Reviewerly/exHarmony huggingface dataset:
Tracing Text Provenance via Context-Aware Lexical Substitution

Text content created by humans or language models is often stolen or misused by adversaries. Tracing text provenance can help claim the ownership of text content or identify the malicious users who distribute misleading content like machine-generated fake news. There have been some attempts to achieve this, mainly based on watermarking techniques. Specifically, traditional text watermarking methods embed watermarks by slightly altering text format like line spacing and font, which, however, are fragile to cross-media transmissions like OCR. Considering this, natural language watermarking methods represent watermarks by replacing words in original sentences with synonyms from handcrafted lexical resources (e.g., WordNet), but they do not consider the substitution’s impact on the overall sentence's meaning. Recently, a transformer-based network was proposed to embed watermarks by modifying the unobtrusive words (e.g., function words), which also impair the sentence's logical and semantic coherence. Besides, one well-trained network fails on other different types of text content. To address the limitations mentioned above, we propose a natural language watermarking scheme based on context-aware lexical substitution (LS). Specifically, we employ BERT to suggest LS candidates by inferring the semantic relatedness between the candidates and the original sentence. Based on this, a selection strategy in terms of synchronicity and substitutability is further designed to test whether a word is exactly suitable for carrying the watermark signal. Extensive experiments demonstrate that, under both objective and subjective metrics, our watermarking scheme can well preserve the semantic integrity of original sentences and has a better transferability than existing methods. Besides, the proposed LS approach outperforms the state-of-the-art approach on the Stanford Word Substitution Benchmark.
The Impact of Augmented Reality-Based Argumentation Activities on Middle School Students’ Academic Achievement and Motivation in Science Classes

Science teaching is one of the subjects that has been actively affected by Augmented Reality (AR) technology worldwide. Although the use of AR in science courses is increasing, the effective use of AR still needs improvement. The purpose of this study was to investigate the effect of augmented reality-based argumentation activities in 7th grade students' academic achievement and motivation in teaching astronomy content. The quasi-experimental design was used in this study. The participants consisted of 79 seventh-grade students from three different science classes. The students in experimental group 1 (n=26) participated in an instruction that included both augmented reality and argumentation activities about astronomy. The students in experimental group 2 (n=27) participated in an astronomy instruction that includes only argumentation activities and the students in control group (n=27) received astronomy instruction through traditional methods. The data were collected through pre-and post- academic achievement test and the Motivated Strategies for Learning Questionnaire (MSLQ). ANOVA and Kruskal Wallis tests were used to determine the statistical differences between the pretest and posttest scores of the students. The results showed that augmented reality-based argumentation activities were more effective in increasing students' achievement and motivation than the argumentation and traditional instruction in teaching astronomy.
HieNet: Bidirectional Hierarchy Framework for Automated ICD Coding

International Classification of Diseases (ICD) is a set of classification codes for medical records. Automated ICD coding, which assigns unique International Classification of Diseases codes with each medical record, is widely used recently for its efficiency and error-prone avoidance. However, there are challenges that remain such as heterogeneity, label unbalance, and complex relationships between ICD codes. In this work, we proposed a novel Bidirectional Hierarchy Framework(HieNet) to address the challenges. Specifically, a personalized PageRank routine is developed to capture the co-relation of codes, a bidirectional hierarchy passage encoder to capture the codes’ hierarchical representations, and a progressive predicting method is then proposed to narrow down the semantic searching space of prediction. We validate our method on two widely used datasets. Experimental results on two authoritative public datasets demonstrate that our proposed method boosts the state-of-the-art performance by a large margin.
SLUG: Feature Selection Using Genetic Algorithms and Genetic Programming

We present SLUG, a method that uses genetic algorithms as a wrapper for genetic programming (GP), to perform feature selection while inducing models. This method is first tested on four regular binary classification datasets, and then on 10 synthetic datasets produced by GAMETES, a tool for embedding epistatic gene-gene interactions into noisy datasets. We compare the results of SLUG with the ones obtained by other GP-based methods that had already been used on the GAMETES problems, concluding that the proposed approach is very successful, particularly on the epistatic datasets. We discuss the merits and weaknesses of SLUG and its various parts, i.e. the wrapper and the learner, and we perform additional experiments, aimed at comparing SLUG with other state-of-the-art learners, like decision trees, random forests and extreme gradient boosting. Despite the fact that SLUG is not the most efficient method in terms of training time, it is confirmed as the most effective method in terms of accuracy.
Defining legal technology and its implications

Abstract Legal technological developments have been both lauded as the promising future of the law and derided as a danger to the fundamentals of justice. This article helps reconcile these divergent perspectives by providing a definition of legal technology and a framework through which to understand its different types and their potential implications for the legal system and society more generally. Mapping technologies according to how specifically they afford legal uses, and the directness with which they engage in unmediated legal activities reveals different technological categories and their differing propensities to have legal, functional or general implications. This framework can help inform discussions both about which types of legal technologies to be excited about, and which to be concerned about, while also helping guide research, policymaking, design and adoption considerations.
Blockchain ecosystems in the creative industries: big dreams for micro-payments

Blockchain technology has hyped up the creative industries, as seen from the rise of blockchain startups in the media and entertainment and arts that attract multiple collaborators to co-create value. Some business ventures want to disrupt the Hollywood studio model, while others aim to "cut the middleman." This chapter explores the value creation processes in blockchain ecosystems in the creative industries, relying on document analysis of blockchain ventures' strategic documents and interviews with CEOs and founders. It highlights new units of value used by blockchain entrepreneurs to attract a large pool of micro-investments. Building on the innovation ecosystem-based perspective, the chapter presents how blockchain startups define value propositions that aim to correct market imperfections and the bottlenecks that might hinder the value creation processes.
Research Status of the Application of Virtual Reality Technology on Self-efficacy

The origin and development of virtual reality technology have a history of decades. Since 2013, virtual reality technology Technology is becoming more and more popular. Virtual reality technology can improve individual negative emotions, reduce anxiety levels, improve individual self-efficacy, and ultimately improve people's quality of life. Therefore, this article aims to study the status of the application of virtual reality technology in self-efficacy. This article uses the literature research method to study the relevant literature, through the search of relevant materials such as doctoral and master's theses, journals, books, etc., to sort out and analyze the relevant materials in the study of virtual reality technology in self-efficacy, this article divides the research results into four categories. The first is the impact of virtual reality teaching methods on learners' self-efficacy, and the second is the impact of virtual reality technology on the self-efficacy of special populations. The third is the research on self-efficacy in the virtual reality technology environment, and finally, it extends to the impact of virtual reality simulation as a learning strategy on self-efficacy. We found that the teaching method of virtual reality technology is effective for learners' self-efficacy. Secondly, virtual reality technology will also have a positive impact on the self-efficacy of special populations, such as children with cerebral palsy, stroke patients, and medical service providers. In addition, some researchers have also studied the relationship between self-efficacy and other psychological variables in the virtual reality technology environment. Finally, the application of virtual reality simulation games in nursing education can also improve individual self-efficacy. This article theoretically standardizes the application of virtual reality technology in the field of self-efficacy. In practice, it has a good guiding role in the research of virtual reality technology in self-efficacy, and at the same time provides new ideas for improving individual self-efficacy.
Optimized Feature Selection Based on a Least-Redundant and Highest-Relevant Framework for a Solar Irradiance Forecasting Model

Exogenous and endogenous variables are typically evaluated several times during the selection trial of a predictive model for Global Horizontal Irradiance (GHI).This is accomplished using various statistical measures (e.g., univariate statistical analysis, correlation analysis, etc.) that are applied to gauge redundancy and relevancy in specific variables.The main benefits of these approaches include lower computational cost, fast screening times, accurate measuring of linear and monotonic degrees of variable pairs, and the removal of features with low relevance.However, they cannot identify instances where single or groups of predictor variables are non-monotonically associated with the response variable, nor can they discern whether variables are predictive in combination with other variables or in isolation.The present study attempts to overcome these challenges by first describing monotonic and non-monotonic (Spearman's rho and Hoeffding's D, respectively) correlation statistics in combined usage for locating groups with major non-monotonic endogenous variable changes.The proposed work's novelty is subset evaluation that determines relevance using Weather Recursive Feature Elimination (WRFE).This is a novel hybrid feature reduction method that optimizes feature selection using a Least-Redundant/Highest-Relevant framework.The proposed WRFE utilizes feature importance for measuring variance reduction in Random Forest Regression (RFR) and as data perturbation in Long Short-Term Memory (LSTM).The simulation results of GHI hourly predictions demonstrate that the proposed optimal features of the training subset make the greatest contributions to the prediction target, proving that the high variability of irradiance conditions lowers training subset reliability.The results showed that the proposed WRFE is superior compared to the other models with 1.0927 % for the RMSE and the R 2 coefficient is exceeding 98%.HMM Hidden Markov Model.K ref + Daily clearness index for the reference station.LR Linear regression.LSTM Long short-term memory.
Improving Conversational Recommendation Systems’ Quality with Context-Aware Item Meta-Information

A key challenge of Conversational Recommendation Systems (CRS) is to integrate the recommendation function and the dialog generation function smoothly.Previous works employ graph neural networks with external knowledge graphs (KG) to model individual recommendation items and integrate KGs with language models through attention mechanisms for response generation.Although previous approaches prove effective, there is still room for improvement.For example, KG-based approaches only rely on entity relations and bagof-words to recommend items and neglect the information in the conversational context.We propose to improve the usage of dialog context for both recommendation and response generation using an encoding architecture along with the self-attention mechanism of transformers.In this paper, we propose a simple yet effective architecture comprising a pre-trained language model (PLM) and an item metadata encoder to integrate the recommendation and the dialog generation better.The proposed item encoder learns to map item metadata to embeddings reflecting the rich information of the item, which can be matched with dialog context.The PLM then consumes the context-aware item embeddings and dialog context to generate high-quality recommendations and responses.Experimental results on the benchmark dataset REDIAL show that our model obtains stateof-the-art results on both recommendation and response generation tasks 1 .
Regularizing End-to-End Speech Translation with Triangular Decomposition Agreement

End-to-end speech-to-text translation (E2E-ST) is becoming increasingly popular due to the potential of its less error propagation, lower latency, and fewer parameters. Given the triplet training corpus〈speech, transcription, translation〉, the conventional high-quality E2E-ST system leverages the〈speech, transcription〉pair to pre-train the model and then utilizes the〈speech, translation〉pair to optimize it further. However, this process only involves two-tuple data at each stage, and this loose coupling fails to fully exploit the association between triplet data. In this paper, we attempt to model the joint probability of transcription and translation based on the speech input to directly leverage such triplet data. Based on that, we propose a novel regularization method for model training to improve the agreement of dual-path decomposition within triplet data, which should be equal in theory. To achieve this goal, we introduce two Kullback-Leibler divergence regularization terms into the model training objective to reduce the mismatch between output probabilities of dual-path. Then the well-trained model can be naturally transformed as the E2E-ST models by a pre-defined early stop tag. Experiments on the MuST-C benchmark demonstrate that our proposed approach significantly outperforms state-of-the-art E2E-ST baselines on all 8 language pairs while achieving better performance in the automatic speech recognition task.
Autonomous Cooperative Multi-Vehicle System for Interception of Aerial and Stationary Targets

This paper presents the design, development, and testing of hardware-software systems by the IISc-TCS team for Challenge 1 of the Mohamed Bin Zayed International Robotics Challenge 2020. The goal of Challenge 1 was to grab a ball suspended from a maneuvering UAV and pop balloons anchored to the ground, using suitable manipulators. The important tasks carried out to address this challenge include the design and development of a hardware system with efficient grabbing and popping mechanisms, considering the restrictions in volume and payload, design of accurate target interception algorithms using visual information suitable for outdoor environments, and development of a software architecture for dynamic, multi-agent, aerial systems performing complex tasks. In this paper, we discuss the design of a custom end-effector mounted on a single degree of freedom manipulator, and robust algorithms for the interception of targets in an uncertain environment. Vision-based guidance and tracking strategies are developed based on the concept of pursuit engagement and artificial potential function. The software architecture presented in this work develops an Operation Management System (OMS) architecture that allocates static and dynamic tasks collaboratively among multiple UAVs to perform any given task. An important aspect of this work is that all the systems developed were designed to operate in completely autonomous mode. A detailed description of the architecture along with simulations of complete challenge in the Gazebo environment and field experiment results are also included in this work. The developed hardware-software system is useful for counter-UAV systems and can be used for other applications.
Dynamic Key-Value Memory Enhanced Multi-Step Graph Reasoning for Knowledge-Based Visual Question Answering

Knowledge-based visual question answering (VQA) is a vision-language task that requires an agent to correctly answer image-related questions using knowledge that is not presented in the given image. It is not only a more challenging task than regular VQA but also a vital step towards building a general VQA system. Most existing knowledge-based VQA systems process knowledge and image information similarly and ignore the fact that the knowledge base (KB) contains complete information about a triplet, while the extracted image information might be incomplete as the relations between two objects are missing or wrongly detected. In this paper, we propose a novel model named dynamic knowledge memory enhanced multi-step graph reasoning (DMMGR), which performs explicit and implicit reasoning over a key-value knowledge memory module and a spatial-aware image graph, respectively. Specifically, the memory module learns a dynamic knowledge representation and generates a knowledge-aware question representation at each reasoning step. Then, this representation is used to guide a graph attention operator over the spatial-aware image graph. Our model achieves new state-of-the-art accuracy on the KRVQR and FVQA datasets. We also conduct ablation experiments to prove the effectiveness of each component of the proposed model.
Intelligent Online Selling Point Extraction for E-commerce Recommendation

In the past decade, automatic product description generation for e-commerce have witnessed significant advancement. As the services provided by e-commerce platforms become diverse, it is necessary to dynamically adapt the patterns of descriptions generated. The selling point of products is an important type of product description for which the length should be as short as possible while still conveying key information. In addition, this kind of product description should be eye-catching to the readers. Currently, product selling points are normally written by human experts. Thus, the creation and maintenance of these contents incur high costs. These costs can be significantly reduced if product selling points can be automatically generated by machines. In this paper, we report our experience developing and deploying the Intelligent Online Selling Point Extraction (IOSPE) system to serve the recommendation system in the JD.com e-commerce platform. Since July 2020, IOSPE has become a core service for 62 key categories of products (covering more than 4 million products). So far, it has generated more than 1.1 billion selling points, thereby significantly scaling up the selling point creation operation and saving human labour. These IOSPE generated selling points have increased the click-through rate (CTR) by 1.89% and the average duration the customers spent on the products by more than 2.03% compared to the previous practice, which are significant improvements for such a large-scale e-commerce platform.
Psychophysical Evaluation of Human Performance in Detecting Digital Face Image Manipulations

In recent years, increasing deployment of face recognition technology in security-critical settings, such as border control or law enforcement, has led to considerable interest in the vulnerability of face recognition systems to attacks utilising legitimate documents, which are issued on the basis of digitally manipulated face images. As automated manipulation and attack detection remains a challenging task, conventional processes with human inspectors performing identity verification remain indispensable. These circumstances merit a closer investigation of human capabilities in detecting manipulated face images, as previous work in this field is sparse and often concentrated only on specific scenarios and biometric characteristics. This work introduces a web-based, remote visual discrimination experiment on the basis of principles adopted from the field of psychophysics and subsequently discusses interdisciplinary opportunities with the aim of examining human proficiency in detecting different types of digitally manipulated face images, specifically face swapping, morphing, and retouching. In addition to analysing appropriate performance measures, a possible metric of detectability is explored. Experimental data of 306 probands indicate that detection performance is widely distributed across the population and detection of certain types of face image manipulations is much more challenging than others.
Temporal Attention-Pyramid Pooling for Temporal Action Detection

Temporal action detection is a challenging task in video understanding, which is usually divided into two stages: proposal generation and classification. Learning proposal features is a crucial step for both stages. However, most methods ignore temporal information of proposals and consider background and action frames in proposals equally, leading to poor proposal features. In this paper, we propose a novel Temporal Attention-Pyramid Pooling (TAPP) method to learn proposal features of arbitrary length action proposals. The TAPP method exploits the attention mechanism to focus on the discriminative part of proposals, suppressing background influence on proposal features. It constructs a temporal pyramid structure to convert arbitrary length proposal feature sequences to multiple fixed-length sequences while retaining the temporal information. In the TAPP method, we design a multi-scale temporal function and apply it to the temporal pyramid to generate final proposal features. Based on the TAPP method, we construct a temporal action proposal generation model and an action proposal classification model, and then we perform extensive experiments on two mainstream temporal action detection datasets for the temporal action proposal and temporal action detection tasks to verify our models. On the THUMOS’14 dataset, our models based on the TAPP significantly outperform the previous state-of-the-art methods for both tasks.
Dialogue Summaries as Dialogue States (DS2), Template-Guided Summarization for Few-shot Dialogue State Tracking

Annotating task-oriented dialogues is notorious for the expensive and difficult data collection process.Few-shot dialogue state tracking (DST) is a realistic solution to this problem.In this paper, we hypothesize that dialogue summaries are essentially unstructured dialogue states; hence, we propose to reformulate dialogue state tracking as a dialogue summarization problem.To elaborate, we train a text-totext language model with synthetic templatebased dialogue summaries, generated by a set of rules.Then, the dialogue states can be recovered by inversely applying the summary generation rules.We empirically show that our method DS2 outperforms previous works on few-shot DST in MultiWoZ 2.0 and 2.1, in both cross-domain and multi-domain settings.Our method 1 also exhibits vast speedup during both training and inference as it can generate all states at once.Finally, based on our analysis, we discover that the naturalness of the summary templates plays a key role for successful training.