the Elementary Effects (EE) test. It was followed by linear stability analysis to locate bistable regimes in the parameter hyperspace. The addition of hyperpolarizing gliotransmission perturbed model dynamics, indicating its sensitivity to qualitatively differing architectures. We then identified a bistable regime within the dynamics spectrum. According to the EE test, the strength of cell population coupling is a low-sensitivity parameter, possibly due to neuroplastic changes. We also found that the threshold of excitatory cell populations and the strength of adaptation are high-sensitivity parameters, whereas the threshold of inhibitory cell populations is low-sensitivity. Our model enables the possibility of testing biologically relevant theories of hyperpolarizing gliotransmission, where data remains scant.