Reviewerly/exHarmony huggingface dataset:
Atomic cross-chain swaps with improved space, time and local time complexities

An effective atomic cross-chain swap protocol is introduced by Herlihy [Herlihy, 2018] as a distributed coordination protocol in order to exchange assets across multiple blockchains among multiple untrusted parties. The atomic cross-chain swap protocol guarantees; (1) if all parties conform to the protocol, then all assets are exchanged among the parties, (2) even if some parties or coalitions of parties deviate from the protocol, no party conforming to the protocol suffers a loss, and (3) no coalition has an incentive to deviate from the protocol. Herlihy [Herlihy, 2018] invented this protocol by using hashed timelock contracts. A cross-chain swap is modeled as a directed graph D=(V,A). Vertex set V denotes a set of parties and arc set A denotes a set of proposed asset transfers. Let Δ be time enough for a party to publish a contract on any of the blockchains, or to change the state of a contract and for the other party to detect the change. The space complexity of the protocol is O(|A|2). The local time complexity of the protocol is O(|V|⋅|L|), where L is a feedback vertex set computed by the protocol. We propose a new atomic cross-chain swap protocol which uses only signature information and improves the space complexity to O(|A|⋅|V|) and the local time complexity to O(|V|). Moreover, if all parties conform to our protocol, our proposed protocol improves the time complexity from O(|V|Δ) to O(Δ).
Analysis of teachers' perceptions of Seoul education policy: Based on integrated approach with IPA and FGI

본 연구는 서울특별시교육청이 추진해 온 교육정책을 중심으로 정책의 중요도와 실행도에 관한 교사의 인식을 조사하여 정책적 개입이 필요한 정책과제를 식별하고 해당 정책과제를 추진하는 과정에서 나타난 한계점과 이를 개선하기 위한 방안을 탐구하는 데 목적이 있다. 이를 위해 본 연구는 IPA 분석 결과를 정교화하는 과정에서 FGI를 활용하는 혼합연구방법의 순차적 접근 방식을 활용하였다. IPA 분석을 위해『서울학생종단연구 2020』1차년도의 교사 설문자료를 활용하였고, 서울특별시교육청에서 근무하는 교사 및 교육전문직 13명을 대상으로 FGI를 진행하였다. IPA 분석을 통해 서울교육정책의 중요도와 실행도를 조사한 결과, 현장 지원 강화 교육행정, 학교공간 혁신 등은 모든 학교급에서 일관되게 정책개선이 요구되는 집중개선 과제인 것으로 나타났다. 이 중 현장 지원 강화 교육행정은 코로나19 이후 교육청의 각종 사업이 늘어나고 일과 삶의 균형을 선호하는 학교 문화가 확산됨에 따라 현장의 요구가 높게 나타났다. 이와 관련해서 학교의 자율적 판단으로 업무를 조정할 수 있는 학교자치를 강화하고 학교통합지원센터의 지원 역량을 내실화하는 등의 정책적 노력이 필요한 것으로 나타났다. 또한 학교공간 혁신의 경우 노후화된 학교 공간을 보수 및 개선해야 한다는 현장의 요구가 컸기 때문에 나타난 결과였는데, 향후 융합수업, 디지털 교육 등 미래교육과 고교학점제와 같은 정책 사업을 고려해서 학교 공간을 전반적으로 바꾸기 위한 노력이 필요한 것으로 나타났다.The purpose of this study is to investigates teachers' perceptions of Seoul education policy, to identify policy tasks that require policy intervention, and to explore improvemental approaches of identified policy tasks. To this end, this study utilized a sequential approach of a mixed research method using FGI in the process of elaborating the IPA analysis results. For the IPA analysis, the teacher survey data from the first year of 『Seoul Education Longitudinal Study 2020』 was used, and FGI was conducted with 13 teachers and educational professionals. As a result of IPA analysis, we found that education administration to strengthen field support and school space innovation are intensive improvement tasks at all school levels. As for education administration to strengthen field support, strengthening school autonomy which allows schools to adjust work at their own discretion, andstrengthening the support capacity of the School Integrated Support Center are required. In addition, in the case of school space innovation, the result was due to the demand from the field to repair and improve the deteriorated school space. It turns out that it takes effort to change school space for future education, digital education, and high school credit system.
The Impact of Augmented Reality on Fashion and Textile Design Education

Technological experiences have been in continuous motion to decades.because of the powerful ideas and projects, these experiences produced Augmented reality is a live, which is an interaction paradigm that aims to combine computer-generated information with the real world This advanced technology, which can be employed on multiple devices such as interactive screens and smart phones become very useful in many fields like fashion, accessories ,footwear ,furniture and finally in education which aimed this research to prove the direct impact of augmented reality in the educational process by using questionnaire method for both students and teachers to reveal the output results from augmented reality in the educational process.The findings reveals that Augmented Reality (AR) in education can empower teachers by providing highly stimulating digital content and features that can engage learners in no time.This research aims to know the impact of augmented reality engagement in learning by students and their designs further more Augmented Reality in education has been proven to improve teacher-student collaboration in classes.this research try to contribute in the field of textile design research and facilitate the implementation designs before printing and applied .
An Image Enhancement Method for the Vision Surveillance of Bridge Cable Anchorage Zone

Vision surveillance is an effective way to detect the water accumulation and corrosion in bridge cable anchorage zone. Duo to the closed dark environment and the high reflectivity of metal surfaces in the zone, the acquired images are partially too bright or too dark. It is adverse for vision surveillance. To solve this problem, we propose to capture multi-exposure images to improve the image quality of the anchorage zone by image fusion. Seven multi-exposure image fusion algorithms are tested in the experiments. Both subjective evaluation and objective evaluation show that all the fusion algorithms can effectively improve the image quality of the anchorage zone. Among the seven algorithms, MEF-OSSI is superior to the others, and it is recommended in the engineering application.
Algorithmic Dissuasion: De-ranking Possible Copyright Infringing Content into Relative Oblivion

This article focuses on platforms operating in the algorithmic twilight zone between presenting search results and blocking them. To avoid risks or to optimize at the copyright holders’ request, platforms directly or in a time-phased way de-rank unauthorized but possibly legal content into relative oblivion. This midway manipulation of traffic to suspected content is opaque to the uploader of content, and the general user, lacks any redress mechanism, and possibly chills the freedom to share transformed content that includes copyrighted works. One can argue that the fundamental rights preempt license conditions which are incompatible with it.
Representation Learning on Dynamic Network of Networks

Network of networks (NoN) where each node of the main network represents a domain-specific network, is a powerful multi-network model to capture the relationships among entities at both coarse and fine granularities. Existing graph convolutional networks (GCN) learn node representations either on a single network or multiple networks while overlooking the relationships among different networks (e.g., main network structure). In addition, many real-world networks often evolve over time, which makes it imperative yet even more challenging to leverage temporal information for node representation learning. In this paper, we study the node representation learning problem on dynamic network of networks. The key idea of designing the static model is the predict-then-propagate strategy such that node representations are obtained by propagating the initial representations of common nodes which are shared across domain-specific networks. To leverage the temporal information underlying dynamic NoN, we extend the static model by a gated recurrent unit (GRU) to capture the dynamics behind cross-network consistency and a self-attention mechanism to learn the dependence of nodes on their historical representations. With these components, we propose an end-to-end model DraNoN to learn node representations on dynamic NoN . We conduct experiments on the dynamic network alignment task, which demonstrate the superior performance of DraNoN compared with the state-of-the-arts.
Embedding Transfer with Enhanced Correlation Modeling for Cross-Domain Recommendation

Modern internet platforms usually have different scenarios to provide rich recommendation services to meet the diverse demands of users. Cross-domain recommendation (CDR) and multi-domain recommendation (MDR) methods are widely used in such platforms to leverage rich auxiliary information from multiple domains. However, state-of-the-art CDR and MDR methods usually enforce some correlations between source and target embeddings on each user, ignoring the correlations between users in both domains. To address this problem, we adopt a relaxed contrastive loss, that employs the pairwise similarities in the source domain as relaxed labels, enforcing such inter-sample relations are reserved in a weighted manner in the target domain. The basic assumption behind such a design is that users with similar interests should be with similar interacted items in a rec- ommender system, and this work takes a step further to realize and specify such similarity modeling as collaborative signals encoded in both implicit embedding spaces. We validate the effectiveness of the proposed method on a large- scale public dataset and a real production dataset with over 700 million samples. We further experimentally show that the proposed embedding transfer method is generic, and can be plugged into any existing deep neural networks, such as YoutubeDNN and BERT4Rec. Currently, the proposed embedding transfer techniques have been successfully deployed in the Guess You Like in WeTV for the CDR/MDR task.
CISum: Learning Cross-modality Interaction to Enhance Multimodal Semantic Coverage for Multimodal Summarization

Multimodal summarization (MS) aims to generate a summary from multimodal input. Previous works mainly focus on textual semantic coverage metrics such as ROUGE, which considers the visual content as supplemental data. Therefore, the summary is ineffective to cover the semantics of different modalities. This paper proposes a multi-task cross-modality learning framework (CISum) to improve multimodal semantic coverage by learning the cross-modality interaction in the multimodal article. To obtain the visual semantics, we translate images into visual descriptions based on the correlation with text content. Then, the visual description and text content are fused to generate the textual summary to capture the semantics of the multimodal content, and the most relevant image is selected as the visual summary. Furthermore, we design an automatic multimodal semantics coverage metric to evaluate the performance. Experimental results show that CISum outperforms baselines in multimodal semantics coverage metrics while maintaining the excellent performance of ROUGE and BLEU.KeywordsSummarizationMulitmodalSemantic coverageMulti-task
Hierarchical Neural Topic Model with Embedding Cluster and Neural Variational Inference

Compared to flat topic models, hierarchical topic models not only exploit inherent structural information in the corpus but detect better semantic topics with the help of hierarchy knowledge. Recently, Neural-Variational-Inference (NVI) based hierarchical neural topic models have achieved better performance. However, existing NVI-based models learn topics of different levels with the same strategy, i.e., word co-occurrence patterns, which causes that topics of different levels cannot be distinguished from a semantic perspective and topics of the first level degenerate into some meaningless common words. To address the above problems, we propose a novel Hierarchical Neural Topic Model with embedding cluster and neural variational inference (C-HNTM). Specifically, C-HNTM adopts Gaussian Mixture Model (GMM) to learn topics of the first level based on word embeddings, which can capture the global semantic information of the whole corpus and generate more meaningful and global semantic topics. Then, the NVI-based method is adopted to learn topics of the second level with Bag-of-Word from a document perspective, which can generate local and more detailed topics. Third, we simultaneously learn global and local topic distributions and dependency matrix by using Stochastic Gradient Variational Bayes (SGVB) estimator. Finally, we provide the detailed inference of variational lower bound and extensive experiments on three real-world datasets to validate the effectiveness of our model.KeywordsNerual Topic ModelingWord Embeddings ClusteringHierarchical structureNeural Variational Inference
Prompt-Based Learning for Aspect-Level Sentiment Classification

The goal of Aspect-level Sentiment Classification (ASC) is to identify the sentiment polarity towards a specific aspect of a given sentence. Mainstream methods design complicated models and require a large scale of annotated training samples, and even perform finetuning based on pre-trained language models. Therefore, those supervised methods may be impractical in real-world scenarios due to the limited availability of labeled training corpora, a.k.a. low-resource settings. To this end, we propose an aspect-specific prompt learning approach (AS-Prompt) that fully utilizes the pre-trained knowledge and aspect-related information to deal with ASC tasks, enabling pre-trained models with huge parameters to achieve considerable results under the few-shot settings. Specifically, we transfer the sentiment classification task into Masked Language Modeling (MLM) by designing appropriate prompts and searching for the ideal expression of prompts in continuous space. Meanwhile, we integrate the prompts into the input sentence, thus adapting the model to the classification task under the guidance of sentiment labels. Experimental results on SemEval-2014 Task 4 show our proposed method achieves noticeably improvement compared with the original BERT models and discrete prompt methods. In addition, we test the performance of the model’s transfer on different datasets and demonstrate the superiority of prompt learning when adapting to a new domain, especially under a low-resource setting.
SCIEnt: A Semantic-Feature-Based Framework for Core Information Extraction from Web Pages

Core Information Extraction (CIE) from web pages aims to extract valuable text to provide data for downstream Text Data Mining (TDM) tasks. Web page representations in existing CIE methods are either based on HTML structural features or visual features. Neither of these representations really understands the semantic associations inherent in the web page, leading to poor extraction quality. This paper proposes a new web page representation method based on semantic features from the perspective of readers’ reading and understanding of web pages. In this method, we introduce a new concept of web page skeleton to parse and represent the web page from a semantic point of view. To observe the relationship between the various parts of the skeleton, we project the skeleton onto the DOM tree and get the skeleton tree. Based on this new web page representation, we propose SCIEnt, a semantic-feature-based web page CIE framework. SCIEnt consists of four modules, i.e. Skeleton Tree Construction, Node Splitting, Node Classification, Semantic Aggregation and Correction. Algorithms in each module can be flexibly replaced according to the requirement of downstream TDM task. We evaluate SCIEnt in terms of three well-studied datasets. Results show that SCIEnt far outperforms baseline methods, and the semantic-feature-based web page representation have superiority in web page CIE.
Medical Visual Question Answering via Targeted Choice Contrast and Multimodal Entity Matching

Although current methods have advanced the development of medical visual question answering (Med-VQA) task, two aspects remain to be improved, namely extracting high-level medical visual features from small-scale data and exploiting external knowledge. To strengt-hen the performance of Med-VQA, we propose a pre-training model called Targeted Choice Contrast (TCC) and a Multimodal Entity Matc-hing (MEM) module, and integrate them into an end-to-end framework. Specifically, the TCC model extracts deep visual features on the small-scale medical dataset by contrastive learning. It improves model robustness by a targeted selection of negative samples. The MEM module is dedicated to embedding knowledge representation into the framework more accurately. Besides, we apply a mixup strategy for data augmentation during the framework training process to make full use of the small-scale images. Experimental results demonstrate our framework outperforms state-of-the-art methods.
Graph-Based Contextual Attention Network for Single Image Deraining

Rain streaks degrade the images and badly affect the outdoor vison tasks, and deep learning based single image deraining approach has witnessed the continuously growing and achieved great success. However, traditional convolution operation which uses a slide window can only extract the local feature patch, most of them ignore the correlation among the features for image deraining. We propose a graph-based contextual attention network for single image deraining. Firstly, we project the input feature map to a latent global context representation, and then the global information flow to spatial graph attention and channel graph attention, respectively. After the two graph attention, a grouped linear layer is adopted to mine the correlation among the information and enhance the global contextual information. The experiments on several datasets demonstrate that our methods achieve better results than the previous state-of-art methods.
Inaugural Editorial. Can We Achieve Our Mission: Fast, Accessible, Cutting-edge, and Top-quality?

We are pleased to launch the first issue of the New England Journal of Statistics in Data Science (NEJSDS). NEJSDS is the official journal of the New England Statistical Society (NESS) under the leadership of Vice President for Journal and Publication and sponsored by the College of Liberal Arts and Sciences, University of Connecticut. The aims of the journal are to serve as an interface between statistics and other disciplines in data science, to encourage researchers to exchange innovative ideas, and to promote data science methods to the general scientific community. The journal publishes high quality original research, novel applications, and timely review articles in all aspects of data science, including all areas of statistical methodology, methods of machine learning, and artificial intelligence, novel algorithms, computational methods, data management and manipulation, applications of data science methods, among others. We encourage authors to submit collaborative work driven by real life problems posed by researchers, administrators, educators, or other stakeholders, and which require original and innovative solutions from data scientists.
Magnetic resonance image restoration via least absolute deviations measure with isotropic total variation constraint

&lt;abstract&gt;&lt;p&gt;This paper presents a magnetic resonance image deblurring and denoising model named the isotropic total variation regularized least absolute deviations measure (LADTV). More specifically, the least absolute deviations term is first adopted to measure the violation of the relation between the desired magnetic resonance image and the observed image, and to simultaneously suppress the noise that may corrupt the desired image. Then, in order to preserve the smoothness of the desired image, we introduce an isotropic total variation constraint, yielding the proposed restoration model LADTV. Finally, an alternating optimization algorithm is developed to solve the associated minimization problem. Comparative experiments on clinical data demonstrate the effectiveness of our approach to synchronously deblur and denoise magnetic resonance image.&lt;/p&gt;&lt;/abstract&gt;
FACE RECOGNITION ALGORITHM BASED ON OPTIMAL WEIGHTED MULTI-DIRECTIONAL LOG-GABOR FEATURES

In order to improve the performance of face recognition, a face recognition method based on an adaptive fusion of multi-directional and multi-scale Log-Gabor features is proposed. First, the multi-direction and multi-scale Log-Gabor transform is applied to face images. Then the Log-Gabor feature maps of different scales are fused to extract the Gist vectors of the fused feature maps. In addition, the mutual information algorithm is used adaptively to calculate the weights of Gist vectors. Finally, the adaptive weighted Gist vector is used as the feature vector of face images, and the K-nearest neighbor classification algorithm is used to realize face recognition. According to the face recognition experiments on WIDER FACE data set and MAFA occlusion face data set, the accuracy of the proposed method is presented. Compared with the classical face recognition algorithms, the effectiveness of the proposed method is verified.
A Lagrangian-based approach to learn distance metrics for clustering with minimal data transformation

Distance metric learning algorithms aim to learn how to measure similarities between data objects in a metric space. In the context of clustering, metric learning typically relies on side-information provided by experts, most commonly expressed in the form of pairwise constraints. In this setting, algorithms for metric learning execute data transformations that bring pairs of data points involved in must-link constraints close together, whereas pair of points involved in cannot-link constraints are moved away from each other. One caveat to such methods is that they can considerably change the original data distribution properties. With that in mind, we propose a Lagrangian-based approach to assist distance metric learning algorithms for clustering. Our method is developed to identify the least impactful transformations to the original data space, while still learning a more suitable metric space for grouping the data using the provided side information. Our results demonstrate that the proposed methodology is able to achieve a competitive clustering performance with respect to truth classification. Furthermore, the method is able to provide more accurate views of the transformed datasets, which can lead to more reliable clustering interpretations.Keywordsclusteringdistance metric learningpairwise constraintsLagrangian relaxationduality theory
Reinforcement Learning Guided Multi-Objective Exam Paper Generation

To reduce the repetitive and complex work of instructors, exam paper generation (EPG) technique has become a salient topic in the intelligent education field, which targets at generating high-quality exam paper automatically according to instructor-specified assessment criteria. The current advances utilize the ability of heuristic algorithms to optimize several well-known objective constraints, such as difficulty degree, number of questions, etc., for producing optimal solutions. However, in real scenarios, considering other equally relevant objectives (e.g., distribution of exam scores, skill coverage) is extremely important. Besides, how to develop an automatic multi-objective solution that finds an optimal subset of questions from a huge search space of large- sized question datasets and thus composes a high-quality exam paper is urgent but non-trivial. To this end, we skillfully design a reinforcement learning guided Multi-Objective Exam Paper Generation framework, termed MOEPG, to simultaneously optimize three exam domain-specific objectives including difficulty degree, distribution of exam scores, and skill coverage. Specifically, to accurately measure the skill proficiency of the examinee group, we first employ deep knowledge tracing to model the interaction information between examinees and response logs. We then design the flexible Exam Q-Network, a function approximator, which automatically selects the appropriate question to update the exam paper composition process. Later, MOEPG divides the decision space into multiple subspaces to better guide the updated direction of the exam paper. Through extensive experiments on two real-world datasets, we demonstrate that MOEPG is feasible in addressing the multiple dilemmas of exam paper generation scenario1.1https://github.com/researcher-tiger/MOEPG