Reviewerly/exHarmony huggingface dataset:
Rover: An Online Spark SQL Tuning Service via Generalized Transfer Learning

Distributed data analytic engines like Spark are common choices to process massive data in industry. However, the performance of Spark SQL highly depends on the choice of configurations, where the optimal ones vary with the executed workloads. Among various alternatives for Spark SQL tuning, Bayesian optimization (BO) is a popular framework that finds near-optimal configurations given sufficient budget, but it suffers from the re-optimization issue and is not practical in real production. When applying transfer learning to accelerate the tuning process, we notice two domain-specific challenges: 1) most previous work focus on transferring tuning history, while expert knowledge from Spark engineers is of great potential to improve the tuning performance but is not well studied so far; 2) history tasks should be carefully utilized, where using dissimilar ones lead to a deteriorated performance in production.
CLUR: Uncertainty Estimation for Few-Shot Text Classification with Contrastive Learning

Few-shot text classification has extensive application where the sample collection is expensive or complicated. When the penalty for classification errors is high, such as early threat event detection with scarce data, we expect to know "whether we should trust the classification results or reexamine them.'' This paper investigates the Uncertainty Estimation for Few-shot Text Classification (UEFTC), an unexplored research area. Given limited samples, a UEFTC model predicts an uncertainty score for a classification result, which is the likelihood that the classification result is false. However, many traditional uncertainty estimation models in text classification are unsuitable for implementing a UEFTC model. These models require numerous training samples, whereas the few-shot setting in UEFTC only provides a few or just one support sample for each class in an episode. We propose Contrastive Learning from Uncertainty Relations (CLUR) to address UEFTC. CLUR can be trained with only one support sample for each class with the help of pseudo uncertainty scores. Unlike previous works that manually set the pseudo uncertainty scores, CLUR self-adaptively learns them using our proposed uncertainty relations. Specifically, we explore four model structures in CLUR to investigate the performance of three common-used contrastive learning components in UEFTC and find that two of the components are effective. Experiment results prove that CLUR outperforms six baselines on four datasets, including an improvement of 4.52% AUPR on an RCV1 dataset in a 5-way 1-shot setting. Our code and data split for UEFTC are in https://github.com/he159ok/CLUR_UncertaintyEst_FewShot_TextCls.
AdaTT: Adaptive Task-to-Task Fusion Network for Multitask Learning in Recommendations

Multi-task learning (MTL) aims to enhance the performance and efficiency of machine learning models by simultaneously training them on multiple tasks. However, MTL research faces two challenges: 1) effectively modeling the relationships between tasks to enable knowledge sharing, and 2) jointly learning task-specific and shared knowledge. In this paper, we present a novel model called Adaptive Task-to-Task Fusion Network (AdaTT) to address both challenges. AdaTT is a deep fusion network built with task-specific and optional shared fusion units at multiple levels. By leveraging a residual mechanism and a gating mechanism for task-to-task fusion, these units adaptively learn both shared knowledge and task-specific knowledge. To evaluate AdaTT's performance, we conduct experiments on a public benchmark and an industrial recommendation dataset using various task groups. Results demonstrate AdaTT significantly outperforms existing state-of-the-art baselines. Furthermore, our end-to-end experiments reveal that the model exhibits better performance compared to alternatives.
Internal Logical Induction for Pixel-Symbolic Reinforcement Learning

Reinforcement Learning (RL) has experienced rapid advancements in recent years. The widely studied RL algorithms mainly focus on a single input form, such as pixel-based image input or symbolic vector input. These two forms have different characteristics and, in many scenarios, will appear together, while few RL algorithms have studied the problems with mixed input types. Specifically, in the scenario where both pixel and symbolic inputs are available, symbolic input usually offers abstract features with specific semantics, which is more conducive to the agent's focus. Conversely, pixel input provides more comprehensive information, enabling the agent to make well-informed decisions. Tailoring the processing approach based on the properties of these two input types can contribute to solving the problem more effectively. To tackle the above issue, we propose an Internal Logical Induction (ILI) framework that integrates deep RL and rule learning into one system. ILI utilizes the deep RL algorithm to process the pixel input and the rule learning algorithm to induce propositional logic knowledge from symbolic input. To efficiently combine these two mechanisms, we further adopt a reward shaping technique by treating valuable knowledge as intrinsic rewards for the RL procedure. Experimental results demonstrate that the ILI framework outperforms baseline approaches in RL problems with pixel-symbolic input, and its inductive knowledge exhibits transferability advantages when pixel input semantics change.
Learning Joint Relational Co-evolution in Spatial-Temporal Knowledge Graph for SMEs Supply Chain Prediction

To effectively explore the supply chain relationships among Small and Medium-sized Enterprises (SMEs), some remarkable progress in such a relation modeling problem, especially knowledge graph-based methods have been witnessed during these years. As a typical link prediction task, supply chain prediction can usually predict the unknown future relationship facts between SMEs by utilizing the historical semantic connections between entities in knowledge graphs (KGs). However, it is still a great challenge for existing models as seldom of them can consider both temporal dependency and cooperative correlation of the connectivity pattern along the timeline synergistically. Accordingly, we propose a novel framework to learn joint relational co-evolution in Spatial-Temporal Knowledge Graphs (STKG). Specifically, on the base of the constructed large-scale financial STKG, a multi-view relational sequences mining method is proposed to reveal the semantic information from ontological concepts. Furthermore, a relational co-evolution learning module is also developed to capture the regularity of evolving connectivity patterns from the spatial-temporal view. Meanwhile, a multiple random subspace representation learning layer is also designed to improve both compatibility and complementarity during knowledge aggregation. Experimental results on large-scale SMEs supply chain prediction tasks from four real-world industries in China have illustrated the effectiveness of the proposed model.
PrivateRec: Differentially Private Model Training and Online Serving for Federated News Recommendation

Federated recommendation can potentially alleviate the privacy concerns in collecting sensitive and personal data for training personalized recommendation systems. However, it suffers from a low recommendation quality when a local serving is inapplicable due to the local resource limitation and the data privacy of querying clients is required in online serving. Furthermore, a theoretically private solution in both the training and serving of federated recommendation is essential but still lacking. Naively applying differential privacy (DP) to the two stages in federated recommendation would fail to achieve a satisfactory trade-off between privacy and utility due to the high-dimensional characteristics of model gradients and hidden representations. In this work, we propose a federated news recommendation method for achieving better utility in model training and online serving under a DP guarantee. We first clarify the DP definition over behavior data for each round in the pipeline of federated recommendation systems. Next, we propose a privacy-preserving online serving mechanism under this definition based on the idea of decomposing user embeddings with public basic vectors and perturbing the lower-dimensional combination coefficients. We apply a random behavior padding mechanism to reduce the required noise intensity for better utility. Besides, we design a federated recommendation model training method, which can generate effective and public basic vectors for serving while providing DP for training participants. We avoid the dimension-dependent noise for large models via label permutation and differentially private attention modules. Experiments on real-world news recommendation datasets validate that our method achieves superior utility under a DP guarantee in both training and serving of federated news recommendations.
The 3rd Workshop on Graph Learning Benchmarks (GLB 2023)

Recent years have witnessed a surge of research interest in graph machine learning. However, the benchmark datasets available to the field are rather limited in both quantity and diversity, an issue particularly notable given the immense potential applications of graph learning. The lack of diverse benchmark datasets may have biased the development of graph machine learning techniques towards narrow directions. By crowdsourcing novel tasks and datasets, this workshop aims to increase the diversity of graph learning benchmarks, identify new demands of graph machine learning in general, and gain a better synergy of how concrete techniques perform on these benchmarks. Moreover, this workshop offers a platform for discussions of best practices in curating graph learning benchmarks and data-centric approaches for graph learning.
TrustGeo: Uncertainty-Aware Dynamic Graph Learning for Trustworthy IP Geolocation

The rising popularity of online social network services has attracted a lot of research focusing on mining various user patterns. Among them, accurate IP geolocation is essential for a plethora of location-aware applications. However, despite extensive research efforts and significant advances, the "accurate and reliable'' desideratum is yet to be achieved at a higher quality level. This work presents a graph neural network (GNN)-based model, called TrustGeo, for trustworthy street-level IP geolocation. A distinct and important aspect of TrustGeo is the incorporation of sources of uncertainty in the learning process. The results of our extensive experimental evaluations on three real-world datasets demonstrate the superiority of our framework in significantly improving the accuracy and trustworthiness of street-level IP geolocation. Our code and datasets are available at https://github.com/ICDM-UESTC/TrustGeo.
Exploiting Relation-aware Attribute Representation Learning in Knowledge Graph Embedding for Numerical Reasoning

Numerical reasoning is an essential task for supporting machine learning applications, such as recommendation and information retrieval. The reasoning task aims to compare two items and infer new facts (e.g., is taller than) by leveraging existing relational information and numerical attributes (e.g., the height of an entity) in knowledge graphs. However, most existing methods rely on leveraging attribute encoders or additional loss functions to predict numerical relations. Therefore, the prediction performance is often not robust in cases when attributes are sparsely observed. In this paper, we propose a Relation-AAware attribute representation learning-based Knowledge Graph Embedding method for numerical reasoning tasks, which we call RAKGE. RAKGE incorporates a newly proposed attribute representation learning mechanism, which can leverage the association between relations and their corresponding numerical attributes. In addition, we introduce a robust self-supervised learning method to generate unseen positive and negative examples, thereby making our approach more reliable when numerical attributes are sparsely available. In the evaluation of three real-world datasets, our proposed model outperformed state-of-the-art methods, achieving an improvement of up to 65.1% in Hits@1 and up to 52.6% in MRR compared to the best competitor. Our implementation code is available at https://github.com/learndatalab/RAKGE.
EXTRACT and REFINE: Finding a Support Subgraph Set for Graph Representation

Subgraph learning has received considerable attention in its capacity of interpreting important structural information for predictions. Existing subgraph learning usually exploits statistics on predefined structures e.g., node degrees, occurrence frequency, to extract subgraphs, or refine the contents via only capturing label-relevant information with node-level sampling. Given diverse subgraph patterns, and mutual independence with local correlations on graphs, current solutions on subgraph learning still have two limitations in extraction and refinement stages. 1) The universality of extracting substructure patterns across domains is still lacking, 2) node-level sampling in refinement will distort the original local topology and none explicit guidance eliminating redundant information contribute to inefficiency issue. In this paper, we propose a unified subgraph learning scheme, Poly-Pivot Graph Neural Network (P2GNN) where we designate the centric node of each subgraph as the pivot. In the extraction stage, we present a general subgraph extraction principle, i.e., Local; Asymmetry between the centric and affiliated nodes. To this end, we asymmetrically model the similarity between each pair of nodes with random walk and quantify mutual affiliations in Affinity Propagation architecture, to extract subgraph structures. In the refinement, we devise a subgraph-level exclusion regularization to squash the target-independent information by considering mutual relations across subgraphs, cooperatively preserving a support set of subgraphs and facilitating the refinement process for graph representation. Empirical experiments on diverse web and biological graphs reveal 1.1%~7.3% improvements against best baselines, and visualized case studies prove the universality and interpretability of our P2GNN.
STUDY ON EUTROPHICATION CHANGE AND THEIR CONSEQUENCES IN PALIASTOMI LAKE

Background: The article describes the study of chlorophyll concentration dynamics in Paliastom lake and its surrounding area. The research covers the period from 2015 to 2022. The study uses remote sensing satellite images obtained from Landsat 8, Sentinel-2, as a result of processing in the SNAP application. At the same time, in situ researches was performed. Aims: This study aims to research chlorophyll concentration in Paliastom lake and its adjacent area through remote sensing methods and in situ data. Methods: For our research and historical data collection, satellite remote sensing was applied, which generates data on spatial and temporal variations of suspended particles in estuaries and coastlines. In situ studies, Chlorophyll concentration and promoting chemical agents were measured, water temperature, pH, total hardness, alkalinity, acidity, and others. Total turbidity was determined, and qualitative reactions were performed to define the presence of phosphates and nitrates. Results: The results received through remote sensing, and in situ studies were compared. Paliastom lake and its adjacent area are prone to eutrophication processes. Natural phenomena and human industrial activity cause the process. Discussion: According to in situ studies and the data obtained from sensors, the water in Paliastom lake is prone to a weak alkaline reaction. The water of the lake can be considered as having medium turbidity. Phosphate and nitrate ion detection reactions performed with qualitative analysis protocols showed positive samples. The intensity of the obtained color indicated their high concentration. Conclusions: The annual evolution of Chl-a in Paliastom lake reaches the maximum value in the summer months. Compared to previous years, a decrease in chlorophyll concentration was observed during the lockdown period of 2020 due to the Covid Pandemic.
On the Assessment of Interactive Detection of Code Smells in Practice: A Controlled Experiment

[Context] Code smells are structures in a program that often indicate the presence of deeper maintainability problems. Code smells should be detected as soon as they are introduced, enabling refactoring actions with less effort and time. Non-Interactive Detection (NID) techniques traditionally support code smells detection, enabling developers to reveal smells in later software program versions. NID techniques do not support developers’ progressive interaction with smelly code, revealing smells in the entire source code upon an explicit developer request, which might lead to the accumulation of code smells and, consequently, the degradation of software quality. [Problem statement] Interactive Detection (ID) has emerged as a solution to overcome NID’s limitations. By revealing code smell as soon as they are introduced, developers can detect smell instances earlier, resulting in more effective refactoring actions and improved code quality. However, despite its promising potential, there is a lack of evidence regarding the ID impact on code smell detection and refactoring actions during coding analysis. [Goal] Our research focused on evaluating the effectiveness of an ID technique in code smell detection. Besides, we analyzed the aid of an ID technique in performing effective refactoring actions during coding analysis. [Method] To this end, we conducted a controlled experiment with 16 subjects that underwent tasks related to detecting code smells and judging refactoring actions. [Results] The experimental tasks revealed that using the ID technique led to an increase of 60% in recall and up to 13% in precision when detecting code smells. Additionally, developers have effectively identified about 55% more code smells instances using the ID technique. [Conclusion] Our study results revealed that using ID can improve the effectiveness of code smells detection, as developers can identify opportunities for refactoring actions earlier when compared to NID.
Movie101: A New Movie Understanding Benchmark

To help the visually impaired enjoy movies, automatic movie narrating systems are expected to narrate accurate, coherent, and role-aware plots when there are no speaking lines of actors.Existing works benchmark this challenge as a normal video captioning task via some simplifications, such as removing role names and evaluating narrations with ngram-based metrics, which makes it difficult for automatic systems to meet the needs of real application scenarios.To narrow this gap, we construct a large-scale Chinese movie benchmark, named Movie101.Closer to real scenarios, the Movie Clip Narrating (MCN) task in our benchmark asks models to generate role-aware narration paragraphs for complete movie clips where no actors are speaking.External knowledge, such as role information and movie genres, is also provided for better movie understanding.Besides, we propose a new metric called Movie Narration Score (MNScore) for movie narrating evaluation, which achieves the best correlation with human evaluation.Our benchmark also supports the Temporal Narration Grounding (TNG) task to investigate clip localization given text descriptions.For both two tasks, our proposed methods well leverage external knowledge and outperform carefully designed baselines.The dataset and codes are released at https://github.com/yuezih/Movie101.
Enhancing Cross-lingual Transfer via Phonemic Transcription Integration

Previous cross-lingual transfer methods are restricted to orthographic representation learning via textual scripts.This limitation hampers cross-lingual transfer and is biased towards languages sharing similar well-known scripts.To alleviate the gap between languages from different writing scripts, we propose PhoneXL, a framework incorporating phonemic transcriptions as an additional linguistic modality beyond the traditional orthographic transcriptions for cross-lingual transfer.Particularly, we propose unsupervised alignment objectives to capture (1) local one-to-one alignment between the two different modalities, (2) alignment via multi-modality contexts to leverage information from additional modalities, and (3) alignment via multilingual contexts where additional bilingual dictionaries are incorporated.We also release the first phonemic-orthographic alignment dataset on two token-level tasks (Named Entity Recognition and Part-of-Speech Tagging) among the understudied but interconnected Chinese-Japanese-Korean-Vietnamese (CJKV) languages.Our pilot study reveals phonemic transcription provides essential information beyond the orthography to enhance cross-lingual transfer and bridge the gap among CJKV languages, leading to consistent improvements on cross-lingual token-level tasks over orthographic-based multilingual PLMs. 1
From Alignment to Entailment: A Unified Textual Entailment Framework for Entity Alignment

Entity Alignment (EA) aims to find the equivalent entities between two Knowledge Graphs (KGs).Existing methods usually encode the triples of entities as embeddings and learn to align the embeddings, which prevents the direct interaction between the original information of the cross-KG entities.Moreover, they encode the relational triples and attribute triples of an entity in heterogeneous embedding spaces, which prevents them from helping each other.In this paper, we transform both triples into unified textual sequences, and model the EA task as a bi-directional textual entailment task between the sequences of cross-KG entities.Specifically, we feed the sequences of two entities simultaneously into a pre-trained language model (PLM) and propose two kinds of PLMbased entity aligners that model the entailment probability between sequences as the similarity between entities.Our approach captures the unified correlation pattern of two kinds of information between entities, and explicitly models the fine-grained interaction between original entity information.The experiments on five crosslingual EA datasets show that our approach outperforms the state-of-the-art EA methods and enables the mutual enhancement of the heterogeneous information.
USSA: A Unified Table Filling Scheme for Structured Sentiment Analysis

Most previous studies on Structured Sentiment Analysis (SSA) have cast it as a problem of bi-lexical dependency parsing, which cannot address issues of overlap and discontinuity simultaneously.In this paper, we propose a nichetargeting and effective solution.Our approach involves creating a novel bi-lexical dependency parsing graph, which is then converted to a unified 2D table-filling scheme, namely USSA.The proposed scheme resolves the kernel bottleneck of previous SSA methods by utilizing 13 different types of relations.In addition, to closely collaborate with the USSA scheme, we have developed a model that includes a proposed bi-axial attention module to effectively capture the correlations among relations in the rows and columns of the table.Extensive experimental results on benchmark datasets demonstrate the effectiveness and robustness of our proposed framework, outperforming state-ofthe-art methods consistently 1 .