Reviewerly/exHarmony huggingface dataset:
HOFD: An Outdated Fact Detector for Knowledge Bases

Knowledge bases (KBs), which store high-quality information, are crucial for many applications, such as enhancing search results and serving as external sources for data cleaning. Not surprisingly, there exist outdated facts in most KBs due to the rapid change of information. Naturally, it is important to keep KBs up-to-date. Traditional wisdom has investigated the problem of using reference data (such as new facts extracted from the news) to detect outdated facts in KBs. However, existing approaches can only cover a small percentage of facts in KBs. In this paper, we propose <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HOFD</monospace> , a novel human-in-the-loop approach for outdated fact detection in KBs. <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HOFD</monospace> trains a binary classifier using features such as historical update frequency and update time of a fact to compute the likelihood of a fact in a KB to be outdated. Then, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HOFD</monospace> interacts with humans to verify whether a fact with high likelihood is indeed outdated. In addition, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HOFD</monospace> also uses logical rules to detect more outdated facts based on human feedback. The outdated facts detected by the logical rules will also be fed back to train the ML model further for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data augmentation</i> . Extensive experiments on real-world KBs, such as Yago and DBpedia, show the effectiveness of our solution.
Hitchhiker: Accelerating ORAM With Dynamic Scheduling

Oblivious RAM (ORAM) remains a bittersweet protection of memory access patterns because of its prohibitively high overhead. The root cause is that ORAM hides intended accesses among a sufficiently large number of dummy accesses. Most existing optimizations mitigate memory accesses using architectural enhancements (e.g., cache) yet few of them improve the efficiency of ORAM primitives per se. In this paper, we identify path-grained static scheduling as a fundamental ORAM performance bottleneck. We propose level-grained dynamic scheduling that directly optimizes ORAM primitives to boost efficiency. It enables ORAM to service more than one request per path and write paths batch wise. We can thus boost ORAM efficiency through handling queued requests as soon as possible and remove as many redundant accesses as possible. Since optimized memory accesses still target the same set of paths, dynamic scheduling preserves ORAM security. We implement dynamic scheduling through Hitchhiker ORAM. In comparison with the state-of-the-art primitive-optimized Fork Path ORAM, Hitchhiker ORAM yields 31.5% fewer memory accesses, 60.2% shorter latency, and 40.7% less energy consumption, being 2.5× faster. In comparison with the state-of-the-art architecture-optimized <inline-formula><tex-math notation="LaTeX">$\rho$</tex-math></inline-formula> , Hitchhiker ORAM is 1.5× faster and the integrated version— <inline-formula><tex-math notation="LaTeX">$\rho$</tex-math></inline-formula> -Hitchhiker ORAM is 2.0× faster.
AAPP: An Accelerative and Adaptive Path Planner for Robots on GPU

Optimal path planning is one of the major bottlenecks for the effective navigation of robots working towards accomplishing complex missions. To overcome the bottleneck and support efficient applications, this paper presents <monospace>AAPP</monospace> , an accelerative and adaptive path planner based on RRT*, a popular path planning algorithm, on GPU, to alleviate four main performance limitations, i.e., bandwidth limitation, load imbalance, high computing complexity, and the choice of parameters. First, <monospace>AAPP</monospace> employs a data storage structure, named simplified compressed sparse rows (SCSR), to compress the large-scale map data and increase the utilization of bandwidth. Second, to exploit the computing performance of GPU, we propose a two-layer parallel framework for RRT* based on SCSR format, named TLRRT*, by using the dynamic parallelism technique. Third, aiming at the problems of parallel load imbalance and high computing complexity in TLRRT*, we further design a two-stage parallel framework, named TSRRT*, that fully exploits hardware heterogeneity (CPU/GPU) by scheduling tasks on CPU and GPU adaptively. Finally, we present optimizations for <monospace>AAPP</monospace> to adaptively select execution schemes and parameters. Experimental results on a heterogeneous CPU/GPU machine show that <monospace>AAPP</monospace> yields the speedup up to <inline-formula><tex-math notation="LaTeX">$22.72\times$</tex-math></inline-formula> over the RRT* algorithm. Compared to the state-of-the-art, <monospace>AAPP</monospace> can handle large-scale datasets and obtain feasible solutions with shorter trajectory lengths.
Integrating Both Parallax and Latency Compensation into Video See-through Head-mounted Display

This work introduces a perspective-corrected video see-through mixed-reality head-mounted display with edge-preserving occlusion and low-latency capabilities. To realize the consistent spatial and temporal composition of a captured real world containing virtual objects, we perform three essential tasks: 1) to reconstruct captured images so as to match the user's view; 2) to occlude virtual objects with nearer real objects, to provide users with correct depth cues; and 3) to reproject the virtual and captured scenes to be matched and to keep up with users' head motions. Captured image reconstruction and occlusion-mask generation require dense and accurate depth maps. However, estimating these maps is computationally difficult, which results in longer latencies. To obtain an acceptable balance between spatial consistency and low latency, we rapidly generated depth maps by focusing on edge smoothness and disocclusion (instead of fully accurate maps), to shorten the processing time. Our algorithm refines edges via a hybrid method involving infrared masks and color-guided filters, and it fills disocclusions using temporally cached depth maps. Our system combines these algorithms in a two-phase temporal warping architecture based upon synchronized camera pairs and displays. The first phase of warping is to reduce registration errors between the virtual and captured scenes. The second is to present virtual and captured scenes that correspond with the user's head motion. We implemented these methods on our wearable prototype and performed end-to-end measurements of its accuracy and latency. We achieved an acceptable latency due to head motion (less than 4 ms) and spatial accuracy (less than 0.1° in size and less than 0.3° in position) in our test environment. We anticipate that this work will help improve the realism of mixed reality systems.
DAGA: Dynamics Aware Reinforcement Learning With Graph-Based Rapid Adaptation

Reinforcement learning (RL) has achieved some notable accomplishments in robotics. However, one of the major challenges when applying RL in the real world is that unexpected disturbances in local dynamics could lead to failures of policies at test time. It is necessary to learn a model capable of adapting to various environments with different dynamics. In this paper, a Dynamic-Aware reinforcement learning model with graph-based rapid adaptation (DAGA) is proposed to address these challenges. DAGA encodes the dynamic features from a few interactions and guides the policy with an environment embedding. To encourage the embedding to capture variations in dynamics, we present an objective function based on forward prediction and environment similarity. The proposed model enables the robot to generalize with a wide range of transition dynamics resulted from different hardware parameters. Experiments in robot locomotion and manipulation tasks show that DAGA outperforms existing baselines in both better sample efficiency and generalization. DAGA has the potential to deploy the RL policy in realistic and changing environments.
Understanding complex dynamics in two and three dimensions

This chapter presents the basic ideas behind how turbulent motions are represented in lake models. The notion of Reynolds averaging is presented, and used to demonstrate the turbulence closure problem. The Reynolds stress is derived and the eddy viscosity closure is presented. The notion of spatial averaging is presented as an alternative to Reynolds averaging, and the large-eddy simulation idea is derived. It is shown that the large eddy simulation equivalent of the Reynolds stress is algebraically more complicated, and the well known Smagorinsky model for eddy viscosity is presented. Two dimensional motions of rotation modified, dispersive shallow water equations are used to demonstrate slow-fast dynamics, and to illustrate how much is lost when the eddy viscosity is chosen to be too high.
Deep Tabular Data Modeling With Dual-Route Structure-Adaptive Graph Networks

Thanks to the inherent spatial or sequential structures underlying the data like images and texts, deep architectures such as convolutional neural networks (CNNs) and the Transformer have been recognized as the preeminent approaches in image processing and language modeling. In the real world, there are a large number of tabular data without any explicit structures, which breaks the inductive bias of most neural networks like CNNs. Although multi-layer perceptrons (MLPs) obtain empirical success on tabular data, they cannot well explain the underlying relationship between multiple variables. Compared with other fields, research on deep models toward tabular data has received relatively less scrutiny. To bridge this gap, we propose Dual-Route Structure-Adaptive Graph Networks (DRSA-Net) to model the nonlinearity in tabular feature vectors without any prior. DRSA-Net adaptively learns a sparse graph structure between variables and then characterizes interactions between them from the view of dual-route message passing. We demonstrate that DRSA-Net could easily degenerate into the typical MLPs and factorization machines (FMs). Extensive experiments on recommendations, images (no spatial information after preprocessing), and some benchmark machine learning datasets show that DRSA-Net achieves comparable or superior performance with many classic algorithms and recently proposed deep models.
WDAN: A Weighted Discriminative Adversarial Network With Dual Classifiers for Fine-Grained Open-Set Domain Adaptation

Deep neural networks usually depend on substantial labeled data and suffer from poor generalization to new domains. Domain adaptation can be used to resolve these issues, using a classifier trained with a label-rich source and transferred to a label-scarce target domain. Traditional domain adaptation adopts the close-set assumption that both domains share the same classes. However, real-world applications operate in an open-set scenario where target domains have private categories. This aspect is considered by open-set domain adaptation (OSDA). Nevertheless, current OSDA benchmarks lack clear definitions of semantic classes that are at the core of the open-set concept. In this study, we propose fine-grained visual categorization (FGVC) datasets containing specific descriptions of semantic classes as a solution, introducing the new setting named fine-grained OSDA. Owing to the entanglement among FGVC, unknown class recognition, and domain adaptation, fine-grained OSDA is a challenging task. For this reason, we designed a weighted discriminative adversarial network with dual classifiers (WDAN). It utilizes a selective transformer encoder with overlapping patches and supervised contrastive learning to extract features suitable for FGVC, adversarial training with domain-specific discriminative information to recognize target-private classes, and a weighted conditional domain discriminator to learn domain-invariant features for domain adaptation. Extensive experiments on five benchmarks, including one newly built, demonstrated that WDAN outperforms state-of-the-art methods. This work fills the existing gap in benchmarks for fine-grained OSDA, promoting future developments of real-world applications.
A Fast Kernel-Based Optimal Control Framework for Autonomous Driving

A novel, promising strategy to improve path-tracking performance for autonomous driving is to combine a nominal model with an additive residual model. The residual part is identified by a kernel method Gaussian process (GP) regression from continuous data streaming. However, the high computational complexity of simultaneously using both numerical optimization and supervised learning is a critical challenge for applications with fast dynamics. This also limits the generalization capability in untrained circumstance. To address these issues, we design a feedforward compensator to handle the stochastic GP model. This allows an analytical solution to optimize the formulation, and therefore greatly lowers the computational burden. Our experimental results show that the computational time was reduced by 40 ms in comparison to the classical general-source solver. Consequently, the learning part can now track 19% more data points in one control step, saving more computation power to be allocated to prediction. In addition, we optimize the prediction dimension and apply the local technology to cluster intractable dictionary. Together, these improvements allow our fast kernel-based optimal control (FKB-OPT) framework to exhibit superior performance under inexperienced trajectories in the road tests.
Enabling Privacy-Preserving and Efficient Authenticated Graph Queries on Blockchain-Assisted Clouds

Prior research has introduced a new scenario of blockchain-assisted clouds where the data owner outsources original data to cloud servers and stores some metadata on the blockchain. Despite some research on key-value query and range query in this hybrid-storage scenario, other more complicated data types are not yet supported. In this article, we conduct pioneering research on authenticated queries for graph data, which is a popular data type such as the knowledge graph data, on the blockchain-assisted cloud. The primary challenge is how to design an authenticated data structure (ADS) that supports authenticated queries and can be easily maintained by the blockchain. To this end, we propose a novel ADS, named PAGB, based on the RSA accumulator and completeness set. It can also prevent the original data from being revealed to the public through blockchain or irrelevant queries. We further optimize our design to be more efficient in terms of communication and computation. The effectiveness and efficiency of PAGB are verified through theoretical analysis and extensive experiments.
Toward fully-decentralized system with hyperledger fabric

A system using Hyperledger Fabric (Hyperledger Fabric-based system) provides the value of executing transactions inter multiple organizations in a decentralized manner. To maximize this value, it is essential to maintain decentralization not only in the execution system but also in the entire system, including system configuration and management. On the other hand, Hyperledger Fabric is still in the process of maturing and full-decentralization is still difficult to achieve. In this paper, we analyze comprehensive basic requirements for realizing fully-decentralized Hyperledger Fabric-based systems and design a reference architecture to satisfy the basic requirements.This section design a reference architecture of fully-decentralized Hyperledger Fabric-based systems that satisfies the basic requirements as shown in Sec 4. Since the basic requirements are too abstract to consider a reference architecture directly, we first break down them into the specific requirements from the architectural perspective and then design a reference architecture.
An Intelligent Tutoring System for Math Word Problem Solving with Tutorial Solution Generation

To provide the step by step tutoring service like a human tutor, an intelligent tutoring system (ITS) for math word problem solving (MathITS) is proposed in this paper. The proposed MathITS has an ability of automatically generate tutorial solutions for any user input problems and thus could be widely used in after-class tutoring. An improved math word problem solver is applied to generate the tutorial solution, which transforms expression solutions into logic sequences of arithmetic operations with illustrating texts. In stage of adaptive tutoring, hints and suggestions are generated and launched to students rather giving them explicit solutions. Finally, an evaluation module is provided which gives immediate feedback on the evaluation of the whole process of multi-turn tutoring interaction. A pioneer experiment is conducted and the results demonstrate the efficiency of the proposed system.
Linear Function Relation Identification Based on BERT and Bi-LSTM

Problem solving technology is a hot research issue in intelligent education. Linear function scenario problem is one of the important types of problems. This paper presents a linear function relation identification algorithm for solving linear function problems. Firstly, the problem text was transformed into semantic vectors through the BERT model. Secondly, a linear function relation candidate set is created and a Bi-LSTM based identification model is used to select the correct set of linear relations among candidates. Finally, a two-stage solving method is used to obtain the implicit and explicit relations from the correct set of linear relations to get the result. The experiment was tested on 486 linear function scenario problems. The result shows our algorithm achieved 86.1% accuracy in finding the correct set of linear relations and 59.4% accuracy in solving linear function scenario problems.
A Comparative Analysis of Math Word Problem Solving on Characterized Datasets

Benefit from the neural network research, a couple of neural solvers have been developed for automatically solving math word problems (MWPs). These neural solvers are evaluated on several benchmark datasets with diverse characteristics, which leads to a poor comparability of the performance of each solver. To address the problem, a comparative analysis is conducted in this paper to explore the performance variations of neural solvers in solving different characteristic MWPs. The architectures of the typical neural solvers are studied and a four-dimensional index model is proposed to characterize the benchmark dataset into different subsets. The experimental results show that the Seq2Seq-based model solvers perform well on most of the subsets, while Graph2Tree based solvers seem to have more potential in solving problems with complex expression structures.
Research on Solving Text-Diagram Function Problems and Its Application in Tutoring System

The function learning, as a major focus of attention for the mathematics education research community for several decades, requires humanized tutoring. Recently, several intelligent tutoring systems for mathematics learning have been proposed, which are relying on expert design and hand-crafted worked examples. However, there exists a strong need for a humanized intelligent tutoring platform for function learning, because (i) the function problems being tutored should not be limited by the worked examples, and (ii) it must be able to handle step-wise tutoring and instant feedback in response to a learner's answer. In order to achieve this goal, a relationcentric solving algorithm to support the above needs is proposed, allowing students to provide unrestricted input to the ITSFL platform and providing humanized tutoring service. Results from several empirical studies suggest that the proposed platform can be used in function learning effectively.
LFCKT: A Learning and Forgetting Convolutional Knowledge Tracking Model

Personalized exercise recommendation is a key research direction of personalized learning. In personalized exercise recommendation, we recommend suitable exercises for students according to their knowledge mastery status to improve their learning efficiency. Therefore, the accuracy of predicting students’ knowledge state in personalized exercise recommendation affects the goodness of the exercise recommendation. In the process of students’ learning, learning behavior and forgetting behavior are intertwined, and students’ forgetting behavior has a great influence on the knowledge state. In order to accurately model students’ learning and forgetting, we propose a Learning and Forgetting Convolutional Knowledge Tracking model (LFCKT) that takes into account both learning and forgetting behaviors. The model takes into account three factors that affect knowledge forgetting, including the interval time of target knowledge interaction, the count of past target knowledge interaction and student’s state of knowledge. LFCKT model uses students’ answer results as indirect feedback of knowledge mastery in the process of knowledge tracking, and integrates individual personalized learning behavior and individual forgetting behavior. Through experiments on the real online education public dataset, LFCKT can better track students’ knowledge mastery status and has better predictive performance than current knowledge tracking models.
Teacher Attention Measurement Based on Head Pose Estimation

Teachers’ attention plays an important role in guiding students’ attention and communicating with students. It is a challenging task to measure teachers’ attention non intrusively. This paper first gives the definition of this task, and proposes a specific method to measure teachers’ attention in a given video based on head pose estimation. This method obtains the head posture information by inputting the head image information in the video into the Hopenet, determines the gaze point constant based on the tangent function of the pitch angle, and calculates the teacher’s attention based on the line of gaze and the gaze point range. The experimental results show that the method has high accuracy and is close to human recognition level.
Scientific Documents Collection and Summarization for Survey Writing

In the process of survey writing, searching for scientific documents related to a topic and fully understanding these documents are two critical but time-consuming steps. Automatic paper collection and summarization technologies can help to improve work efficiency and work quality. Therefore, this work-in-progress paper integrates the citation recommendation model, the structured content extraction model, and the long scientific documents summarization model to propose an automatic scientific document collection and summarization system. This system can extract topics from single or multiple paragraphs, collect relevant papers, and generate a summary table for the collected papers to benefit survey writing.
Prompt-Based Missing Entity Recovery for Solving Arithmetic Word Problems

Most existing neural models solve arithmetic word problems from explicit problem text. However, they can hardly give the solution procedure for problems that contain implicit quantity relations. This paper proposes a missing entity recovery(MER) model to solve arithmetic word problems(AWPs) with implicit knowledge. Given an AWP, the model effectively identifies and represents its explicit expressions into the Nodes Dependency Graph(NDG). Then the nodes on the graph get implicit knowledge from the knowledge base in a recursive way. The group of selected nodes is finally transformed into a group of equations using the solving engine to obtain the answers. The proposed algorithm is evaluated practically based on a collection of established datasets Math23K, showcasing its high accuracy in problem-solving and application in various application situations.