Miracle-dz/UniFAR-FTUs huggingface dataset:
TEI2GO: A Multilingual Approach for Fast Temporal Expression Identification

Temporal expression identification is crucial for understanding texts written in natural language. Although highly effective systems such as HeidelTime exist, their limited runtime performance hampers adoption in large-scale applications and production environments. In this paper, we introduce the TEI2GO models, matching HeidelTime's effectiveness but with significantly improved runtime, supporting six languages, and achieving state-of-the-art results in four of them. To train the TEI2GO models, we used a combination of manually annotated reference corpus and developed "Professor HeidelTime'', a comprehensive weakly labeled corpus of news texts annotated with HeidelTime. This corpus comprises a total of 138,069 documents (over six languages) with 1,050,921 temporal expressions, the largest open-source annotated dataset for temporal expression identification to date. By describing how the models were produced, we aim to encourage the research community to further explore, refine, and extend the set of models to additional languages and domains. Code, annotations, and models are openly available for community exploration and use. The models are conveniently on HuggingFace for seamless integration and application.
Interplay between Spatial Ability and Visual Complexity in the Aesthetic Appreciation of Landscape Paintings

Starting with Birkhoff's (1933) proposal of monotonic inverse relationship between aesthetic preference and the complexity, extensive research has been conducted to modify and expand this equation by considering other factors to account for the large individual difference in aesthetic experiences. This study revisits this issue and reveals that Chinese ink paintings are appreciated equally regardless of complexity, while European oil paintings are generally perceived as more appealing when they have greater image complexity. Additionally, not only extrinsic factors of the artwork itself, but also viewers’ intrinsic spatial information processing capacity play crucial roles in accounting for variations in aesthetic bias toward complex visual elements when viewing landscape paintings. We propose that the fundamental spatial information processing ability may exert implicit yet significant influences on human aesthetic appreciation of landscape artworks, which are independent of semantic and cultural factors.
Pseudo Triplet Networks for Classification Tasks with Cross-Source Feature Incompleteness

Cross-source feature incompleteness -- a scenario where certain features are only available in one data source but missing in another -- is a common and significant challenge in machine learning. It typically arises in situations where the training data and testing data are collected from different sources with distinct feature sets. Addressing this challenge has the potential to greatly improve the utility of valuable datasets that might otherwise be considered incomplete and enhance model performance. This paper introduces the novel Pseudo Triplet Network (PTN) to address cross-source feature incompleteness. PTN fuses two Siamese network architectures -- Triplet Networks and Pseudo Networks. By segregating data into instance, positive, and negative subsets, PTN facilitates effectively contrastive learning through a hybrid loss function design. The model was rigorously evaluated on six benchmark datasets from the UCI Repository, in comparison with five other methods for managing missing data, under a range of feature overlap and missing data scenarios. The PTN consistently exhibited superior performance, displaying resilience in high missing ratio situations and maintaining robust stability across various data scenarios.
Joie: a Joy-based Brain-Computer Interface (BCI)

The size and cost of electroencephalography (EEG) headsets have been decreasing at a steadfast pace. Prefrontal cortical activity is a promising input source that is also important for affect regulation. We created Joie, a joy-based EEG brain-computer interface (BCI) which uses prefrontal asymmetries associated with joyful thoughts as input to an endless runner game where the user’s character collects coins in response. In a lab study (20 participants, 15 training sessions per participant, up to two weeks of training), we found that our experiment group instructed to imagine positive music, winning awards, and similar strategies, demonstrated significantly greater ability in activating asymmetry compared to our placebo and control groups. In our analysis, Joie demonstrates the ability for prefrontal asymmetries to be used as input to an affective BCI and builds upon prior work in this area. Training these asymmetries can teach mental strategies that have applications in mental health.
Skinergy: Machine-Embroidered Silicone-Textile Composites as On-Skin Self-Powered Input Sensors

We propose Skinergy for self-powered on-skin input sensing, a step towards prolonged on-skin device usages. In contrast to prior on-skin gesture interaction sensors, Skinergy’s sensor operation does not require external power. Enabled by the triboelectric nanogenerator (TENG) phenomenon, the machine-embroidered silicone-textile composite sensor converts mechanical energy from the input interaction into electrical energy. Our proof-of-concept untethered sensing system measures the voltages of generated electrical signals which are then processed for a diverse set of sensing tasks: discrete touch detection, multi-contact detection, contact localization, and gesture recognition. Skinergy is fabricated with off-the-shelf materials. The aesthetic and functional designs can be easily customized and digitally fabricated. We characterize Skinergy and conduct a 10-participant user study to (1) evaluate its gesture recognition performance and (2) probe user perceptions and potential applications. Skinergy achieves 92.8% accuracy for a 11-class gesture recognition task. Our findings reveal that human factors (e.g., individual differences in skin properties, and aesthetic preferences) are key considerations in designing self-powered on-skin sensors for human inputs.
Geometric Graph Learning for Protein Mutation Effect Prediction

Proteins govern a wide range of biological systems. Evaluating the changes in protein properties upon protein mutation is a fundamental application of protein design, where modeling the 3D protein structure is a principal task for AI-driven computational approaches. Existing deep learning (DL) approaches represent the protein structure as a 3D geometric graph and simplify the graph modeling to different degrees, thereby failing to capture the low-level atom patterns and high-level amino acid patterns simultaneously. In addition, limited training samples with ground truth labels and protein structures further restrict the effectiveness of DL approaches. In this paper, we propose a new graph learning framework, Hierarchical Graph Invariant Network (HGIN), a fine-grained and data-efficient graph neural encoder for encoding protein structures and predicting the mutation effect on protein properties. For fine-grained modeling, HGIN hierarchically models the low-level interactions of atoms and the high-level interactions of amino acid residues by Graph Neural Networks. For data efficiency, HGIN preserves the invariant encoding for atom permutation and coordinate transformation, which is an intrinsic inductive bias of property prediction that bypasses data augmentations. We integrate HGIN into a Siamese network to predict the quantitative effect on protein properties upon mutations. Our approach outperforms 9 state-of-the-art approaches on 3 protein datasets. More inspiringly, when predicting the neutralizing ability of human antibodies against COVID-19 mutant viruses, HGIN achieves an absolute improvement of 0.23 regarding the Spearman coefficient.
Personalized Differentially Private Federated Learning without Exposing Privacy Budgets

The meteoric rise of cross-silo Federated Learning (FL) is due to its ability to mitigate data breaches during collaborative training. To further provide rigorous privacy protection with consideration of the varying privacy requirements across different clients, a privacy-enhanced line of work on personalized differentially private federated learning (PDP-FL) has been proposed. However, the existing solution for PDP-FL [20] assumes the raw privacy budgets of all clients should be collected by the server. These values are then directly utilized to improve the model utility via facilitating the privacy preferences partitioning (i.e., partitioning all clients into multiple privacy groups). It is however non-realistic because the raw privacy budgets can be quite informative and sensitive. In this work, our goal is to achieve PDP-FL without exposing clients' raw privacy budgets by indirectly partitioning the privacy preferences solely based on clients' noisy model updates. The crux lies in the fact that the noisy updates could be influenced by two entangled factors of DP noises and non-IID clients' data, leaving it unknown whether it is possible to uncover privacy preferences by disentangling the two affecting factors. To overcome the hurdle, we systematically investigate the unexplored question of under what conditions can the model updates of clients be primarily influenced by noise levels rather than data distribution. Then, we propose a simple yet effective strategy based on clustering the L2 norm of the noisy updates, which can be integrated into the vanilla PDP-FL to maintain the same performance. Experimental results demonstrate the effectiveness and feasibility of our privacy-budget-agnostic PDP-FL method.
CallMine: Fraud Detection and Visualization of Million-Scale Call Graphs

Given a million-scale dataset of who-calls-whom data containing imperfect labels, how can we detect existing and new fraud patterns? We propose CallMine, with carefully designed features and visualizations. Our CallMine method has the following properties: (a) Scalable, being linear on the input size, handling about 35 million records in around one hour on a stock laptop; (b) Effective, allowing natural interaction with human analysts; (c) Flexible, being applicable in both supervised and unsupervised settings; (d) Automatic, requiring no user-defined parameters. In the real world, in a multi-million-scale dataset, CallMine was able to detect fraudsters 7,000x faster, namely in a matter of hours, while expert humans took over 10 months to detect them. CIKM-ARP Categories: Application; Analytics and machine learning; Data presentation.
The effect of synoptic scale weather systems on sub‐surface soil temperatures in a diurnal frost environment: preliminary observations from sub‐antarctic marion island

Abstract. Marion Island in the South Indian Ocean has a maritime climate dominated by diurnal frost processes in the landscape. We test the hypothesis that synoptic time‐scale measurements are essential in understanding the drivers of diurnal frost processes. Preliminary results from automated microclimate measurements in a polar desert habitat show that diurnal soil surface temperatures on Marion Island are influenced by a complex interaction of radiation balance, air mass circulation, cloud cover and snow. The passage of synoptic scale weather systems influences soil thermal characteristics through changes in dominance of the radiation budget. Soil frost on Marion appears to be dependent on clear skies, while synoptic weather systems affect the duration and intensity of soil frost processes and non‐radiative heat fluxes. Air circulation patterns at Marion Island influence diurnal scale temperature fluctuations and its direct and indirect interactions with ecosystem processes. The data suggest that in a maritime sub‐Antarctic environment the climatic drivers of soil frost occur at a finertemporal resolution than for seasonal and permafrost environments and needs to be measured at a diurnal time‐scale to be meaningful.
Automatic and Precise Data Validation for Machine Learning

Machine learning (ML) models in production pipelines are frequently retrained on the latest partitions of large, continually- growing datasets. Due to engineering bugs, partitions in such datasets almost always have some corrupted features; thus, it's critical to find data issues and block retraining before downstream ML accuracy decreases. However, current ML data validation methods are difficult to operationalize: they yield too many false positive alerts, require manual tuning, or are infeasible at scale. In this pa- per, we present an automatic, precise, and scalable data validation system for ML pipelines, employing a simple idea that we call a Partition Summarization (PS) approach to data validation: each timestamp-based partition of data is summarized with data quality metrics, and summaries are compared to detect corrupted partitions. We demonstrate how to adapt PS for any data validation method in a robust manner and evaluate several adaptations-which by themselves provide limited precision. Finally, we present gate, our data validation method that leverages these adaptations, giving a 2.1× average improvement in precision over the baseline from prior work on a case study within our large tech company.
Homogeneous Cohort-Aware Group Cognitive Diagnosis: A Multi-grained Modeling Perspective

Cognitive Diagnosis has been widely investigated as a fundamental task in the field of education, aiming at effectively assessing the students' knowledge proficiency level by mining their exercise records. Recently, group-level cognitive diagnosis is also attracting attention, which measures the group-level knowledge proficiency on specific concepts by modeling the response behaviors of all students within the classes. However, existing work tends to explore group characteristics with a coarse-grained perspective while ignoring the inter-individual variability within groups, which is prone to unstable diagnosis results. To this end, in this paper, we propose a novel Homogeneous cohort-aware Group Cognitive Diagnosis model, namely HomoGCD, to effectively model the group's knowledge proficiency level from a multi-grained modeling perspective. Specifically, we first design a homogeneous cohort mining module to explore subgroups of students with similar ability status within a class by modeling their routine exercising performance. Then, we construct the mined cohorts into fine-grained organizations for exploring stable and uniformly distributed features of groups. Subsequently, we develop a multi-grained modeling module to comprehensively learn the cohort and group ability status, which jointly trains both interactions with the exercises. In particular, an extensible diagnosis module is introduced to support the incorporation of different diagnosis functions. Finally, extensive experiments on two real-world datasets clearly demonstrate the generality and effectiveness of our HomoGCD in group as well as cohort~assessments.
STAR: Smartphone-analogous Typing in Augmented Reality

While text entry is an essential and frequent task in Augmented Reality (AR) applications, devising an efficient and easy-to-use text entry method for AR remains an open challenge. This research presents STAR, a smartphone-analogous AR text entry technique that leverages a user’s familiarity with smartphone two-thumb typing. With STAR, a user performs thumb typing on a virtual QWERTY keyboard that is overlain on the skin of their hands. During an evaluation study of STAR, participants achieved a mean typing speed of 21.9 WPM (i.e., 56% of their smartphone typing speed), and a mean error rate of 0.3% after 30 minutes of practice. We further analyze the major factors implicated in the performance gap between STAR and smartphone typing, and discuss ways this gap could be narrowed.
ShadowTouch: Enabling Free-Form Touch-Based Hand-to-Surface Interaction with Wrist-Mounted Illuminant by Shadow Projection

We present ShadowTouch, a novel sensing method to recognize the subtle hand-to-surface touch state for independent fingers based on optical auxiliary. ShadowTouch mounts a forward-facing light source on the user’s wrist to construct shadows on the surface in front of the fingers when the corresponding fingers are close to the surface. With such an optical design, the subtle vertical movements of near-surface fingers are magnified and turned to shadow features cast on the surface, which are recognizable for computer vision algorithms. To efficiently recognize the touch state of each finger, we devised a two-stage CNN-based algorithm that first extracted all the fingertip regions from each frame and then classified the touch state of each region from the cropped consecutive frames. Evaluations showed our touch state detection algorithm achieved a recognition accuracy of 99.1% and an F-1 score of 96.8% in the leave-one-out cross-user evaluation setting. We further outlined the hand-to-surface interaction space enabled by ShadowTouch’s sensing capability from the aspects of touch-based interaction, stroke-based interaction, and out-of-surface information and developed four application prototypes to showcase ShadowTouch’s interaction potential. The usability evaluation study showed the advantages of ShadowTouch over threshold-based techniques in aspects of lower mental demand, lower effort, lower frustration, more willing to use, easier to use, better integrity, and higher confidence.
Parametric Haptics: Versatile Geometry-based Tactile Feedback Devices

Haptic feedback is important for immersive, assistive, or multimodal interfaces, but engineering devices that generalize across applications is notoriously difficult. To address the issue of versatility, we propose Parametric Haptics, geometry-based tactile feedback devices that are customizable to render a variety of tactile sensations. To achieve this, we integrate the actuation mechanism with the tactor geometry into passive 3D printable patches, which are then connected to a generic wearable actuation interface consisting of micro gear motors. The key benefit of our approach is that the 3D-printed patches are modular, can consist of varying numbers and shapes of tactors, and that the tactors can be grouped and moved by our actuation geometry over large areas of the skin. The patches are soft, thin, conformable, and easy to customize to different use cases, thus potentially enabling a large design space of diverse tactile sensations. In our user study, we investigate the mapping between geometry parameters of our haptic patches and users’ tactile perceptions. Results indicate a good agreement between our parameters and the reported sensations, showing initial evidence that our haptic patches can produce a wide range of sensations for diverse use scenarios. We demonstrate the utility of our approach with wearable prototypes in immersive Virtual Reality (VR) scenarios, embedded into wearable objects such as glasses, and as wearable navigation and notification interfaces. We support designing such patches with a design tool in Rhino.
NP-SSL: A Modular and Extensible Self-supervised Learning Library with Neural Processes

Neural Processes (NPs) are a family of supervised density estimators devoted to probabilistic function approximation with meta-learning. Despite extensive research on the subject, the absence of a unified framework for NPs leads to varied architectural solutions across diverse studies. This non-consensus poses challenges to reproducing and benchmarking different NPs. Moreover, existing codebases mainly prioritize generative density estimation, yet rarely consider expanding the capability of NPs to self-supervised representation learning, which however has gained growing importance in data mining applications. To this end, we present NP-SSL, a modular and configurable framework with built-in support, requiring minimal effort to 1) implement classical NPs architectures; 2) customize specific components; 3) integrate hybrid training scheme (e.g., contrastive); and 4) extend NPs to act as a self-supervised learning toolkit, producing latent representations of data, and facilitating diverse downstream predictive tasks. To illustrate, we discuss a case study that applies NP-SSL to model time-series data. We interpret that NP-SSL can handle different predictive tasks such as imputation and forecasting, by a simple switch in data samplings, without significant change to the underlying structure. We hope this study can reduce the workload of future research on leveraging NPs to tackle more a broader range of real-world data mining applications. Code and documentation are at https://github.com/zyecs/NP-SSL.
pADR: Towards Personalized Adverse Drug Reaction Prediction by Modeling Multi-sourced Data

Predicting adverse drug reactions (ADRs) of drugs is one of the most critical steps in drug development. By pre-estimating the adverse reactions, researchers and drug development companies can greatly prevent the potential ADR risks and tragedies. However, the current ADR prediction methods suffer from several limitations. First, the prediction results are based on pure drug-related information, which makes them impossible to be directly applied for the personalized ADR prediction task. The lack of personalization of models also makes rare adverse events hard to be predicted. Therefore, it is of great interest to develop a new personalized ADR prediction method by introducing additional sources, e.g., patient health records. However, few methods have tried to use additional sources. In the meantime, the variety of different source formats and structures makes this task more challenging. To address the above challenges, we propose a novel personalized multi-sourced-based drug adverse reaction prediction model named pADR. pADR first works on every single source to transform them into proper representations. Next, a hierarchical multi-sourced Transformer is designed to automatically model the interactions between different sources and fuse them together for the final adverse event prediction. Experimental results on a new multi-sourced ADR prediction dataset show that pADR1 outperforms state-of-the-art drug-based baselines. Moreover, the case and ablation studies also illustrate the effectiveness of our proposed fusion strategies and the reasonableness of each module design.
UniTE: A Unified Treatment Effect Estimation Method for One-sided and Two-sided Marketing

Many internet platforms are two-sided markets that involve two types of participants. Examples include e-commerce platforms like Taobao (retailers and consumers) and ride-hailing platforms like Uber (drivers and passengers). Participants of different types in the two-sided market have relationships (i.e., supply and demand) that provide externalities and network benefits. On two-sided platforms, marketing campaigns are designed by subsidizing supply or demand. Uplift models built in this scenario usually consider the treatment assignment for only one of the two sides. However, ignoring the interaction of treatments between two sides or treating them as noises may result in incomplete models and inaccurate predictions. As far as we know, there is not much work related to modeling the combinational treatment effects in the two-sided market. In this paper, we first introduce the two-sided treatment effects estimation problem and then propose a Unified Treatment effect Estimation (UniTE) method for one-sided and two-sided marketing. We extend the Robinson Decomposition to two-sided, in which the relationship of the three involved tasks, namely the outcome, the propensity, and the treatment effect, is theoretically derived. Based on the decomposition result, a multi-task-based neural network model is proposed to integrate the three tasks and learn the inter-task-related common information, which prompts the model to estimate the treatment effects better. We also propose a unified synthetic data generation method that adapts to one/two-sided situations to verify the treatment effects estimation performance. Extensive and comprehensive experimental results show that our method outperforms the other methods.