Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating Topic Difficulty Using Normalized Discounted Cumulated Gain

Information retrieval evaluation has to consider the varying "difficulty" between topics. Topic difficulty is often defined in terms of the aggregated effectiveness of a set of retrieval systems to satisfy a respective information need. Current approaches to estimate topic difficulty come with drawbacks such as being incomparable across different experimental settings. We introduce a new approach to estimate topic difficulty, which is based on the ratio of systems that achieve an NDCG score that is better than a baseline formed as random ranking of the pool of judged documents. We modify the NDCG measure to explicitly reflect a system's divergence from this hypothetical random ranker. In this way we achieve relative comparability of topic difficulty scores across experimental settings as well as stability to outlier systems? features lacking in previous difficulty estimations. We reevaluate the TREC 2012 Web Track's ad hoc task to demonstrate the feasibility of our approach in practice.
Cooperative Multi-Agent Reinforcement Learning in Express System

Express systems are widely deployed in many major cities. One type of important tasks in the system is to pick up packages from customers in time. As pick-up requests come in real time and there are many couriers picking up packages, how to dispatch couriers to ensure the cooperation among them and to complete more pick-up tasks in a long time, is very important but challenging. In this paper, we propose a reinforcement learning based framework to learn courier dispatching policies. At first, we divide the city into independent regions, inner each of which a constant number of couriers pick up packages at the same time. Besides reducing problem complexity, city division has practical operation benefits. Afterwards, we focus on each region separately. For each region, we propose a Cooperative Multi-Agent Reinforcement Learning model, i.e. CMARL, to learn the optimal courier dispatching policy in it. CMARL tries to maximize the total number of completed pick-up tasks by all couriers in a long time. Our model achieves this target by combining two Markov Decision Processes, one to guarantee the cooperation among couriers, and the other one to ensure the long-term optimization. After obtaining the value functions of these two MDPs, a new value function is designed to trade off them, based on which we can infer the courier dispatching policy. Experiments based on real-world road network data and historical express data from Beijing are conducted, to confirm the superiority of our model compared with nine baselines.
Behavior-driven Student Performance Prediction with Tri-branch Convolutional Neural Network

Student performance prediction aims to leverage student-related information to predict their future academic outcomes, which may be beneficial to numerous educational applications, such as personalized teaching and academic early warning. In this paper, we seek to address the problem by analyzing students' daily studying and living behavior, which is comprehensively recorded via campus smart cards. Different from previous studies, we propose an end-to-end student performance prediction model, namely Tri-branch CNN, which is equipped with three types of convolutional filters, i.e., the row-wise convolution, column-wise convolution, and group-wise convolution, to effectively capture the duration, periodicity, and location-aware characteristic of student behavior, respectively. We also introduce the attention mechanism and cost-sensitive learning strategy to further improve the accuracy of our approach. Extensive experiments on a large-scale real-world dataset demonstrate the potential of our approach for student performance prediction.
MAEC: A Multimodal Aligned Earnings Conference Call Dataset for Financial Risk Prediction

In the area of natural language processing, various financial datasets have informed recent research and analysis including financial news, financial reports, social media, and audio data from earnings calls. We introduce a new, large-scale multi-modal, text-audio paired, earnings-call dataset named MAEC, based on S&P 1500 companies. We describe the main features of MAEC, how it was collected and assembled, paying particular attention to the text-audio alignment process used. We present the approach used in this work as providing a suitable framework for processing similar forms of data in the future. The resulting dataset is more than six times larger than those currently available to the research community and we discuss its potential in terms of current and future research challenges and opportunities. All resources of this work are available at https://github.com/Earnings-Call-Dataset/
Personalized Imputation on Wearable-Sensory Time Series via Knowledge Transfer

The analysis of wearable-sensory time series data (e.g., heart rate records) benefits many applications (e.g., activity recognition, disease diagnosis). However, sensor measurements usually contain missing values due to various factors (e.g., user behavior, lack of charging), which may degrade the performance of downstream analytical tasks (e.g., regression, prediction). Thus, time series imputation is desired, which is capable of making sensory time series complete. Existing time series imputation methods generally employ various deep neural network models (e.g., GRU and GAN) to fill missing values by leveraging temporal patterns extracted from the contextual observations. Despite their effectiveness, we argue that most existing models can only achieve sub-optimal imputation performance due to the fact that they are inherently limited in sharing only one single set of model parameters to perform imputation on all individuals. Relying on one set of parameters limits the expressiveness of the imputation model as such models are bound to fail in capturing various complex personal characteristics. Therefore, most existing models tend to achieve inferior imputation performance, especially when a long duration of missing values, i.e., a large gap, is observed in the time series data. To address the limitation, this work develops a new imputation framework--Personalized Wearable-Sensory Time Series Imputation framework (PTSI) to provide a fully personalized treatment for time series imputation via effective knowledge transfer. In particular, PTSI first leverages a meta-learning paradigm to learn a well-generalized initialization to facilitate the adaption process for each user. To make the time series imputation be reflective of an individual's unique characteristics, we further endow PTSI with the capability of learning personalized model parameters, which is achieved by designing a parameter initialization modulating component. Extensive experiments on real-world human heart rate datasets demonstrate that our PTSI framework outperforms various state-of-the-art methods by a large margin consistently.
Uncovering Semantic Bias in Neural Network Models Using a Knowledge Graph

While neural networks models have shown impressive performance in many NLP tasks, lack of interpretability is often seen as a disadvantage. Individual relevance scores assigned by post-hoc explanation methods are not sufficient to show deeper systematic preferences and potential biases of the model that apply consistently across examples. In this paper we apply rule mining using knowledge graphs in combination with neural network explanation methods to uncover such systematic preferences of trained neural models and capture them in the form of conjunctive rules. We test our approach in the context of text classification tasks and show that such rules are able to explain a substantial part of the model behaviour as well as indicate potential causes of misclassifications when the model is applied outside of the initial training context.
U-rank: Utility-oriented Learning to Rank with Implicit Feedback

Learning to rank with implicit feedback is one of the most important tasks in many real-world information systems where the objective is some specific utility, e.g., clicks and revenue. However, we point out that existing methods based on probabilistic ranking principle do not necessarily achieve the highest utility. To this end, we propose a novel ranking framework called U-rank that directly optimizes the expected utility of the ranking list. With a position-aware deep click-through rate prediction model, we address the attention bias considering both query-level and item-level features. Due to the item-specific attention bias modeling, the optimization for expected utility corresponds to a maximum weight matching on the item-position bipartite graph. We base the optimization of this objective in an efficient Lambdaloss framework, which is supported by both theoretical and empirical analysis. We conduct extensive experiments for both web search and recommender systems over three benchmark datasets and two proprietary datasets, where the performance gain of U-rank over state-of-the-arts is demonstrated. Moreover, our proposed U-rank has been deployed on a large-scale commercial recommender and a large improvement over the production baseline has been observed in an online A/B testing.
Multi-task Adversarial Spatial-Temporal Networks for Crowd Flow Prediction

Crowd flow prediction, which aims to predict the in-out flows (e.g. the traffic of crowds, taxis and bikes ) of different areas of a city, is critically important to many real applications including public safety and intelligent transportation systems. The challenges of this problem lie in both the dynamic mobility patterns of crowds and the complex spatial-temporal correlations. Meanwhile, crowd flow is highly correlated to and affected by the Origin-Destination (OD) locations of the flow trajectories, which is largely ignored by existing works. In this paper, we study the novel problem of predicting the crowd flow and flow OD simultaneously, and propose a multi-task adversarial spatial-temporal network model entitled MT-ASTN to effectively address it. As a multi-task learning model, MT-ASTN adopts a shared-private framework which contains private spatial-temporal encoders, a shared spatial-temporal encoder, and decoders to learn the task-specific features and shared features. To effectively extract high quality shared features, a discriminative loss on task classification and an adversarial loss on shared feature extraction are incorporated to reduce information redundancy. We also design an attentive temporal queue to automatically capture the complex temporal dependency without the help of domain knowledge. Extensive evaluations are conducted over the bike and taxicab trip datasets in New York. The results demonstrate that our approach significantly outperforms state-of-the-art methods by a large margin on both tasks.
Structural Relationship Representation Learning with Graph Embedding for Personalized Product Search

To provide more accurate personalized product search (PPS) results, it is compulsory to go beyond modeling user-query-item interaction. Graph embedding techniques open the potential to integrate node information and topological structure information. Existing graph embedding enhanced PPS methods are mostly based on entity-relation-entity graph learning. In this work, we propose to consider structural relationship in users' product search scenario with graph embedding by latent representation learning. We argue that explicitly modeling the structural relationship in graph embedding is essential for more accurate PPS results. We propose a novel method, Graph embedding based Structural Relationship Representation Learning (GraphSRRL), which explicitly models the structural relationship in users-queries-products interaction. It combines three key conjunctive graph patterns to learn graph embedding for better PPS. In addition, GraphSRRL facilitates the learning of affinities between users (resp. queries or products) in the designed geometric operation in low-dimensional latent space. We conduct extensive experiments on four datasets to evaluate GraphSRRL for PPS. Experimental results show that GraphSRRL outperforms the state-of-the-art algorithm on real-world search datasets by at least 50.7% in term of Hit@10 and 48.7% in terms of NDCG@10.
Minimal Edit-Based Diffs for Large Trees

Hierarchically structured data are commonly represented as trees and have given rise to popular data formats like XML or JSON. An interesting query computes the difference between two versions of a tree, expressed as the minimum set of node edits (deletion, insertion, label rename) that transform one tree into another, commonly known as the tree edit distance. Unfortunately, the fastest tree edit distance algorithms run in cubic time and quadratic space and are therefore not feasible for large inputs. In this paper, we leverage the fact that the difference between two versions of a tree is typically much smaller than the overall tree size. We propose a new tree edit distance algorithm that is linear in the tree size for similar trees. Our algorithm is based on the new concept of top node pairs and avoids redundant distance computations, the main issue with previous solutions for tree diffs. We empirically evaluate the runtime of our algorithm on large synthetic and real-world trees; our algorithm clearly outperforms the state of the art, often by orders of magnitude.
When Structure Meets Keywords: Cohesive Attributed Community Search

As an online, query-dependent variant of the well-known community detection problem, community search has been studied for years to find communities containing the query vertices. Along with the generation of graphs with rich attribute information, attributed community search has attracted increasing interest recently, aiming to select communities where vertices are cohesively connected and share homogeneous attributes. However, existing community models may include cut-edges/vertices and thus cannot well guarantee the strong connectivity required by a cohesive community. In this paper, we propose a new cohesive attributed community (CAC) model that can ensure both structure cohesiveness and attribute cohesiveness of communities. Specifically, for a query with vertex vq and keyword set S, we aim to find the cohesively connected communities containing vq with the most shared keywords in S. It is nontrivial as we need to explore all possible subsets of S to verify the existence of structure cohesive communities until we find the communities with the most common keywords. To tackle this problem, we make efforts in two aspects. The first is to reduce the candidate keyword subsets. We achieve this by exploring the anti-monotonicity and neighborhood-constraint properties of our CAC model so that we can filter out the unpromising keyword subsets. The second is to speed up the verification process for each candidate keyword subset. We propose two indexes TIndex and MTIndex to reduce the size of the candidate subgraph before the verification. Moreover, we derive two new properties based on these indexes to reduce the candidate keyword subsets further. We conducted extensive experimental studies on four real-world graphs and validated the effectiveness and efficiency of our approaches.
The effect of addition sucrose concentrations toward weight of Nata DE Lontar (Borassus flabellifer) Linn

Making nata made from older Borrasus flabellifer wine (namely Nata de Lontar), it needs addition sucrose to produce nata with the optimum weight. The research aimed to know (1) The effect of addition sucrose with different concentrations on older palm wine toward the weight of “nata de lontar” (Borrasus flabellifer) (2) The concentration of sucrose is the most influential part to produce optimum weight of “nata de lontar”. This study belonged to an true experimental research with posttest only control group design. The independent variable in this research was the addition of sucrose with different concentrations of 6%, 9 %, and 12 %. The dependent variable in this study was the weight of nata. The results were analyzed using one-way anova and then followed by a test of Least Significant Difference at the 5 % significance level. The results of the experiment show (1) There have been a weight difference “nata de lontar” from bacterial fermentation in older palm wine Borassus flabellifer Linn medium with the addition of different concentrations of sucrose with p-value less than < 0.05. (2) Addition 12% sucrose concentration to the medium has influenced the formation of nata optimum results with average 46.13 grams.
DisenHAN: Disentangled Heterogeneous Graph Attention Network for Recommendation

Heterogeneous information network has been widely used to alleviate sparsity and cold start problems in recommender systems since it can model rich context information in user-item interactions. Graph neural network is able to encode this rich context information through propagation on the graph. However, existing heterogeneous graph neural networks neglect entanglement of the latent factors stemming from different aspects. Moreover, meta paths in existing approaches are simplified as connecting paths or side information between node pairs, overlooking the rich semantic information in the paths. In this paper, we propose a novel disentangled heterogeneous graph attention network DisenHAN for top-N recommendation, which learns disentangled user/item representations from different aspects in a heterogeneous information network. In particular, we use meta relations to decompose high-order connectivity between node pairs and propose a disentangled embedding propagation layer which can iteratively identify the major aspect of meta relations. Our model aggregates corresponding aspect features from each meta relation for the target user/item. With different layers of embedding propagation, DisenHAN is able to explicitly capture the collaborative filtering effect semantically. Extensive experiments on three real-world datasets show that DisenHAN consistently outperforms state-of-the-art approaches. We further demonstrate the effectiveness and interpretability of the learned disentangled representations via insightful case studies and visualization.
MindReader: Recommendation over Knowledge Graph Entities with Explicit User Ratings

Knowledge Graphs (KGs) have been integrated in several models of recommendation to augment the informational value of an item by means of its related entities in the graph. Yet, existing datasets only provide explicit ratings on items and no information is provided about users' opinions of other (non-recommendable) entities. To overcome this limitation, we introduce a new dataset, called the MindReader dataset, providing explicit user ratings both for items and for KG entities. In this first version, the MindReader dataset provides more than 102 thousands explicit ratings collected from 1,174 real users on both items and entities from a KG in the movie domain. This dataset has been collected through an online interview application that we also release as open source. As a demonstration of the importance of this new dataset, we present a comparative study of the effect of the inclusion of ratings on non-item KG entities in a variety of state-of-the-art recommendation models. In particular, we show that most models, whether designed specifically for graph data or not, see improvements in recommendation quality when trained on explicit non-item ratings. Moreover, for some models, we show that non-item ratings can effectively replace item ratings without loss of recommendation quality. This finding, in addition to an observed greater familiarity from users towards certain descriptive entities than movies, motivates the use of KG entities for both warm and cold-start recommendations.
Improvement of Vitamin C on septic brain injury in rats

Objective To preliminarily investigate the role of Vitamin C in cecal ligation and puncture-induced septic brain injury. Methods Male specific pathogen free (SPF) Sprague-Dawley male rats were randomly assigned into control group, sham operation group, sepsis group and sepsis therapy group.The rats in sepsis group were prepared by cecal ligation and puncture.The rats in sepsis therapy group were injected sodium ascorbate through the tail vein 3 h after the cecal ligation and punature procedure.The animals in other groups were subjected only to subcuta-neous bolus injection of 9 g/L saline only.Animals were evaluated by neurologic reflex scores before sacrifice and brain tissues were quickly removed at the indicated time points.Reactive oxygen species(ROS), superoxide dismutase(SOD), malondialdehyde(MDA), nitric oxide(NO), inducible nitric oxide synthase(iNOS) and catalase(CAT) were determined by using enzyme assay kits.Hematoxylin-eosin (HE) staining was used to observe morphological changes in brain tissues. Results The survival rate of the sepsis group (30% at 7th day) was significantly lower than that of the control group (100% at 7th day )and sham operation group(100% at 7th day). The survival rate of the sepsis therapy group (45% at 7th day)was significantly higher than that of the sepsis group(P<0.05). The neurological reflex assessment began to decrease at 6 h in sepsis group and reached the lowest at 24 h (6.00±0.53). The sepsis therapy group (7.62±0.52) was significantly higher (P<0.05) than the sepsis group and began to recover at 72 h(8.63±0.52). ROS, SOD, MDA, NO and iNOS in the sepsis group and the sepsis therapy group reached a peak at 24 h, which decreased at 72 h. The value in sepsis therapy group was significantly decreased than that in the sepsis group, and the difference was statistically significant(P<0.05). CAT changed in the opposition.The SOD/CAT in sepsis group was the highest 24 h after the operation, while the ratio in sepsis therapy group was significantly improved.SOD/CAT and MDA were positively correlated(r=0.968, P<0.05). HE staining showed significant damage to the brain tissue structure in the sepsis group, however some improvement was observed in the sepsis therapy group. Conclusion Vitamin C can significantly improve the survival rate and encephalopathy prognosis in the cecal ligation and puncture SD rat models.The mechanism may be related to the reduction of oxidative stress. Key words: Sepsis; Brain injury; Vitamin C; Oxidative stress
Computational Approaches for Drug Repositioning: Towards a Holistic Perspective based on Knowledge Graphs

Drug development is a costly and time consuming activity. The traditional process relies on extensive experimental efforts to map out the relevant part of the chemical space. Data about molecules, diseases, genes and other entities are present on many isolated databases, be that internal or external and in heterogeneous formats. They either require costly and inflexible data integration, or time-consuming workflows. Computational approaches, and more recently artificial intelligence based techniques, have emerged as a promising alternative for reducing the development cycle through drug repositioning. Knowledge bases are used to predict new links between old drugs and new targets. We present below the overall approach adopted for my PhD thesis, for a more holistic knowledge graph-based drug repositioning that aims to discover hidden or missing links between existing drugs and targets for which no known treatment is available. Currently, eight data and knowledge resources have already been integrated into the designed knowledge graph.