Miracle-dz/UniFAR-FTUs huggingface dataset:
Alphacore: Data Depth based Core Decomposition

Core decomposition in networks has proven useful for evaluating the importance of nodes and communities in a variety of application domains, ranging from biology to social networks and finance. However, existing core decomposition algorithms have limitations in simultaneously handling multiple node and edge attributes. We propose a novel unsupervised core decomposition method that can be easily applied to directed and weighted networks. Our algorithm, AlphaCore, allows us to systematically and mathematically rigorously combine multiple node properties by using the notion of data depth. In addition, it can be used as a mixture of centrality measure and core decomposition. Compared to existing approaches, AlphaCore avoids the need to specify numerous thresholds or coefficients and yields meaningful quantitative and qualitative insights into the network structural organization. We evaluate AlphaCore's performance with a focus on financial, blockchain-based token networks, the social network Reddit and a transportation network of international flight routes. We compare our results with existing core decomposition and centrality algorithms. Using ground truth about node importance, we show that AlphaCore yields the best precision and recall results among core decomposition methods using the same input features. An implementation is available at https://github.com/friedhelmvictor/alphacore.
Anomaly and Novelty Detection, Explanation, and Accommodation (ANDEA)

The detection of, explanation of, and accommodation to anomalies and novelties are active research areas in multiple communities, including data mining, machine learning, and computer vision. They are applied in various guises including anomaly detection, out-of-distribution example detection, adversarial example recognition and detection, curiosity-driven reinforcement learning, and open-set recognition and adaptation, all of which are of great interest to the SIGKDD community. The techniques developed have been applied in a wide range of domains including fraud detection and anti-money laundering in fintech, early disease detection, intrusion detection in large-scale computer networks and data centers, defending AI systems from adversarial attacks, and in improving the practicality of agents through overcoming the closed-world assumption. This workshop is focused on Anomaly and Novelty Detection, Explanation, and Accommodation (ANDEA). It will gather researchers and practitioners from data mining, machine learning, and computer vision communities and diverse knowledge background to promote the development of fundamental theories, effective algorithms, and novel applications of anomaly and novelty detection, characterization, and adaptation. All materials of keynote talks, panel discussion, and accepted papers of the workshop are made available at https://tinyurl.com/andea2021.
DisenQNet: Disentangled Representation Learning for Educational Questions

Learning informative representations for educational questions is a fundamental problem in online learning systems, which can promote many applications, e.g., difficulty estimation. Most solutions integrate all information of one question together following a supervised manner, where the representation results are unsatisfactory sometimes due to the following issues. First, they cannot ensure the presentation ability due to the scarcity of labeled data. Then, the label-dependent representation results have poor feasibility to be transferred. Moreover, aggregating all information into the unified may introduce some noises in applications since it cannot distinguish the diverse characteristics of questions. In this paper, we aim to learn the disentangled representations of questions. We propose a novel unsupervised model, namely DisenQNet, to divide one question into two parts, i.e., a concept representation that captures its explicit concept meaning and an individual representation that preserves its personal characteristics. We achieve this goal via mutual information estimation by proposing three self-supervised estimators in a large unlabeled question corpus. Then, we propose another enhanced model, DisenQNet+, that transfers the representation knowledge from unlabeled questions to labeled questions in specific applications by maximizing the mutual information between both. Extensive experiments on real-world datasets demonstrate that DisenQNet can generate effective and meaningful disentangled representations for questions, and furthermore, DisenQNet+ can improve the performance of different applications.
TMC 2021: 2021 International Workshop on Talent and Management Computing

In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to deal with the talent and management related tasks in a quantitative manner. Indeed, thanks to the era of big data, the availability of large-scale talent data provides unparalleled opportunities for business leaders to understand the rules of talent and management, which in turn deliver intelligence for effective decision making and management for their organizations. In the past few years, talent and management computing have increasingly attracted attentions from KDD communities, and a number of research/applied data science efforts have been devoted. To this end, the purpose of this workshop, i.e., the 2021 International Workshop on Talent and Management Computing, is to bring together researchers and practitioners to discuss both the critical problems faced by talent and management related domains, and potential data-driven solutions by leveraging state-of-the-art data mining technologies.
H2MN: Graph Similarity Learning with Hierarchical Hypergraph Matching Networks

Graph similarity learning, which measures the similarities between a pair of graph-structured objects, lies at the core of various machine learning tasks such as graph classification, similarity search, etc. In this paper, we devise a novel graph neural network based framework to address this challenging problem, motivated by its great success in graph representation learning. As the vast majority of existing graph neural network models mainly concentrate on learning effective node or graph level representations of a single graph, little effort has been made to jointly reason over a pair of graph-structured inputs for graph similarity learning. To this end, we propose Hierarchical Hypergraph Matching Networks (H2sup>MN) to calculate the similarities between graph pairs with arbitrary structure. Specifically, our proposed H2MN learns graph representation from the perspective of hypergraph, and takes each hyperedge as a subgraph to perform subgraph matching, which could capture the rich substructure similarities across the graph. To enable hierarchical graph representation and fast similarity computation, we further propose a hyperedge pooling operator to transform each graph into a coarse graph of reduced size. Then, a multi-perspective cross-graph matching layer is employed on the coarsened graph pairs to extract the inter-graph similarity. Comprehensive experiments on five public datasets empirically demonstrate that our proposed model can outperform state-of-the-art baselines with different gains for graph-graph classification and regression tasks.
Temporal Biased Streaming Submodular Optimization

Submodular optimization lies at the core of many data mining and machine learning applications such as data summarization and subset selection. For data streams where elements arrive one at a time, streaming submodular optimization (SSO) algorithms are desired. Existing SSO solutions are mainly designed for insertion-only streams where elements in the stream all participate in the analysis, or sliding-window streams where only the most recent data participates in the analysis. SSO for insertion-only streams does not sufficiently emphasize recent data. SSO for sliding-window streams abruptly forgets all past data. In this work, we propose a new SSO problem, i.e., temporal biased streaming submodular optimization (TBSSO), which embraces the special settings of all previous studies. TBSSO leverages a temporal bias function to force each element in the stream to participate in the analysis with a probability decreasing over time and hence elements in the stream are forgotten gradually. We design novel streaming algorithms to solve the TBSSO problem with provable approximation guarantees. Experiments show that our algorithm can find high quality solutions and improve the efficiency to about one order of magnitude faster than the baseline method.
Network-Wide Traffic States Imputation Using Self-interested Coalitional Learning

Accurate network-wide traffic state estimation is vital to many transportation operations and urban applications. However, existing methods often suffer from the scalability issue when performing real-time inference at the city-level, or not robust enough under limited data. Currently, GPS trajectory data from probe vehicles has become a popular data source for many transportation applications. GPS trajectory data has large coverage area, which is ideal for network-wide applications, but also has the disadvantage of being sparse and highly heterogeneous among different time and locations. In this study, we focus on developing a robust and interpretable network-wide traffic state imputation framework using partially observed traffic information. We introduce a new learning strategy, called self-interested coalitional learning (SCL), which forges cooperation between a main self-interested semi-supervised learning task and a discriminator as a critic to facilitate main task training while providing interpretability on the results. In our detailed model, we use a temporal graph convolutional variational autoencoder (TG-VAE) as the reconstructor, which models the complex spatio-temporal pattern in data and solves the main traffic state imputation task. A discriminator is introduced to output interpretable imputation confidence on the estimated results and also help to enhance the performance of the reconstructor. The framework is evaluated using a large GPS trajectory dataset from taxis in Jinan, China. Extensive experiments against the state-of-the-art baselines demonstrate the effectiveness and robustness of the proposed method for network-wide traffic state estimation.
A Hyper-surface Arrangement Model of Ranking Distributions

A distribution on the permutations over a fixed finite set is called a ranking distribution. Modelling ranking distributions is one of the major topics in preference learning as such distributions appear as the ranking data produced by many judges. In this paper, we propose a geometric model for ranking distributions. Our idea is to use hyper-surface arrangements in a metric space as the representation space, where each component cut out by hyper-surfaces corresponds to a total ordering, and its volume is proportional to the probability. In this setting, the union of components corresponds to a partial ordering and its probability is also estimated by the volume. Similarly, the probability of a partial ordering conditioned by another partial ordering is estimated by the ratio of volumes. We provide a simple iterative algorithm to fit our model to a given dataset. We show our model can represent the distribution of a real-world dataset faithfully and can be used for prediction and visualisation purposes.
MiLeTS'21: 7th KDD Workshop on Mining and Learning from Time Series

Time series data are ubiquitous. Rapid advances in diverse sensing technologies, ranging from remote sensors to wearables and social sensing, are generating a rapid growth in the size and complexity of time series archives. This has resulted in a fundamental shift away from parsimonious, infrequent measurement to nearly continuous monitoring and recording. This demands development of new tools and solutions. The goals of this workshop are to: (1) highlight the significant challenges that underpin learning and mining from time series data (e.g. irregular sampling, spatiotemporal structure, and uncertainty quantification), (2) discuss recent algorithmic, theoretical, statistical, or systems-based developments for tackling these problems, and (3) synergize the research activities and discuss both new and open problems in time series analysis and mining.
Data Science on Blockchains

Blockchain technology garners an ever-increasing interest of researchers in various domains that benefit from scalable cooperation among trust-less parties. As blockchains and their applications proliferate, so do the complexity and volume of data stored by Blockchains. Analyzing this data has emerged as an important research topic, already leading to methodological advancements in information sciences. In this tutorial, we offer a holistic view of applied Data Science on Blockchains. Starting with the core components of Blockchain, we will detail the state of art in Blockchain data analytics for graph, security, and finance domains. Our examples will answer questions, such as, how to parse, extract and clean the data stored in blockchains? , how to store and query Blockchain data? and what features we could compute from blockchains? We will share tutorial notes, collected meta-information, and further reading pointers on our tutorial website at https://blockchaintutorial.github. io/
DARING: Differentiable Causal Discovery with Residual Independence

Discovering causal structure among a set of variables is a crucial task in various scientific and industrial scenarios. Given finite i.i. d. samples from a joint distribution, causal discovery is a challenging combinatorial problem in nature. The recent development in functional causal models, especially the NOTEARS provides a differentiable optimization framework for causal discovery. They formulate the structure learning problem as a task of maximum likelihood estimation over observational data (i.e., variable reconstruction) with specified structural constraints such as acyclicity and sparsity. Despite its success in terms of scalability, we find that optimizing the objectives of these differentiable methods is not always consistent with the correctness of learned causal graph especially when the variables carry heterogeneous noises (i.e., different noise types and noise variances) in real data from wild environments. In this paper, we provide the justification that their proneness to erroneous structures is mainly caused by the over-reconstruction problem, i.e., the noises of variables are absorbed into the variable reconstruction process, leading to the dependency among variable reconstruction residuals, and thus raise structure identifiability problems according to FCM theories. To remedy this, we propose a novel differentiable method DARING by imposing explicit residual independence constraint in an adversarial way. Extensive experimental results on both simulation and real data show that our proposed method is insensitive to the heterogeneity of external noise, and thus can significantly improve the causal discovery performances.
Simple Yet Efficient Algorithms for Maximum Inner Product Search via Extreme Order Statistics

We present a novel dimensionality reduction method for the approximate maximum inner product search (MIPS), named CEOs, based on the theory of concomitants of extreme order statistics. Utilizing the asymptotic behavior of these concomitants, we show that a few projections associated with the extreme values of the query signature are enough to estimate inner products. This yields a sublinear approximate MIPS algorithm with search recall guarantee under a mild condition. The indexing space is exponential but optimal for the approximate MIPS on a unit sphere. To deal with the exponential space complexity, we present practical variants, including CEOs-TA and coCEOs, that use near-linear indexing space and time. CEOs-TA exploits the threshold algorithm (TA) and provides superior search recalls to LSH-based MIPS solvers. coCEOs is a new data and dimension co-reduction technique that outperforms CEOs-TA and other competitive methods. Empirically, they are simple to implement and achieve at least 100x speedup compared to the bruteforce search while returning top-10 MIPS with recall of at least 90% on many large-scale data sets.
A Unified Solution to Constrained Bidding in Online Display Advertising

In online display advertising, advertisers usually participate in real-time bidding to acquire ad impression opportunities. In most advertising platforms, a typical impression acquiring demand of advertisers is to maximize the sum value of winning impressions under budget and some key performance indicators constraints, (e.g. maximizing clicks with the constraints of budget and cost per click upper bound). The demand can be various in value type (e.g. ad exposure/click), constraint type (e.g. cost per unit value) and constraint number. Existing works usually focus on a specific demand or hardly achieve the optimum. In this paper, we formulate the demand as a constrained bidding problem, and deduce a unified optimal bidding function on behalf of an advertiser. The optimal bidding function facilitates an advertiser calculating bids for all impressions with only m parameters, where m is the constraint number. However, in real application, it is non-trivial to determine the parameters due to the non-stationary auction environment. We further propose a reinforcement learning (RL) method to dynamically adjust parameters to achieve the optimum, whose converging efficiency is significantly boosted by the recursive optimization property in our formulation. We name the formulation and the RL method, together, as Unified Solution to Constrained Bidding (USCB). USCB is verified to be effective on industrial datasets and is deployed in Alibaba display advertising platform.
Causal Inference and Machine Learning in Practice with EconML and CausalML: Industrial Use Cases at Microsoft, TripAdvisor, Uber

In recent years, both academic research and industry applications see an increased effort in using machine learning methods to measure granular causal effects and design optimal policies based on these causal estimates. Open source packages such as CausalML and EconML provide a unified interface for applied researchers and industry practitioners with a variety of machine learning methods for causal inference. The tutorial will cover the topics including conditional treatment effect estimators by meta-learners and tree-based algorithms, model validations and sensitivity analysis, optimization algorithms including policy leaner and cost optimization. In addition, the tutorial will demonstrate the production of these algorithms in industry use cases.
parkrun: a panacea for health and wellbeing?

As a nurse and a runner, I have always been passionate about the physical and mental health benefits of physical activity for everyone. In a previous life I was a competitive athlete, and admittedly despite an aging body I do not think that competitiveness will ever leave me, but in recent years as I have struggled with bouts of anxiety and depression, I have learned to appreciate just how important exercise is in maintaining mental wellbeing. With this insight I have been excitedly watching the growth of the ‘parkrun’ initiative – a free weekly 5km run/ walk which takes places in local parks, from its humble beginnings in a London park to the global phenomenon it has evolved into today; and the hugely positive impact it has had on the health and wellbeing of individuals and communities. parkrun was quite rightly paused last year to limit the spread of Covid-19; however, as the world begins to re-emerge from lockdown, the resumption of parkrun could have an important role to play in improving community health and facilitating social reconnection.
SIRPα sequesters SHP-2 to promote IL-4/13 signaling and alternative activation of macrophages

The Th2 cytokines IL-4 and IL-13 through activation of their shared receptor IL-4Rα direct macrophage alternative activation to promote immunosuppression and wound healing. However, the mechanisms that control macrophage responses to IL-4/13 are not fully understood. Apart from driving JAK-STAT and PI3K-Akt pathways to polarize macrophages toward the alternative phenotype, the activated IL-4/13 receptors recruit negative regulators SHP-1 and SHP-2, which dephosphorylate IL-4Rα and decrease its signaling. Here we report that SIRPα spatially restricts SHP-2 and, by such, promotes IL-4/13 signaling and macrophage alternative activation. SIRPα executes this regulation via its cytoplasmic ITIMs/ITSMs that undergo phosphorylation by IL-4/13-induced, Src kinase-activated Bruton’s tyrosine kinase (Btk), resulting in recruitment of SHP-2 and preclusion of SHP-2 from binding to and inhibiting IL-4/13 receptors. Despite that this regulation occurs independent of CD47, extracellular CD47 ligation of SIRPα facilitates its cytoplasmic phosphorylation and SHP-2 sequestration, leading to stronger IL-4/13 signaling and enhanced macrophage expression of IL-10, TGFβ, CD206, arginase-1, etc. Conversely, deficiency of SIRPα allows SHP-2 to freely bind to γC or IL-13Rα1 and through which dephosphorylate IL-4Rα, dampening its signaling. Consistent with these findings, impaired wound healing in Sirpα−/− mice under experimental colitis correlated with a deficit of immunosuppressive macrophages in the colon, a condition that was corrected by transfusion of ex vivo-produced SIRPαhigh alternatively activated macrophages.
A New Metric for Routing Optimization in Mobile Networks Based on The Entropy Concept

The modern paradigm of Mobile Computing is playing a vital role in everyday life, both in terms of comfort and security. Unfortunately, mobility causes undesired phenomena for wireless communications, so several actions need to be take into account, in order to overcome the possible degradations introduced by mobility effects. OSI layer 3 approaches have been widely investigated in literature regarding the optimization of the whole network performance, while in this article we focus our attention on the analysis of the entropy trend for a mobile environment. The effects of mobility on entropy are investigated, as well as a new approach for predicting entropy evolution over time. In a mobile scenario, so in the considered context, the mobility entropy is able to optimise some key aspects of wireless communications, such as nodes stability, channel failures, and routing costs. In order to state the effectiveness of our proposal, many simulation campaigns have been carried out, taking into account the movement of the real nodes, confirming our assumptions.
Design of Digital OTAs With Operation Down to 0.3 V and nW Power for Direct Harvesting

In this paper, passive-less fully-digital operational transconductance amplifiers (DIGOTA) for energy- and area-constrained systems are modeled and analyzed from a design viewpoint. The digital behavior of DIGOTAs is modeled as an equivalent small-signal differential-mode circuit with zero bias current, and a common-mode feedback loop operating as a self-oscillating threshold sampler. Such continuous-time equivalent circuits are used to derive an explicit model of the main performance parameters that are generally adopted to characterize OTAs. This provides an insight into circuit operation and allows to derive practical guidelines to achieve a given design target. Among the others, an explicit model is derived for the DC gain, the frequency response, the gain-bandwidth product, the input-referred noise, and the input offset voltage. The models are validated via direct comparison with multi-die measurement results in CMOS 180 nm. From an application viewpoint, the voltage (power) reduction down to 0.25 V (sub-nW) uniquely enable direct harvesting (e.g., with solar cells), suppressing any intermediate DC-DC conversion stage. This further enhances the area efficiency advantage of DIGOTA stemming from its fully-digital nature, making it well suited for cost-sensitive and purely-harvested systems.