Miracle-dz/UniFAR-FTUs huggingface dataset:
Research status and development trend of magnetic adsorption wall climbing robot

Magnetic adsorption wall climbing robots are extensively used for surface inspection, maintenance, and repair of ships, large chemical storage tanks, and steel structures, thereby avoiding high-risk work and lowering the occurrence of accidents. The research status of magnetic adsorption wall climbing robots at home and abroad is summarized using the three main adsorption methods of magnetic adsorption wall climbing robots: permanent magnetic adsorption, electromagnetic adsorption, and electric permanent magnetic adsorption, and the main development trend of future magnetic adsorption wall climbing robots is given using the magnetic adsorption methods and application scenarios to provide a reference basis.
Improving Programming Q&A with Neural Generative Augmentation

Knowledge-intensive programming Q&A is an active research area in industry. Its application boosts developer productivity by aiding developers in quickly finding programming answers from the vast amount of information on the Internet. In this study, we propose ProQANS and its variants ReProQANS and ReAugProQANS to tackle programming Q&A. ProQANS is a neural search approach that leverages unlabeled data on the Internet (such as StackOverflow) to mitigate the cold-start problem. ReProQANS extends ProQANS by utilizing reformulated queries with a novel triplet loss. We further use an auxiliary generative model to augment the training queries, and design a novel dual triplet loss function to adapt these generated queries, to build another variant of ReProQANS termed as ReAugProQANS. In our empirical experiments, we show ReProQANS has the best performance when evaluated on the in-domain test set, while ReAugProQANS has the superior performance on the out-of-domain real programming questions, by outperforming the state-of-the-art model by up to 477% lift on the MRR metric respectively. The results suggest their robustness to previously unseen questions and its wide application to real programming questions.
Unlocking Financial Success: Empowering Higher Ed Students and Developing Financial Literacy Interventions at Scale

Greater financial literacy is critically needed among young adults in the United States [10,35], but many financial literacy education courses have been less effective than hoped for by educators and researchers [7,15]. Additionally, many have not been designed around established curricula or learning science principles, rendering findings difficult for researchers to study empirically [6,34]. In order to better understand the psychosocial mechanisms that predict success in improving learner knowledge and behavior, online educational interventions at scale can be an effective path forward. We conducted interviews with subject matter experts and young adult students to explore the highest priority learning objectives for a brief course curriculum to improve the financial literacy of US young adults. We then leveraged our findings from this study and content from our open-source textbooks to develop the first of several brief online learning interventions for deployment on the large-scale OpenStax Kinetic research infrastructure [2]. In this work-in-progress paper, we discuss the next steps in our research agenda, including course content development and deploying this intervention, as well as our broader plans for our future financial literacy education interventions and translating research into practice with our institutional collaborations.
eCom'23: The SIGIR 2023 Workshop on eCommerce

eCommerce Information Retrieval (IR) is receiving increasing attention in the academic literature and is an essential component of some of the largest web sites (e.g. Airbnb, Alibaba, Amazon, eBay, Facebook, Flipkart, Lowes's, Taobao, Target). SIGIR has for several years seen sponsorship from eCommerce organizations, reflecting the importance of IR research to them. The purpose of this workshop is (1) to bring together researchers and practitioners of eCommerce IR to discuss topics unique to it, (2) to determine how to use eCommerce's unique combination of free text, structured data, and customer behavior data to improve search relevance, and (3) to examine how to build datasets and evaluate algorithms in this domain. The theme of this year's eCommerce IR workshop is Foundation Models and Unified Information Access in eCommerce. The workshop solicits papers on this topic and includes a panel focused on this area. In addition, Lowe's is sponsoring an eCommerce data challenge on Cross-modal and Multi-modal Visual Search for eCommerce. The data challenge reflects themes from the successful SIGIR workshops in 2017, 2018, 2019, 2020, 2021, and 2022. ECOM23 will be held as a full day hybrid workshop to accommodate for diverse participation.
Measuring Service-Level Learning Effects in Search Via Query-Randomized Experiments

In order to determine the relevance of a given item to a query, most modern search ranking systems make use of features which aggregate prior user behavior for that item and query (e.g. click rate). For practical reasons, when running A/B tests on ranking systems, these features are generally shared between all treatments. For the most common experiment designs, which randomize traffic by user or by session, this creates a pathway by which the behavior of units in one treatment can effect the outcomes for units in other treatments, violating the Stable Unit Treatment Value Assumption (SUTVA) and biasing measured outcomes. Moreover, for experiments targeting improvements to the behavior data available to such features (e.g. online exploration), this pathway is precisely the one we are trying to affect; if such changes occur identically in treatment and control, then they cannot be measured. To address this, we propose the use of experiments which instead randomize traffic based on the search query. To validate our approach, we perform a pair of A/B tests on an explore-exploit framework in the Amazon search page: one under query randomization, and one under user randomization. In line with the theoretical predictions, we find that the query-randomized iteration is able to measure a statistically significant effect (+0.66% Purchases, p=0.001) where the user-randomized iteration does not (-0.02% Purchases, p=0.851).
Longitudinal Control of Vehicle Platoon Based on Sliding Mode Algorithm

The coordinate control problem of vehicle platoon is studied with the sliding mode control (SMC) method in this paper. Starting from the inter-vehicle safety distance, a longitudinal controller for vehicle platoon is developed to improve the stability by incorporating information on the positions, inter-vehicle distance, speed, and acceleration of the current and preceding vehicles. The effectiveness of the proposed controller is verified through joint simulation with Carsim and MATLAB/Simulink. Simulation results demonstrate that the problem of excessively large safety distance is resolved and the stability of vehicle platooning system is also improved with the proposed controller.
Spatio-Temporal Hypergraph Learning for Next POI Recommendation

Next Point-of-Interest (POI) recommendation task focuses on predicting the immediate next position a user would visit, thus providing appealing location advice. In light of this, graph neural networks (GNNs) based models have recently been emerging as breakthroughs for this task due to their ability to learn global user preferences and alleviate cold-start challenges. Nevertheless, most existing methods merely focus on the relations between POIs, neglecting the higher-order information including user trajectories and the collaborative relations among trajectories. In this paper, we propose the Spatio-Temporal HyperGraph Convolutional Network (STHGCN). This model leverages a hypergraph to capture the trajectory-grain information and learn from user's historical trajectories (intra-user) as well as collaborative trajectories from other users (inter-user). Furthermore, a novel hypergraph transformer is introduced to effectively combine the hypergraph structure encoding with spatio-temporal information. Extensive experiments on real-world datasets demonstrate that our model outperforms the existing state-of-the-art methods and further analysis confirms the effectiveness in alleviating cold-start issues and achieving improved performance for both short and long trajectories.
A Consumer Compensation System in Ride-hailing Service

In the ride-hailing business, compensation is mostly used to motivate consumers to place more orders and grow the market scale. However, most of the previous studies focus on car-hailing services. Few works investigate localized smart transportation innovations, such as intra-city freight logistics and designated driving. In addition, satisfying consumer fairness and improving consumer surplus, with the objective of maximizing revenue, are also important. In this paper, we propose a consumer compensation system, where a transfer learning enhanced uplift modeling is designed to measure the elasticity, and a model predictive control based optimization is formulated to control the budget accurately. Our implementation is effective and can keep the online environment lightweight. The proposed system has been deployed in the production environment of the real-world ride-hailing platform for 300 days, which outperforms the expert strategy by using 0.5% less subsidy and achieving 14.4% more revenue.
Click-Conversion Multi-Task Model with Position Bias Mitigation for Sponsored Search in eCommerce

Position bias, the phenomenon whereby users tend to focus on higher-ranked items of the search result list regardless of the actual relevance to queries, is prevailing in many ranking systems. Position bias in training data biases the ranking model, leading to increasingly unfair item rankings, click-through-rate (CTR), and conversion rate (CVR) predictions. To jointly mitigate position bias in both item CTR and CVR prediction, we propose two position-bias-free CTR and CVR prediction models: Position-Aware Click-Conversion (PACC) and PACC via Position Embedding (PACC-PE). PACC is built upon probability decomposition and models position information as a probability. PACC-PE utilizes neural networks to model product-specific position information as embedding. Experiments on the E-commerce sponsored product search dataset show that our proposed models have better ranking effectiveness and can greatly alleviate position bias in both CTR and CVR prediction.
Multiple Topics Community Detection in Attributed Networks

Since existing methods are often not effective to detect communities with multiple topics in attributed networks, we propose a method named SSAGCN via Autoencoder-style self-supervised learning. SSAGCN firstly designs an adaptive graph convolutional network (AGCN), which is treated as the encoder for fusing topology information and attribute information automatically, and then utilizes a dual decoder to simultaneously reconstruct network topology and attributes. By further introducing the modularity maximization and the joint optimization strategies, SSAGCN can detect communities with multiple topics in an end-to-end manner. Experimental results show that SSAGCN outperforms state-of-the-art approaches, and also can be used to conduct topic analysis well.
An algorithm for arbitrary–order cumulant tensor calculation in a sliding window of data streams

Abstract High-order cumulant tensors carry information about statistics of non-normally distributed multivariate data. In this work we present a new efficient algorithm for calculation of cumulants of arbitrary orders in a sliding window for data streams. We show that this algorithm offers substantial speedups of cumulant updates compared with the current solutions. The proposed algorithm can be used for processing on-line high-frequency multivariate data and can find applications, e.g., in on-line signal filtering and classification of data streams. To present an application of this algorithm, we propose an estimator of non-Gaussianity of a data stream based on the norms of high order cumulant tensors. We show how to detect the transition from Gaussian distributed data to non-Gaussian ones in a data stream. In order to achieve high implementation efficiency of operations on super-symmetric tensors, such as cumulant tensors, we employ a block structure to store and calculate only one hyper-pyramid part of such tensors.
Theoretically and Practically Efficient Parallel Nucleus Decomposition (Abstract)

Discovering dense substructures in graphs is a fundamental topic in graph mining, and has been studied across many areas including computational biology, spam and fraud-detection, and large-scale network analysis. Recently, Sariyuce et al. introduced the nucleus decomposition problem, which generalizes the influential notions of k-cores and k-trusses to k-(r,s) nucleii, and can better capture higher-order structures. Informally, a k-(r,s) nucleus is the maximal induced subgraph such that every r-clique in the subgraph is contained in at least k s-cliques. The goal of the (r, s) nucleus decomposition problem is to identify for each r-clique in the graph, the largest k such that it is in a k-(r,s) nucleus. Solving the (r, s) nucleus decomposition problem is a significant computational challenge for several reasons. First, simply counting and enumerating s-cliques is a challenging task, even for modest s. Second, storing information for all r-cliques can require a large amount of space, even for relatively small graphs. Third, engineering fast and high-performance solutions to this problem requires taking advantage of parallelism due to the computationally-intensive nature of listing cliques. There are two well-known parallel paradigms for approaching the (r, s) nucleus decomposition problem, a global peeling-based model and a local update model that iterates until convergence. The former is inherently challenging to parallelize due to sequential dependencies and necessary synchronization steps, which we address in this paper, and we demonstrate that the latter requires orders of magnitude more work to converge to the same solution and is thus less performant.
One Stop Shop for Question-Answering Dataset Selection

In this paper, we offer a new visualization tool -- Dataset Statistical View (DSV), to lower the barrier of research entry by providing easy access to the question-answering (QA) datasets that researchers can build their work upon. Our target users are new researchers to the QA domain with no prior knowledge nor programming skills. The system is populated with multiple QA datasets, which covers a wide range of QA tasks. It allows researchers to explore and compare existing QA datasets at a one-stop website. The system shows statistical graphs for each QA dataset to offer an overview and a visual comparison between datasets. Although this paper focuses mainly at the syntactic level comparison, integrating bias and semantic level analysis is our ongoing work. We believe our DSV system is a valuable contribution to the advancement of the QA field, as it provides a solid starting point for new researchers and practitioners. An overview of the framework is demonstrated in this paper and the introduction of the application system is available at https://cnchuy.github. io/images/demo.mp4.
How Common are Common Wrong Answers? Crowdsourcing Remediation at Scale

Solving mathematical problems is cognitively complex, involving strategy formulation, solution development, and the application of learned concepts. However, gaps in students' knowledge or weakly grasped concepts can lead to errors. Teachers play a crucial role in predicting and addressing these difficulties, which directly influence learning outcomes. However, preemptively identifying misconceptions leading to errors can be challenging. This study leverages historical data to assist teachers in recognizing common errors and addressing gaps in knowledge through feedback. We present a longitudinal analysis of incorrect answers from the 2015-2020 academic years on two curricula, Illustrative Math and EngageNY, for grades 6, 7, and 8. We find consistent errors across 5 years despite varying student and teacher populations. Based on these Common Wrong Answers (CWAs), we designed a crowdsourcing platform for teachers to provide Common Wrong Answer Feedback (CWAF). This paper reports on an in vivo randomized study testing the effectiveness of CWAFs in two scenarios: next-problem-correctness within-skill and next-problem-correctness within-assignment, regardless of the skill. We find that receiving CWAF leads to a significant increase in correctness for consecutive problems within-skill. However, the effect was not significant for all consecutive problems within-assignment, irrespective of the associated skill. This paper investigates the potential of scalable approaches in identifying Common Wrong Answers (CWAs) and how the use of crowdsourced CWAFs can enhance student learning through remediation.
A Survey of Malware Analysis Using Community Detection Algorithms

In recent years, we have witnessed an overwhelming and fast proliferation of different types of malware targeting organizations and individuals, which considerably increased the time required to detect malware. The malware developers make this issue worse by spreading many variants of the same malware [13]. To deal with this issue, graph theory techniques, and particularly community detection algorithms, can be leveraged to achieve bulk detection of malware families and variants to identify malicious communities instead of focusing on the detection of an individual instance of malware, which could significantly reduce the detection time. In this article, we review the state-of-the-art malware analysis solutions that employ community detection algorithms and provide a taxonomy that classifies the solutions with respect to five facets: analysis task, community detection approach, target platform, analysis type, and source of features. We present the solutions with respect to the analysis task, which covers malware detection, malware classification, cyber-threat infrastructure detection, and feature selection. The findings of this survey indicate that there is still room for contributions to further improve the state of the art and address research gaps. Finally, we discuss the advantages and the limitations of the solutions, identify open issues, and provide future research directions.
Exploring the Spatiotemporal Features of Online Food Recommendation Service

Online Food Recommendation Service (OFRS) has remarkable spatiotemporal characteristics and the advantage of being able to conveniently satisfy users' needs in a timely manner. There have been a variety of studies that have begun to explore its spatiotemporal properties, but a comprehensive and in-depth analysis of the OFRS spatiotemporal features is yet to be conducted. Therefore, this paper studies the OFRS based on three questions: how spatiotemporal features play a role; why self-attention cannot be used to model the spatiotemporal sequences of OFRS; and how to combine spatiotemporal features to improve the efficiency of OFRS. Firstly, through experimental analysis, we systemically extracted the spatiotemporal features of OFRS, identified the most valuable features and designed an effective combination method. Secondly, we conducted a detailed analysis of the spatiotemporal sequences, which revealed the shortcomings of self-attention in OFRS, and proposed a more optimized spatiotemporal sequence method for replacing self-attention. In addition, we also designed a Dynamic Context Adaptation Model to further improve the efficiency and performance of OFRS. Through the offline experiments on two large datasets and online experiments for a week, the feasibility and superiority of our model were proven.
Synerise Monad: A Foundation Model for Behavioral Event Data

The complexity of industry-grade event-based datalakes grows dynamically each passing hour. Companies actively gather behavioral information on their customers, recording multiple types of events, such as clicks, likes, page views, card transactions, add-to-basket, or purchase events. In response to this, the Synerise Monad platform has been proposed. The primary focus of Monad is to produce Universal Behavioral Representations (UBRs) - large vectors encapsulating the behavioral patterns of each user. UBRs do not lose knowledge about individual events, in contrast to aggregated features or averaged embeddings. They are based on award-winning algorithms developed at Synerise - Cleora and EMDE - and allow to process real-life datasets composed of billions of events in record time. In this paper, we introduce a new aspect of Monad: private foundation models for behavioral data, trained on top of UBRs. The foundation models are trained in purely self-supervised manner and allow to exploit general knowledge about human behavior, which proves especially useful when multiple downstream models must be trained and time constraints are tight, or when labeled data is scarce. Experimental results show that the Monad foundation models can cut training time in half and require 3x less data to reach optimal results, often achieving state-of-the-art results.
Object Detection Algorithm of Stairs Based on Multi-information Fusion

In view of the mobile robot's perception of the environment in the process of moving, the method of deep learning is used to detect the target terrain in order to send follow-up instructions to the robot. In this paper, a lightweight stairs detection algorithm based on improved YOLOv5-Lite is proposed, which uses RepVGG as the backbone network to extract features to build YOLOv5-Lite lightweight neural network model, and removes the Focus layer in the backbone network and replaces it with a 6x6 convolution layer to reduce the number of network parameters to improve the speed of model detection. Through the introduction of BasicRFB_s pooling layer, increase the receptive field of the network, and add C3SE attention mechanism in the pooling layer to reduce pooling loss. To decrease the mistakes, we also segment the point cloud of stairs into vertical and horizontal planes by the difference of normal and consider their probability distribution for the further detection. The experimental results show that the accuracy of the improved lightweight algorithm can reach 97.1%, and the frame rate can reach 51 fps. After segmenting the point cloud, the accuracy of detection method can reach 99.9% and the frame rate can reach 46 fps. The results show that the proposed method has the characteristics of real-time, lightweight and high accuracy.