Reviewerly/exHarmony huggingface dataset:
Two-stage Interest Calibration Network for Reranking Hotels

As one key task of hotel search, interest modelling is to capture users' interests over their historical clicks. To this end, it is important yet challenging to understand the searching intention behind each click (interest focus). Besides, the query input by a user represents the user's interests to some extent. The differences between historical queries and the current query result in the gap between historical interests and the current interests (interest gap). Because historical clicks stem from historical interests, using them to model the current interests inevitably suffers from the interest gap challenge. To capture the interest focus and address the interest gap, we propose the two-stage interest calibration network (TCN), i.e., search-internal and search-external. In the search-internal calibration, we propose new insights of using the divergences among clicks and unclicks to model interest focus, and then develop a divergence-based calibration network. In the search-external calibration, inspired by the smoothing techniques for language models, we propose the interest smoothing principle to bridge interest gap: the interests learnt from historical clicks $$+$$ smoothing factor $$\approx $$ current interests. To implement this principle, we develop an interest smoothing network by reusing the query data. In the network, the interest domination unit is developed to learn user's interests from historical clicks, and the interest smoothing unit is developed to construct the smoothing factor. Extensive offline experiments and online A/B testing are performed and show that TCN significantly outperforms the state-of-the-art baselines. Besides, our model has been deployed in a hotel e-commerce platform and brought $$2.90\%$$ CTCVR and $$1.53\%$$ CTR lifts.
Enlarge the Hidden Distance: A More Distinctive Embedding to Tell Apart Unknowns for Few-Shot Learning

Most few-shot classifiers assume consistency of the training and testing distributions. However, in many practical applications, the two distributions are often different. In this paper, we focus on the few-shot open-set recognition problem which allows that the testing categories are different from the training categories. To alleviate this problem, we take the semantic adhesion scenario as an example to analyze the influence of sample embedding vectors on the identification indicator value. Then, we propose an Extra Embedding Classification Model with an adjustment module that is trained with optional contrastive loss functions to learn distinctive features of samples in the same category. This model can enlarge the hidden distances among samples while keeping the category information. We comprehensively verified the effectiveness of our model on both the normal and the semantic adhesion scenario of the few-shot open-set recognition problem.
Meta Pseudo Labels for Anomaly Detection via Partially Observed Anomalies

General anomaly detection based on weakly supervised or partially observed anomalies has been an important research. However, most such algorithms treat the unlabeled set as a substitute for normal samples and ignore the potential anomalies in it, which fails make full use of the abnormal supervision information. To address this issue, we propose a meta-pseudo-label based framework for anomaly detection (MPAD). The framework strives to obtain effective pseudo anomalies from the unlabeled samples to supplement the observed anomaly set. Specifically, a teacher network is improved based on the feedback of a student network on a validation set, thereby generating more conducive pseudo anomalies to assist the student network while incurring less confirmation bias. Extensive experiments show that the proposed MPAD algorithm outperforms current popular algorithms on five real datasets.
Storyline Generation from News Articles Based on Approximate Personalized Propagation of Neural Predictions

Generating a storyline is aiming to discover the evolution of events from news websites. Some existing approaches aim to automatically cluster news articles into events and connect related events in growing trees to generate storylines. Unfortunately, these methods did not perform well in learning the implicit associations of events. More recently, Graph Convolutional Network (GCN) based methods are proposed to learn the implicit associations between events. However, since the event representation in GCN model tends to be consistent after multi-layer propagation, the events cannot be correctly distinguished, which is not conducive to comprehensively learning the implicit associations between different events. In this paper, we propose an effective storyline generation method for news articles. Firstly, a novel model is presented based on Approximate Personalized Propagation of Neural Predictions for Story Branch Construction Model, called SBCM, which preserves local features and can better learn the implicit association between different events. Then, we utilize a statistical method to identify transition events in news articles, and connect story branches with transition events through temporal relationships to finally generate storylines. The experimental results on two real-world Chinese news datasets show that our proposal outperforms several state-of-the-art methods.
Information Flow Verification

Information flow tracking (IFT) models the movement of data which enables verification of security properties related to integrity and confidentiality. This chapter introduces the basics of hardware information flow analysis and illustrates its use for hardware security verification. The chapter starts by describing information flow models and properties. Then it highlights how information flow analysis is critical to hardware security verification. After that, there is a discussion regarding the differences between trace properties, which are commonly used in hardware functional verification, and information flow properties. This is followed by a description of different information flow verification techniques. The chapter concludes with two case studies demonstrating security verification using information flow properties.
Ark Filter: A General and Space-Efficient Sketch for Network Flow Analysis

Sketches are widely deployed to represent network flows to support complex flow analysis. Typical sketches usually employ hash functions to map elements into a hash table or bit array. Such sketches still suffer from potential weaknesses upon throughput, flexibility, and functionality. To this end, we propose Ark filter, a novel sketch that stores the element information with either of two candidate buckets indexed by the quotient or remainder between the fingerprint and filter length. In this way, no further hash calculations are required for future queries or reallocations. We further extend the Ark filter to enable capacity elasticity and more functionalities (such as frequency estimation and top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula> query). Comprehensive experiments demonstrate that, compared with Cuckoo filter, Ark filter has <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.08\times$</tex-math> </inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.34\times$</tex-math> </inline-formula> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.68\times$</tex-math> </inline-formula> throughput of deletion, insertion, and hybrid query, respectively; compared with Quotient filter, Ark filter has <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.55\times$</tex-math> </inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.74\times$</tex-math> </inline-formula> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$22.12\times$</tex-math> </inline-formula> throughput of deletion, insertion, and hybrid query, respectively; compared with Bloom filter, Ark filter has <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.55\times$</tex-math> </inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.11\times$</tex-math> </inline-formula> throughput of insertion and hybrid query, respectively.
Trace element determination result for the USGS standard rocks BCR2, BIR1a and GSP2 during 2015-2019

Trace element concentrations of the US Geological Survey's basalt standards (BCR2, BIR1a) and a granite standard (GSP2) were determined during five years from 2015 to 2019. These data will be helpful for judging data quality of trace element concentrations determined for the geochemical interpretation at KIGAM trace-isotope team. In addition, our data also include measurements of some elements whose recommendation values are not available in the US Geological Survey report.
Is the ocean science data repository, JOISS able to be FAIR?

As the global open science movement has recently proven its effectiveness in responding to the corona pandemic, research on disciplinary or institutional data repositories and establishing service platforms for the open and sharing research data are also active in Korea. The purpose of the research data repository is not to manage data per se but to discover and innovate knowledge and to integrate and reuse subsequent data and knowledge. Therefore, recent repository-related studies emphasize implementing the FAIR principle in this collaborative process, from observation to data documentation, data combination, quality control, and data publication. In particular, high-level data interoperability through the FAIR implementation of the repository is essential for ocean observation that requires multidisciplinary collaborative research. In Korea, ocean observatory organizations have repositories, including the ocean science data repository, JOISS; however, no studies evaluate the establishment and operation of data repositories in the FAIR principle. Therefore, this study aims to examine the construction process and data management status of the JOISS repository and the main functions and services of the web platform in terms of the data lifecycle and evaluate The FAIR principle of Open Science works in such an operating system and its limitations. The study provides implications for the improvement direction of data management and services of domestic marine repositories, including the JOISS, in an environment where the diversity and volume of data are rapidly increasing along with the evolution of ocean observation.
Content Moderation Regulation as Legal Role-Scripting

Lawmakers and scholars concerned with content moderation regulation typically appeal to “analogies” to justify or undermine different forms of regulation. The logic goes, law should afford individuals due process rights against speech platforms because speech platforms are “like” speech governors as a matter of objective reality. Other common analogies include common carriers, publishers, distributors, shopping malls, and book stores. Commentators attempt to invoke social roles to understand what the content moderation relationship is, what behaviors are “right” and “wrong” within it, and how law should police behavioral deviations. But they do so without relying on foundational sociology theory that explains what social roles are, what they do, and how they come to be. Without this theoretical foundation, the discourse incompletely portrays the project of content moderation regulation. Content moderation regulations do not simply “take” speech platforms’ role as it currently exists. They will also “make” speech platforms’ role, by expressing that speech platforms should be speech governors, common carriers, publishers, or something else, based on how lawmakers choose to regulate. This Article is the first to introduce role theory into the content moderation discourse. Content moderation regulations are poised to define the basic contours of what it means to be a “speech platform” because the role remains unsettled. Earlier, the Communications Decency Act failed to articulate coherent roles within the content moderation relationship. But current content moderation regulatory reforms — including the PACT Act in Congress, state platform-common carriage laws, and the Supreme Court’s decision in Gonzalez v. Google — have a renewed opportunity to script social roles for speech platforms and individuals. Foregrounding these reforms’ role scripts directs attention to urgent questions about whether they are likely to produce a desirable content moderation relationship and an online speech ecosystem that meets the public’s needs.
Archimedes Spiral-based Coverage Path for AUVs with Range-only Beacon Localization

The lawnmower is a widely used path for autonomous vehicles in coverage planning missions. However, when used in conjunction with single beacon-based navigation, the straight line excursions in the lawnmower path can result in higher uncertainty in the vehicle's position estimate. In this work, we investigate the Archimedes spiral as an alternative coverage path in single beacon navigation. With a unicycle model for the vehicle, we show that the proposed path preserves the observability of the system using tools from Lie algebra. We further compare the proposed path against the lawnmower in simulation using an extended Kalman filter for state estimation and show that the proposed solution has better error performance.
Right-of-Way-based Acceleration Velocity Obstacle

A novel second -order online local reactive motion planner, named ‘Right-of-Way-based Acceleration Velocity Obstacle’ (R-AVO), which also incorporates right-of-way rules established by regulatory authorities, is developed in this paper. Following the developed algorithm, each unmanned vehicle (a.k.a. agent) in a multi-agent, dynamic environment can generate a collision-free, dynamically feasible, and goal-oriented trajectory. Moreover, the considered right-of-way rules enable an implicit coordination between the agents and aid in preventing any reciprocal oscillation in the planned trajectories of the agents. Extensive simulation studies are performed to illustrate the performance of the proposed algorithm in terms of relevant measures of effectiveness, which also indicate real-time implementability of the developed motion planning algorithm.
A State-Space Perspective on the Expedited Gradient Methods: Nadam, RAdam, and Rescaled Gradient Flow

Fast gradient-descent algorithms are the default practice in training complex machine learning models. This paper presents the convergence guarantee of two existing adaptive gradient algorithms, Nadam and RAdam, for the first time, and the rescaled gradient flow in solving non-convex optimization. The analyses of all three algorithms are unified by a common underlying proof sketch, relying upon Barbalat's lemma. The utility of another tool from classical control, the transfer function, hitherto used to propose a new variant of the famous Adam optimizer, is extended in this paper for developing an improved variant of the Nadam algorithm. Our experimental results validate the efficiency of this proposed algorithm for solving benchmark machine learning problems in a shorter time and with enhanced accuracy.
Named Entity Recognition of Diseases and Pests with Small Samples Based on Space Mapping

In the field of agricultural pests and diseases, there is a lack of annotated data and standard word formations, so standard Named entity recognition (NER) technology is not effective. Aiming at these problems, this paper proposes an NER method based on spatial mapping model and has achieved significant gains. First, the Chinese corpus of pests and diseases is constructed. Then, the label word set is obtained by the label words search algorithm, and the mapping function is used to establish the mapping relationship between label words and labels. Finally, the NER task is reconstructed into a language model task, and a pretrained language model RoBERTa, is used to predict, based on spatial mapping, the named entity of pests and diseases. A Focal Loss function is introduced, alleviating the problem of uneven distribution of difficult and easy training samples and improving the recognition ability of the model for difficult-to-identify samples. The experimental results on the pest and disease corpus show that the recognition performance of the proposed method is significantly improved compared with other models under the condition of small samples (testing at 50-shot, i.e., only 50 samples for each type of entity label): the F1-Score reaches 68.66%, and the precision measure reaches 71.88%.
Deep Reinforcement Learning for Two-Player DouDizhu

Recently, DouZero, an AI system for the game of DouDizhu, has been proposed and made a breakthrough by reaching human level in DouDizhu, one of the most popular imperfect information games in China. In order to verify the effectiveness and versatility of this system, and further compare the performance of Deep Monte-Carlo (DMC) and Deep Q-Network (DQN) algorithms, in this paper we implement and improve DouZero system on two-player DouDizhu, a variant of the classic DouDizhu, where there is no cooperation between the players yet with more hidden information. We first revise DouZero system on this game. We then design filter network based on supervised learning to improve the quality of training data and thus accelerate the training process. To improve the learning efficiency, we also extract the state and action features according to the characteristics of the two-player DouDizhu. In addition, a variety of reward functions are especially designed in terms of this game. The experimental results show that in twoplayer DouDizhu with those improvements both DMC and DQN make satisfactory performances, but different from the existing work, DQN achieves a better performance than DMC.
Automatic Evaluation System of Aerobics based on Action Recognition Algorithm

This paper analyzes the AES of aerobics based on action recognition algorithm(ARA). This paper analyzes the difficult problem of human motion recognition, and on this basis, discusses the ARA and the automatic evaluation system(AES) of aerobics. The ARA is applied to the design of the AES of aerobics. Finally, through the visualization experiment of the key posture extraction of the movement sequence, the AES of aerobics based on the ARA in this paper is compared with the traditional evaluation system. The test results show that, compared with the traditional evaluation system, The automatic recognition system in this paper has improved the recognition accuracy of each kind of aerobics action(AA), which proves the effectiveness of the AES based on ARA discussed in this paper to improve the recognition accuracy.
Research on Cultivating Craftsman Spirit of College Students in the New Era Based on Deep Reinforcement Learning Technology

Through the analysis of the development direction of college students' craftsman spirit network in the new era, the supervision and management of college students ‘ craftsman spirit in the new era are carried out, and the research on cultivating college students' craftsman spirit in the new era based on deep reinforcement learning technology is put forward. The statistical sequence model of artisan spirit cultivation is constructed, the direction of artisan spirit cultivation is evaluated, descriptive statistical features are extracted, and the extracted correlation feature sets of big data of artisan spirit cultivation distribution of college students in the new era are classified and fused, and the optimization control of data clustering center is carried out to realize the cultivation of artisan spirit of college students in the new era. The empirical analysis results show that the model can effectively evaluate the direction and reliability of cultivating college students' artisan spirit in the new era, and the big data fusion performance is good. The social atmosphere of excellence is improved by cultivating college students' artisan spirit in the new era. Combined with the background of the times, according to the needs of social development and enterprise talents, the ingenious quality of loving one's job, integrating knowledge with practice and striving for excellence is cultivated, and the all-round development of students is constantly promoted.
Research on Classification Method of Bank Statement in Low Resource and Cross-domain Scenarios

Bank statement classification is of great value in the field of risk control and marketing in the financial industry, but the lack of annotated samples, cumbersome remark information, and large differences among different industries pose great challenges to model training. In this paper, based on the idea of Prompt-based Learning, we propose PDT: Prompt-based Data-to-text Template, which converts structure data into text containing background information, with the purpose of further releasing the capability of the pre-trained model. The experimental results show that the method can effectively solve the model training problem in low-resource and cross-domain scenario, and the model shows better performance under both Bidirectional Language Model (LM) method and Seq-to-Seq LM method compared with the original input.
A Lightweight Federated Learning Framework in a One-way Active Communication Environment

In the medical field, patient data is protected by privacy and cannot be made public. Meanwhile, the medical institution is in the local area network, and the outside world cannot directly access it. In this case, data is not available, nor can a centralized machine learning approach be used to benefit from the data. Federated learning is a distributed machine learning method that solves the problem that data cannot be centralized for training like traditional machine learning because data is scattered and limited in access. This paper proposes a federated learning framework that enables decentralized machine learning. In the framework, we use a one-way active communication method that is not adopted by the current mainstream federated learning frameworks. The framework only allows clients to actively access the server, and multiple clients access the server asynchronously. The design of this communication method ensures the data security of the client in the framework. The framework proposed in this paper is flexible and lightweight. It does not depend on complex configuration and environment, and has strong scalability. Finally, the proposed framework is validated on federated learning tasks, proving the usability and effectiveness of the framework.