Reviewerly/exHarmony huggingface dataset:
Construction of Bilingual Teaching Mode Based on Digital Twinning Technology

Based on the digital twin technology, a digital twin platform can be built to connect the real teaching space with the virtual teaching space and become the mainstream of online teaching space. All this has determined that the social demand for designers' education is undergoing fundamental changes. The so-called "scientific and technological progress, education first" bilingual education is undergoing comprehensive and profound changes in the digital age, which has a strong impact on the traditional bilingual teaching mode and concept. Traditional concepts, aging theoretical knowledge, and backward teaching methods will inevitably be eliminated and updated gradually in the contest with digitalization, which makes it necessary to transform traditional bilingual education into digital bilingual education. Through the comparative experimental analysis of the teaching effect, the independent sample t-test shows that the t-statistic is 3.634, and the corresponding significance level is 0.013, which is less than 0.05. It shows that there are significant differences between boys and girls in bilingual teaching in this class of digital twin technology experimental teaching. However, compared with the control class, the results of both boys and girls are higher, so to some extent, it shows that the application of digital twin technology experimental teaching in bilingual teaching will indeed produce certain results.
Detecting Cocircular Subsets of a Spherical Set of Points

Given a spherical set of points, we consider the detection of cocircular subsets of the data. We distinguish great circles from small circles, and develop algorithms for detecting cocircularities of both types. The suggested approach is an extension of the Hough transform. We address the unique parameter-space quantization issues arising due to the spherical geometry, present quantization schemes, and evaluate the quantization-induced errors. We demonstrate the proposed algorithms by detecting cocircular cities and airports on Earth’s spherical surface. These results facilitate the detection of great and small circles in spherical images.
Detecting Frozen Phrases in Open-Domain Question Answering

There is essential information in the underlying structure of words and phrases in natural language questions, and this structure has been extensively studied. In this paper, we study one particular structure, referred to as frozen phrases, that is highly expected to transfer as a whole from questions to answer passages. Frozen phrases, if detected, can be helpful in open-domain Question Answering (QA) where identifying the localized context of a given input question is crucial. An interesting question is if frozen phrases can be accurately detected. We cast the problem as a sequence-labeling task and create synthetic data from existing QA datasets to train a model. We further plug this model into a sparse retriever that is made aware of the detected phrases. Our experiments reveal that detecting frozen phrases whose presence in answer documents are highly plausible yields significant improvements in retrievals as well as in the end-to-end accuracy of open-domain QA models.
Randomized-to-Canonical Model Predictive Control for Real-World Visual Robotic Manipulation

Many works have recently explored Sim-to-real transferable visual model predictive control (MPC). However, such works are limited to one-shot transfer, where real-world data must be collected once to perform the sim-to-real transfer, which remains a significant human effort in transferring the models learned in simulations to new domains in the real world. To alleviate this problem, we first propose a novel model-learning framework called Kalman Randomized-to-Canonical Model (KRC-model). This framework is capable of extracting task-relevant intrinsic features and their dynamics from randomized images. We then propose Kalman Randomized-to-Canonical Model Predictive Control (KRC-MPC) as a zero-shot sim-to-real transferable visual MPC using KRC-model. The effectiveness of our method is evaluated through a valve rotation task by a robot hand in both simulation and the real world, and a block mating task in simulation. The experimental results show that KRC-MPC can be applied to various real domains and tasks in a zero-shot manner.
A Meta-learning Approach to Fair Ranking

In recent years, the fairness in information retrieval (IR) system has received increasing research attention. While the data-driven ranking models achieve significant improvements over traditional methods, the dataset used to train such models is usually biased, which causes unfairness in the ranking models. For example, the collected imbalance dataset on the subject of the expert search usually leads to systematic discrimination on the specific demographic groups such as race, gender, etc, which further reduces the exposure for the minority group. To solve this problem, we propose a Meta-learning based Fair Ranking (MFR) model that could alleviate the data bias for protected groups through an automatically-weighted loss. Specifically, we adopt a meta-learning framework to explicitly train a meta-learner from an unbiased sampled dataset (meta-dataset), and simultaneously, train a listwise learning-to-rank (LTR) model on the whole (biased) dataset governed by "fair" loss weights. The meta-learner serves as a weighting function to make the ranking loss attend more on the minority group. To update the parameters of the weighting function and the ranking model, we formulate the proposed MFR as a bilevel optimization problem and solve it using the gradients through gradients. Experimental results on several real-world datasets demonstrate that the proposed method achieves a comparable ranking performance and significantly improves the fairness metric compared with state-of-the-art methods.
Generating Game Levels of Diverse Behaviour Engagement

Recent years, there has been growing interests in experience-driven procedural level generation. Various metrics have been formulated to model player experience and help generate personalised levels. In this work, we question whether experience metrics can adapt to agents with different personas. We start by reviewing existing metrics for evaluating game levels. Then, focusing on platformer games, we design a framework integrating various agents and evaluation metrics. Experimental studies on Super Mario Bros. indicate that using the same evaluation metrics but agents with different personas can generate levels for particular persona. It implies that, for simple games, using a game-playing agent of specific player archetype as a level tester is probably all we need to generate levels of diverse behaviour engagement.
Level-wise aligned dual networks for text–video retrieval

Abstract The vast amount of videos on the Internet makes efficient and accurate text–video retrieval tasks increasingly important. The current methods leverage a high-dimensional space to align video and text for these tasks. However, a high-dimensional space cannot fully use different levels of information in videos and text. In this paper, we put forward a method called level-wise aligned dual networks (LADNs) for text–video retrieval. LADN uses four common latent spaces to improve the performance of text–video retrieval and utilizes the semantic concept space to increase the interpretability of the model. Specifically, LADN first extracts different levels of information, including global, local, temporal, and spatial–temporal information, from videos and text. Then, they are mapped into four different latent spaces and one semantic space. Finally, LADN aligns different levels of information in various spaces. Extensive experiments conducted on three widely used datasets, including MSR-VTT, VATEX, and TRECVID AVS 2016-2018, demonstrate that our proposed approach is superior to several state-of-the-art text–video retrieval approaches.
DeSCoVeR: Debiased Semantic Context Prior for Venue Recommendation

We present a novel semantic context prior-based venue recommendation system that uses only the title and the abstract of a paper. Based on the intuition that the text in the title and abstract have both semantic and syntactic components, we demonstrate that a joint training of a semantic feature extractor and syntactic feature extractor collaboratively leverages meaningful information that helps to provide venues for papers. The proposed methodology that we call DeSCoVeR at first elicits these semantic and syntactic features using a Neural Topic Model and text classifier respectively. The model then executes a transfer learning optimization procedure to perform a contextual transfer between the feature distributions of the Neural Topic Model and the text classifier during the training phase. DeSCoVeR also mitigates the document-level label bias using a Causal back-door path criterion and a sentence-level keyword bias removal technique. Experiments on the DBLP dataset show that DeSCoVeR outperforms the state-of-the-art methods.
Investigation of the influence of measurement imperfections on quantum communication complexity superiority for the Clauser-Horne-Shimony-Holt game

Demonstrating quantum communication complexity superiority non-trivially with currently available experimental systems is of utmost importance in quantum information science. Here, we propose a generalized entanglement-assisted communication complexity reduction protocol and analyze the robustness of its quantum superiority against the measurement imperfections, such as measurement basis deviation and choice probability bias, a common problem rarely studied before. We find that the quantum superiority can be obtained in a specific entangled state in a suitable range of measurement basis and basis choice parameters. And the quantum superiority strengthens with the increase of the entanglement degree of quantum states. By using the maximum entangled state and its corresponding optimal measurement, the result we obtained violated the optimal classical bound by 239 standard deviations. Besides, the robustness of effective measurement basis in dephasing and depolarizing quantum channels is also investigated. These results not only make a step forward in investigating sufficient experimental conditions to unambiguously demonstrate the superiority of quantum communication complexity but also help to develop relevant practical applications.
Towards Decentralized Importance-based Multi-UAS Path Planning for Wildfire Monitoring

For area monitoring tasks, Unmanned Aircraft Systems (UASs) is one of the common considerations by the researchers and practitioners. When monitoring a small and spatially static area of interest, a single UAS might be adequate for the monitoring task. However, when monitoring a large area of interest that is evolving dynamically in space, such as a dynamical wildfire, efficient and optimized use of multiple UASs is required. Many of the existing works in UAS-based wildfire monitoring adopt a centralized approach for the path planning of the UASs. However, the use of centralized approaches is often limited in terms of applicability and adaptability. In addition, many of the applications do not consider the uneven nature of wildfire spread and thus the uneven importance of the fire boundary. To address these concerns, previously we developed a importance-based on-board path planning algorithm considering a single UAS. In this work, we extend our single UAS on-board path planning algorithm towards a decentralized and importance-based multi-UAS path planning algorithm. The design of the algorithm has been described in detail and preliminary results have been presented for a simulated wildfire spread scenario.
Learning to Enrich Query Representation with Pseudo-Relevance Feedback for Cross-lingual Retrieval

Cross-lingual information retrieval (CLIR) aims to provide access to information across languages. Recent pre-trained multilingual language models brought large improvements to the natural language tasks, including cross-lingual adhoc retrieval. However, pseudo-relevance feedback (PRF), a family of techniques for improving ranking using the contents of top initially retrieved items, has not been explored with neural CLIR retrieval models. Two of the challenges are incorporating feedback from long documents, and cross-language knowledge transfer. To address these challenges, we propose a novel neural CLIR architecture, NCLPRF, capable of incorporating PRF feedback from multiple potentially long documents, which enables improvements to query representation in the shared semantic space between query and document languages. The additional information that the feedback documents provide in a target language, can enrich the query representation, bringing it closer to relevant documents in the embedding space. The proposed model performance across three CLIR test collections in Chinese, Russian, and Persian languages, exhibits significant improvements over traditional and SOTA neural CLIR baselines across all three collections.
Controllable face editing for video reconstruction in human digital twins

High fidelity and controllable manipulation are critical to facial video reconstruction in human digital twins. Current generative adversarial networks (GANs) have achieved impressive performance in realistic face generation with high resolution, motivating several recent works to perform face editing via pretrained GANs. However, existing works suffer identity loss and semantic entanglement while editing real faces. To tackle these limitations, we propose a framework to perform controllable facial editing in video reconstruction. First, we propose to train a semantic inversion network to embed the target attribute change into the latent space of GANs. Disentangled semantic manipulation is performed during the semantic inversion by changing only the target attribute with the other unrelated attributes kept. Furthermore, we propose a novel personalized GAN inversion for the real face cropped from videos via retraining the generator of GANs, which can embed the real face into the latent space of GANs and preserve identity details for the real face. Finally, the realistic edited face is fused back into the original video. We use the identity preservation rate and disentanglement rate to evaluate the performance of our controllable face editing. Both qualitative and quantitative evaluations show that our method achieves prominent identity preservation and semantic disentanglement in controllable face editing, outperforming recent state-of-the-art methods.
Case Study on Design Considerations for Wide-Area Transmission Grid Operation Visual Storytelling

Effectively communicating the behavior of an electric grid is an important pursuit in education, research, and industrial applications. This paper presents considerations for visual storytelling as a means for interpreting and communicating the behavior of the grid for simulations ranging in duration from seconds to days. By framing the issue of communicating the vast amounts of data associated with wide-area transmission grid operation as a story to tell, those tasked with presenting this data are encouraged to consider what makes an effective story and, in turn, create compelling and understandable stand-alone narratives. Demonstrations are presented for cases of various scales including a 37-bus case, a 2000-bus case, and a 24,000-bus case. Narrative techniques including non-linear time and alternative path storytelling are shown.
Creating a User Model to Support User-specific Explanations of AI Systems

In this paper, we present a framework that supports providing user-specific explanations of AI systems. This is achieved by proposing a particular approach for modeling a user which enables a decision procedure to reason about how much detail to provide in an explanation. We also clarify the circumstances under which it is best not to provide an explanation at all, as one novel aspect of our design. While transparency of black box AI systems is an important aim for ethical AI, efforts to date are often one-size-fits-all. Our position is that more attention should be paid towards offering explanations that are context-specific, and our model takes an important step forward towards achieving that aim.
SnR

Code snippets are prevalent on websites such as Stack Overflow and are effective in demonstrating API usages concisely. However they are usually difficult to be used directly because most code snippets not only are syntactically incomplete but also lack dependency information, and thus do not compile. For example, Java snippets usually do not have import statements or required library names; only 6.88% of Java snippets on Stack Overflow include import statements necessary for compilation.
Neural Statistics for Click-Through Rate Prediction

With the success of deep learning, click-through rate (CTR) predictions are transitioning from shallow approaches to deep architectures. Current deep CTR prediction usually follows the Embedding & MLP paradigm, where the model embeds categorical features into latent semantic space. This paper introduces a novel embedding technique called neural statistics that instead learns explicit semantics of categorical features by incorporating feature engineering as an innate prior into the deep architecture in an end-to-end manner. Besides, since the statistical information changes over time, we study how to adapt to the distribution shift in the MLP module efficiently. Offline experiments on two public datasets validate the effectiveness of neural statistics against state-of-the-art models. We also apply it to a large-scale recommender system via online A/B tests, where the user's satisfaction is significantly improved.
A Secure and Efficient Blockchain-Based Data Sharing Scheme for Location Data

Location data containing trajectory information is of great value, but its unrestricted sharing also brings problems such as data abuse, privacy threat and lack of audit evidence. Especially when it comes to researchers or governments to collect location data to stem the spread of COVID-19, the authorization of the data owner is often missing. In this paper, we propose a data sharing scheme for location data based on a high-performance consortium blockchain combined with proxy re-encryption, which can guarantee location data sharing under user authorization while ensure the efficiency and security in the process of data sharing. The performance analysis and simulation results illustrate that the proposed scheme can meet the performance requirements in practical scenarios, guarantee the privacy of personal location data while making the data useful for stemming the spread of COVID-19.
Numerical Analysis and Scientific Calculation Considering the Management Mechanism of College Students’ Innovation and Entrepreneurship Education

Purpose. This article aims to study how to analyze and study the numerical value of education management mechanisms based on cloud computing and describe the innovation and entrepreneurship of college students. Methodology. This article addresses the problems of numerical analysis and scientific computing. This problem is based on cloud computing, so it elaborates on the concepts and related algorithms of cloud computing and big data and designs and analyzes cases of numerical analysis and scientific computing of educational management mechanisms. Research Findings. Through the research of different kernel functions, the IG_CDmRMR algorithm can obtain relatively high accuracy results for numerical analysis and scientific computing. The IG_CDmRMR algorithm is the closest to expert evaluation. The maximum difference is 0.002, which is consistent in sample three. The maximum difference of the IG algorithm is 0.005, and the minimum difference is 0.002. The evaluation effect of the IG_CDmRMR algorithm is closer to the evaluation effect of experts. Practical Implications. It analyzes the numerical value of the education management mechanism and finds that the accuracy has a certain height. This has certain evaluation significance for the management mechanism of college students’ innovation and entrepreneurship education.
A Hybrid Approach for Task Scheduling Based Particle Swarm and Chaotic Strategies in Cloud Computing Environment

This paper presents a hybrid approach based discrete Particle Swarm Optimization (PSO) and chaotic strategies for solving multi-objective task scheduling problem in cloud computing. The main purpose is to allocate the summited tasks to the available resources in the cloud environment with minimum makespan (i.e. schedule length) and processing cost while maximizing resource utilization without violating Service Level Agreement (SLA) among users and cloud providers. The main challenges faced by Particle Swarm Optimization (PSO) when used to solve scheduling problems are premature convergence and trapping into local optimum. This paper presents an enhanced Particle Swarm Optimization algorithm hybridized with Chaotic Map strategies. The proposed approach is called Enhanced Particle Swarm Optimization based Chaotic Strategies (EPSOCHO) algorithm. Our proposed approach suggests two Chaotic Map strategies: sinusoidal iterator and Lorenz attractor to enhanced PSO algorithm in order to get good convergence and diversity for optimizing the task scheduling in cloud computing. The proposed approach is simulated and implemented in Cloudsim simulator. The performance of the proposed approach is compared with the standard PSO algorithm, the improved PSO algorithm with Longest job to fastest processor (LJFP-PSO), and the improved PSO algorithm with minimum completion time (MCT-PSO) using different sizes of tasks and various benchmark datasets. The results clearly demonstrate the efficiency of the proposed approach in terms of makespan, processing cost and resources utilization.
Explaining User Models with Different Levels of Detail for Transparent Recommendation: A User Study

In this paper, we shed light on explaining user models for transparent recommendation while considering user personal characteristics. To this end, we developed a transparent Recommendation and Interest Modeling Application (RIMA) that provides interactive, layered explanations of the user model with three levels of detail (basic, intermediate, advanced) to meet the demands of different types of end-users. We conducted a within-subject study (N=31) to investigate the relationship between personal characteristics and the explanation level of detail, and the effects of these two variables on the perception of the explainable recommender system with regard to different explanation goals. Based on the study results, we provided some suggestions to support the effective design of user model explanations for transparent recommendation.