Reviewerly/exHarmony huggingface dataset:
An energy-efficient load balance strategy based on virtual machine consolidation in cloud environment

In the cloud computing environment, unbalanced utilization of multi-dimensional resources in physical servers generates resource fragmentation, leading to inefficient resource utilization and energy wastage in data centers. The inefficient utilization of resources makes high energy consumption and low quality of service (QoS) in resource management an urgent problem to be solved. We propose a load balancing strategy based on virtual machine consolidation (LBVMC), which aims to reduce the energy consumption and service level agreement (SLA) violation of data centers by balancing the multi-dimensional resource utilization in physical machines (PMs). First, we present a load state classification algorithm for PMs with load abnormality considering current and future loads to reduce unnecessary virtual machine (VM) migrations caused by occasional load fluctuations. Then, we propose a resource-weight based selection model for migratable VMs, which selects appropriate VMs to be migrated based on multi-dimensional resource utilization, and reduces resource fragmentation caused by load imbalance. Finally, we design a VM placement algorithm based on resource fitness and load correlation to deploy VMs on the optimal destination PMs to assure load balancing of the destination PMs after VM placement. We perform simulated experiments under homogeneous, heterogeneous, and bottleneck resource environments. Experimental results show that LBVMC is superior to other strategies in reducing energy consumption and SLA violations and achieves better overall performance.
A shallow slow slip event in 2018 in the Semidi segment of the Alaska subduction zone detected by machine learning

Slow slip events (SSEs) have been discovered at shallow depth near the trench in some subduction zones and have been linked to the triggering of large earthquakes and the absence of tsunami. These shallow SSEs are invariably submarine, making it difficult to observe their temporal and spatial extent. Here, we report a shallow SSE in late 2018 near the west Semidi segment of the Alaska subduction zone, up-dip of and preceding the Mw 8.2 Chignik earthquake on July 29th, 2021. The SSE was detected in data from an offshore array of seafloor pressure gauges by a machine learning method. This detection is supported by the spatial pattern of simulated SSE deformation, the increased seismicity after the SSE in the positive Coulomb stress change area, the lack of shallow slip in Chignik earthquake, and the absence of a sizable tsunami following the Chignik earthquake. Our method has the potential to transform the way offshore SSEs are detected and to improve tsunami hazard assessment in subduction zones.
Fairly Adaptive Negative Sampling for Recommendations

Pairwise learning strategies are prevalent for optimizing recommendation models on implicit feedback data, which usually learns user preference by discriminating between positive (i.e., clicked by a user) and negative items (i.e., obtained by negative sampling). However, the size of different item groups (specified by item attribute) is usually unevenly distributed. We empirically find that the commonly used uniform negative sampling strategy for pairwise algorithms (e.g., BPR) can inherit such data bias and oversample the majority item group as negative instances, severely countering group fairness on the item side. In this paper, we propose a Fairly adaptive Negative sampling approach (FairNeg), which improves item group fairness via adaptively adjusting the group-level negative sampling distribution in the training process. In particular, it first perceives the model’s unfairness status at each step and then adjusts the group-wise sampling distribution with an adaptive momentum update strategy for better facilitating fairness optimization. Moreover, a negative sampling distribution Mixup mechanism is proposed, which gracefully incorporates existing importance-aware sampling techniques intended for mining informative negative samples, thus allowing for achieving multiple optimization purposes. Extensive experiments on four public datasets show our proposed method’s superiority in group fairness enhancement and fairness-utility tradeoff.
A Dual Prompt Learning Framework for Few-Shot Dialogue State Tracking

Dialogue State Tracking (DST) module is an essential component of task-oriented dialog systems to understand users’ goals and needs. Collecting dialogue state labels including slots and values can be costly, requiring experts to annotate all (slot, value) information for each turn in dialogues. It is also difficult to define all possible slots and values in advance, especially with the wide application of dialogue systems in more and more new-rising applications. In this paper, we focus on improving DST module to generate dialogue states in circumstances with limited annotations and knowledge about slot ontology. To this end, we design a dual prompt learning framework for few-shot DST. The dual framework aims to explore how to utilize the language understanding and generation capabilities of pre-trained language models for DST efficiently. Specifically, we consider the learning of slot generation and value generation as dual tasks, and two kinds of prompts are designed based on this dual structure to incorporate task-related knowledge of these two tasks respectively. In this way, the DST task can be formulated as a language modeling task efficiently under few-shot settings. To evaluate the proposed framework, we conduct experiments on two task-oriented dialogue datasets. The results demonstrate that the proposed method not only outperforms existing state-of-the-art few-shot methods, but also can generate unseen slots. It indicates that DST-related knowledge can be probed from pre-trained language models and utilized to address low-resource DST efficiently with the help of prompt learning.
CurvDrop: A Ricci Curvature Based Approach to Prevent Graph Neural Networks from Over-Smoothing and Over-Squashing

Graph neural networks (GNNs) are powerful models to handle graph data and can achieve state-of-the-art in many critical tasks including node classification and link prediction. However, existing graph neural networks still face both challenges of over-smoothing and over-squashing based on previous literature. To this end, we propose a new Curvature-based topology-aware Dropout sampling technique named CurvDrop, in which we integrate the Discrete Ricci Curvature into graph neural networks to enable more expressive graph models. Also, this work can improve graph neural networks by quantifying connections in graphs and using structural information such as community structures in graphs. As a result, our method can tackle the both challenges of over-smoothing and over-squashing with theoretical justification. Also, numerous experiments on public datasets show the effectiveness and robustness of our proposed method. The code and data are released in https://github.com/liu-yang-maker/Curvature-based-Dropout.
Membership Inference Attacks Against Sequential Recommender Systems

Recent studies have demonstrated the vulnerability of recommender systems to membership inference attacks, which determine whether a user’s historical data was utilized for model training, posing serious privacy leakage issues. Existing works assumed that member and non-member users follow different recommendation modes, and then infer membership based on the difference vector between the user’s historical behaviors and the recommendation list. The previous frameworks are invalid against inductive recommendations, such as sequential recommendations, since the disparities of difference vectors constructed by the recommendations between members and non-members become imperceptible. This motivates us to dig deeper into the target model. In addition, most MIA frameworks assume that they can obtain some in-distribution data from the same distribution of the target data, which is hard to gain in recommender system.
Learning Denoised and Interpretable Session Representation for Conversational Search

Conversational search supports multi-turn user-system interactions to solve complex information needs. Compared with the traditional single-turn ad-hoc search, conversational search faces a more complex search intent understanding problem because a conversational search session is much longer and contains many noisy tokens. However, existing conversational dense retrieval solutions simply fine-tune the pre-trained ad-hoc query encoder on limited conversational search data, which are hard to achieve satisfactory performance in such a complex conversational search scenario. Meanwhile, the learned latent representation also lacks interpretability that people cannot perceive how the model understands the session. To tackle the above drawbacks, we propose a sparse Lexical-based Conversational REtriever (LeCoRE), which extends the SPLADE model with two well-matched multi-level denoising methods uniformly based on knowledge distillation and external query rewrites to generate denoised and interpretable lexical session representation. Extensive experiments on four public conversational search datasets in both normal and zero-shot evaluation settings demonstrate the strong performance of LeCoRE towards more effective and interpretable conversational search.
P-MMF: Provider Max-min Fairness Re-ranking in Recommender System

In this paper, we address the issue of recommending fairly from the aspect of providers, which has become increasingly essential in multistakeholder recommender systems. Existing studies on provider fairness usually focused on designing proportion fairness (PF) metrics that first consider systematic fairness. However, sociological researches show that to make the market more stable, max-min fairness (MMF) is a better metric. The main reason is that MMF aims to improve the utility of the worst ones preferentially, guiding the system to support the providers in weak market positions. When applying MMF to recommender systems, how to balance user preferences and provider fairness in an online recommendation scenario is still a challenging problem. In this paper, we proposed an online re-ranking model named Provider Max-min Fairness Re-ranking (P-MMF) to tackle the problem. Specifically, P-MMF formulates provider fair recommendation as a resource allocation problem, where the exposure slots are considered the resources to be allocated to providers and the max-min fairness is used as the regularizer during the process. We show that the problem can be further represented as a regularized online optimizing problem and solved efficiently in its dual space. During the online re-ranking phase, a momentum gradient descent method is designed to conduct the dynamic re-ranking. Theoretical analysis showed that the regret of P-MMF can be bounded. Experimental results on four public recommender datasets demonstrated that P-MMF can outperformed the state-of-the-art baselines. Experimental results also show that P-MMF can retain small computationally costs on a corpus with the large number of items.
Do NFTs’ Owners Really Possess their Assets? A First Look at the NFT-to-Asset Connection Fragility

Most NFTs (Non-Fungible Tokens) use multi-hop URLs to address the off-chain assets due to the costly on-chain storage, but the path from NFTs to the underlying assets is fraught with instability, which may degrade its value. Hence, this paper aims to answer the question: Is the NFT-to-Asset connection fragile? This paper makes a first step towards this end by characterizing NFT-to-Asset connections of 12,353 Ethereum NFT Contracts (6,234,141 NFTs in total) from three perspectives, storage, accessibility, and duplication. In order to overcome challenges of affecting the measurement accuracy, e.g., IPFS instability and the changing availability of both IPFS and servers’ data, we propose to leverage multiple gateways to enlarge the data coverage and extend a longer measurement period with non-trivial efforts. Results of our extensive study show that such connection is very fragile in practice. The loss, unavailability, or duplication of off-chain assets could render the value of NFTs worthless. For instance, we find that assets of 25.24% of Ethereum NFT contracts are not accessible, and 21.48% of Ethereum NFT contracts include duplicated assets. Our work sheds light on the fragility along the NFT-to-Asset connection, which could help the NFT community to better enhance the trust of off-chain assets.
TTS: A Target-based Teacher-Student Framework for Zero-Shot Stance Detection

The goal of zero-shot stance detection (ZSSD) is to identify the stance (in favor of, against, or neutral) of a text towards an unseen target in the inference stage. In this paper, we explore this problem from a novel angle by proposing a Target-based Teacher-Student learning (TTS) framework. Specifically, we first augment the training set by extracting diversified targets that are unseen during training with a keyphrase generation model. Then, we develop a teacher-student framework which effectively utilizes the augmented data. Extensive experiments show that our model significantly outperforms state-of-the-art ZSSD baselines on the available benchmark dataset for this task by 8.9% in macro-averaged F1. In addition, previous ZSSD requires human-annotated targets and labels during training, which may not be available in real-world applications. Therefore, we go one step further by proposing a more challenging open-world ZSSD task: identifying the stance of a text towards an unseen target without human-annotated targets and stance labels. We show that our TTS can be easily adapted to the new task. Remarkably, TTS without human-annotated targets and stance labels even significantly outperforms previous state-of-the-art ZSSD baselines trained with human-annotated data. We publicly release our code 1 to facilitate future research.
Knowledge Graph Question Answering with Ambiguous Query

Knowledge graph question answering aims to identify answers of the query according to the facts in the knowledge graph. In the vast majority of the existing works, the input queries are considered perfect and can precisely express the user’s query intention. However, in reality, input queries might be ambiguous and elusive which only contain a limited amount of information. Directly answering these ambiguous queries may yield unwanted answers and deteriorate user experience. In this paper, we propose PReFNet which focuses on answering ambiguous queries with pseudo relevance feedback on knowledge graphs. In order to leverage the hidden (pseudo) relevance information existed in the results that are initially returned from a given query, PReFNet treats the top-k returned candidate answers as a set of most relevant answers, and uses variational Bayesian inference to infer user’s query intention. To boost the quality of the inferred queries, a neighborhood embedding based VGAE model is used to prune inferior inferred queries. The inferred high quality queries will be returned to the users to help them search with ease. Moreover, all the high-quality candidate nodes will be re-ranked according to the inferred queries. The experiment results show that our proposed method can recommend high-quality query graphs to users and improve the question answering accuracy.
Sanitizing Sentence Embeddings (and Labels) for Local Differential Privacy

Differentially private (DP) learning, notably DP stochastic gradient descent (DP-SGD), has limited applicability in fine-tuning gigantic pre-trained language models (LMs) for natural language processing tasks. The culprit is the perturbation of gradients (as gigantic as entire models), leading to significant efficiency and accuracy drops.
CAMUS: Attribute-Aware Counterfactual Augmentation for Minority Users in Recommendation

Embedding-based methods currently achieved impressive success in recommender systems. However, such methods are more likely to suffer from bias in data distribution, especially the attribute bias problem. For example, when a certain type of user, like the elderly, occupies the mainstream, the recommendation results of minority users would be seriously affected by the mainstream users’ attributes. To address this problem, most existing methods are proposed from the perspective of fairness, which focuses on eliminating unfairness but deteriorates the recommendation performance. Unlike these methods, in this paper, we focus on improving the recommendation performance for minority users of biased attributes. Along this line, we propose a novel attribute-aware Counterfactual Augmentation framework for Minority Users(CAMUS). Specifically, the CAMUS consists of a counterfactual augmenter, a confidence estimator, and a recommender. The counterfactual augmenter conducts data augmentation for the minority group by utilizing the interactions of mainstream users based on a universal counterfactual assumption. Besides, a tri-training-based confidence estimator is applied to ensure the effectiveness of augmentation. Extensive experiments on three real-world datasets have demonstrated the superior performance of the proposed methods. Further case studies verify the universality of the proposed CAMUS framework on different data sparsity, attributes, and models.
Intra and Inter Domain HyperGraph Convolutional Network for Cross-Domain Recommendation

Cross-Domain Recommendation (CDR) aims to solve the data sparsity problem by integrating the strengths of different domains. Though researchers have proposed various CDR methods to effectively transfer knowledge across domains, they fail to address the following key issues, i.e., (1) they cannot model high-order correlations among users and items in every single domain to obtain more accurate representations; (2) they cannot model the correlations among items across different domains. To tackle the above issues, we propose a novel Intra and Inter Domain HyperGraph Convolutional Network (II-HGCN) framework, which includes two main layers in the modeling process, i.e., the intra-domain layer and the inter-domain layer. In the intra-domain layer, we design a user hypergraph and an item hypergraph to model high-order correlations inside every single domain. Thus we can address the data sparsity problem better and learn high-quality representations of users and items. In the inter-domain layer, we propose an inter-domain hypergraph structure to explore correlations among items from different domains based on their interactions with common users. Therefore we can not only transfer the knowledge of users but also combine embeddings of items across domains. Comprehensive experiments on three widely used benchmark datasets demonstrate that II-HGCN outperforms other state-of-the-art methods, especially when datasets are extremely sparse.
Hierarchy-Aware Multi-Hop Question Answering over Knowledge Graphs

Knowledge graphs (KGs) have been widely used to enhance complex question answering (QA). To understand complex questions, existing studies employ language models (LMs) to encode contexts. Despite the simplicity, they neglect the latent relational information among question concepts and answers in KGs. While question concepts ubiquitously present hyponymy at the semantic level, e.g., mammals and animals, this feature is identically reflected in the hierarchical relations in KGs, e.g., a_type_of. Therefore, we are motivated to explore comprehensive reasoning by the hierarchical structures in KGs to help understand questions. However, it is non-trivial to reason over tree-like structures compared with chained paths. Moreover, identifying appropriate hierarchies relies on expertise. To this end, we propose HamQA, a novel Hierarchy-aware multi-hop Question Answering framework on knowledge graphs, to effectively align the mutual hierarchical information between question contexts and KGs. The entire learning is conducted in Hyperbolic space, inspired by its advantages of embedding hierarchical structures. Specifically, (i) we design a context-aware graph attentive network to capture context information. (ii) Hierarchical structures are continuously preserved in KGs by minimizing the Hyperbolic geodesic distances. The comprehensive reasoning is conducted to jointly train both components and provide a top-ranked candidate as an optimal answer. We achieve a higher ranking than the state-of-the-art multi-hop baselines on the official OpenBookQA leaderboard with an accuracy of 85%.
Smartphone-based non-invasive haemoglobin level estimation by analyzing nail pallor

Blood haemoglobin concentration is a vital physiological parameter for assessing disease conditions, such as Anaemia in the human body. Anaemia is one of the most common diseases that affects billions of people worldwide. Especially, in developing countries, like India, the prevalence is quite high according to WHO statistics. Traditional invasive process for screening of anaemia is exorbitant and is hard to orchestrate in a resource-poor environment which raises the demand for cost-effective, reliable, portable, non-invasive solution. Hence, in this work, we aim to design a solution for non-invasive screening of anaemia by combining the cutting-edge Artificial-Intelligence techniques with the conventional practises for rough assessment of anaemia by observing pallor in the finger-nail of hand of a human subject. The proposed method first induces color changes in nail-bed by application of suitable amount of pressure and release thereafter through a customized hardware device. Finally, the captured video by a smartphone camera is analysed to measure the rate of change of color to quantify the blood haemoglobin level after correlating with the clinically determined value of blood haemoglobin. The proposed model with a fusion-based prediction approach outperforms the state-of-the-art solutions, ensuring a mean RMSE and MSE error of 0.63 and 0.61 and standard deviation ± 0.46 g dL−1 and ± 0.73 g dL−1 respectively as compared to clinically tested haemoglobin level for a total of 220 subjects.