Reviewerly/exHarmony huggingface dataset:
Paraphrase Generation as Zero-Shot Multilingual Translation: Disentangling Semantic Similarity from Lexical and Syntactic Diversity

Recent work has shown that a multilingual neural machine translation (NMT) model can be used to judge how well a sentence paraphrases another sentence in the same language (Thompson and Post, 2020); however, attempting to generate paraphrases from such a model using standard beam search produces trivial copies or near copies. We introduce a simple paraphrase generation algorithm which discourages the production of n-grams that are present in the input. Our approach enables paraphrase generation in many languages from a single multilingual NMT model. Furthermore, the amount of lexical diversity between the input and output can be controlled at generation time. We conduct a human evaluation to compare our method to a paraphraser trained on the large English synthetic paraphrase database ParaBank 2 (Hu et al., 2019c) and find that our method produces paraphrases that better preserve meaning and are more gramatical, for the same level of lexical diversity. Additional smaller human assessments demonstrate our approach also works in two non-English languages.
Human Rights and Digital Health Technologies.

Digital health technologies have been heralded as a critical solution to challenges and gaps in the delivery of quality health care and essential to achieving the Sustainable Development Goals. Yet they also present threats to privacy and confidentiality, which can lead to discrimination and violence, resulting in violations of the rights to health, housing, employment, freedom of assembly, expression, protection from arbitrary detention, bodily autonomy, and security. More broadly, without proper planning and safeguards, digital health technologies can contribute to expanding health inequity, widening the "digital divide" that separates those who can and cannot access such interventions. This article outlines key harms related to digital technologies for health, as well as ethical and human rights standards relevant to their use. It also presents several strategies for mitigating risks from digital health technologies and reviews mechanisms of accountability, including recent judicial rulings.
On-line three-dimensional coordinate measurement of dynamic binocular stereo vision based on rotating camera in large FOV

A dynamic binocular stereo vision (DBSV) based on a non-zoom rotating camera in a large field of view (FOV) is established herein. A novel two-point method is proposed to estimate the initial parameters of the camera quickly. The intrinsic parameters and roll angle of each camera remains constant, and the pitch and yaw angles after the camera rotates are directly estimated from output of high-precision two-axes platform, which makes it possible that three-dimensional (3D) coordinate can be measured online after rotation. Moreover, a target matching algorithm based on moving DLT is proposed to achieve automatic alignment of the camera. The accuracy of 3D coordinate measurement is evaluated on various synthetic and real data, and the DBSV is suitable for occasions where extremely high accuracy is not required in large FOV.
Artificial Intelligence and Intellectual Property

Abstract This book explores artificial intelligence (AI), which has become omnipresent in today's business environment: from chatbots to healthcare services to various ways of creating useful information. While AI has been increasingly used to optimize various creative and innovative processes, the integration of AI into products, services, and other operational procedures raises significant concerns across virtually all areas of intellectual property (IP) law. Thus, AI has drawn extensive attention from IP experts globally and there have been some works on specific issues in the intersection between AI and IP. Surprisingly, however, there has not been a book providing a broad and comprehensive picture from the perspectives of the very nature of AI technology, its commercial implications, its interaction with different kinds of IP, IP administration, software and data, its social and economic impact on the innovation policy, and ultimately AI's eligibility as a legal entity. The book aims to fill the gap.
Six Years of Shiny in Research - Collaborative Development of Web Tools in R

The use of Shiny in research publications is investigated. From the appearance of this popular web application framework for R through to 2018, it has been utilised in many diverse research areas. While it can be shown that the complexity of Shiny applications is limited by the background architecture, and real security concerns exist for novice app developers, the collaborative benefits are worth attention from the wider research community. Shiny simplifies the use of complex methodologies for users of different specialities, at the level of proficiency appropriate for the end user. This enables a diverse community of users to interact efficiently, utilising cutting-edge methodologies. The literature reviewed demonstrates that complex methodologies can be put into practice without the necessity for investment in professional training. It would appear that Shiny opens up concurrent benefits in communication between those who analyse data and those in other disciplines, thereby potentially enriching research through this technology.
A New Reciprocal Teaching Approach for Information Literacy Education under the Background of Big Data

The library is an important platform for readers to acquire information, and also shoulders the responsibility to cultivate readers’ information literacy. How to enhance information literacy is a public service issue that warrants prompt solution. In this study, we establish a reciprocal teaching mode composed of four elements: teacher, student, learning content and interaction-supporting platform, based on the reciprocal teaching theory, in combination with the characteristics of information literacy training in information-based libraries, uses virtual reality (VR) technology to produce verisimilar teaching resources and build a virtual reading environment and a virtual reading community, to enable readers to discuss, read and evaluate under a dynamic reading mode. Meanwhile, a new reciprocal teaching technique of “Rain Classroom” is also introduced innovatively. We first conduct a quantitative evaluation on information literacy using fuzzy comprehensive evaluation method, then verify and correct the evaluation results using data mining technology, so that objective and accurate results can be obtained for the evaluation of readers’ information literacy. After the implementation of this library learning mode, through a questionnaire survey and analysis, it is concluded that the proposed mode can help the library spread knowledge, improve the effect of information literacy.
PGT: Pseudo Relevance Feedback Using a Graph-Based Transformer

Most research on pseudo relevance feedback (PRF) has been done in vector space and probabilistic retrieval models. This paper shows that Transformer-based rerankers can also benefit from the extra context that PRF provides. It presents PGT, a graph-based Transformer that sparsifies attention between graph nodes to enable PRF while avoiding the high computational complexity of most Transformer architectures. Experiments show that PGT improves upon non-PRF Transformer reranker, and it is at least as accurate as Transformer PRF models that use full attention, but with lower computational costs.
Semi-Decentralized Federated Edge Learning for Fast Convergence on Non-IID Data

Federated edge learning (FEEL) has emerged as an effective approach to reduce the large communication latency in Cloud-based machine learning solutions, while preserving data privacy. Unfortunately, the learning performance of FEEL may be compromised due to limited training data in a single edge cluster. In this paper, we investigate a novel framework of FEEL, namely semi-decentralized federated edge learning (SD-FEEL). By allowing model aggregation across different edge clusters, SD-FEEL enjoys the benefit of FEEL in reducing the training latency, while improving the learning performance by accessing richer training data from multiple edge clusters. A training algorithm for SD-FEEL with three main procedures in each round is presented, including local model updates, intra-cluster and inter-cluster model aggregations, which is proved to converge on non-independent and identically distributed (non-IID) data. We also characterize the interplay between the network topology of the edge servers and the communication overhead of inter-cluster model aggregation on the training performance. Experiment results corroborate our analysis and demonstrate the effectiveness of SD-FFEL in achieving faster convergence than traditional federated learning architectures. Besides, guidelines on choosing critical hyper-parameters of the training algorithm are also provided.
Secondary Iron Mineral Detection via Hyperspectral Unmixing Analysis with Sentinel-2 Imagery

The exposition of minerals to oxygen as well as non-treated tailings in mining activities alter the balance of the ecosystem, specifically leading to the generation of acidic solutions in the presence of sulfidic minerals. Several secondary iron minerals are precipitated in these settings that can be detected via remote sensing applications. The purpose of this research is to investigate the capacity of hyperspectral analysis to determine the abundance of Acid Mine Drainage (AMD)-indicator secondary iron minerals in mine sites with the guidance of ground truth information. To this end, we focus on an abandoned coal mine site in Turkey to detect secondary iron minerals associated with AMD via multispectral Sentinel-2 imagery, in accordance with the laboratory analysis of field-collected samples through X-Ray Diffraction (XRD), Inductive Coupled Plasma (ICP), and ASD spectral analysis. In relation with the conducted laboratory XRD and ICP results, the proposed methodology first reveals the iron-induced absorption feature located between 700 and 900 nm on field-collected ASD spectra and reference USGS spectra through a baseline method, namely parabola fitting method. The subsequent remote sensing analysis then applies hyperspectral unmixing to Sentinel-2 imagery and identifies the spectral endmember indicating iron-absorption behavior by computing its spectral angle distance to reference spectra. The experiments reveal that while the iron absorption characteristics are not apparent in pixel spectra, the utilized unmixing methodology enables capturing of those features at sub-pixel level on the resulting endmembers. Second, the comparison between the calculated abundances with unmixing and iron levels obtained with ground based ICP analysis indicate coherent correlation values. Finally, among the utilized unmixing methods, the performance of SISAL is found better than MVSA with the resulting correlation values of 0.76 and 0.63, respectively, while also returning closer endmembers to the reference iron spectra. The performed research demonstrates the potential of hyperspectral applications on Sentinel-2 data to uncover the sub-pixel iron-induced spectral features in the visible region, proving compatible results between the spectral and laboratory analysis.
Knowledge Inheritance for Pre-trained Language Models

Recent explorations of large-scale pre-trained language models (PLMs) have revealed the power of PLMs with huge amounts of parameters, setting off a wave of training everlarger PLMs.However, it requires tremendous computational resources to train a largescale PLM, which may be practically unaffordable.In addition, existing large-scale PLMs are mainly trained from scratch individually, ignoring that many well-trained PLMs are available.To this end, we explore the question how could existing PLMs benefit training large-scale PLMs in future.Specifically, we introduce a pre-training framework named "knowledge inheritance" (KI) and explore how could knowledge distillation serve as auxiliary supervision during pre-training to efficiently learn larger PLMs.Experimental results demonstrate the superiority of KI in training efficiency.We also conduct empirical analyses to explore the effects of teacher PLMs' pre-training settings, including model architecture, pre-training data, etc.Finally, we show that KI could be applied to domain adaptation and knowledge transfer.The implementation is publicly available at https://github.com/thunlp/Knowledge-Inheritance.
An Amplified COCOMO-II Based Cost Estimation Model in Global Software Development Context

Global Software Development (GSD) projects comprise several critical cost drivers that affect the overall project cost and budget overhead. Thus, there is a need to amplify the existing model in GSD context to reduce the risks associated with cost overhead. Motivated by this, the current work aims at amplifying the existing algorithmic model with GSD cost drivers to get efficient estimates in the context of GSD. To achieve the targeted research objective, current state-of-the-art cost estimation techniques and GSD models are reported. Furthermore, the current study has proposed a conceptual framework to amplify the algorithmic COCOMO-II model in the GSD domain to accommodate additional cost drivers empirically validated by a systematic review and industrial practitioners. The main phases of amplification include identifying cost drivers, categorizing cost drivers, forming metrics, assignment of values, and finally altering the base model equation. Moreover, the proposed conceptual model's effectiveness is validated through expert judgment, case studies, and Magnitude of Relative Estimates (MRE). The obtained estimates are efficient, quantified, and cover additional GSD aspects than the existing models; hence we could overcome the GSD project's overall risk by implementing the model. Finally, the results indicate that the model needs further calibration and validation.
An optimized machine learning technology scheme and its application in fault detection in wireless sensor networks

Aiming at the problem of fault detection in data collection in wireless sensor networks, this paper combines evolutionary computing and machine learning to propose a productive technical solution. We choose the classical particle swarm optimization (PSO) and improve it, including the introduction of a biological population model to control the population size, and the addition of a parallel mechanism for further tuning. The proposed RS-PPSO algorithm was successfully used to optimize the initial weights and biases of back propagation neural network (BPNN), shortening the training time and raising the prediction accuracy. Wireless sensor networks (WSN) has become the key supporting platform of Internet of Things (IoT). The correctness of the data collected by the sensor nodes has a great influence on the reliability, real-time performance and energy saving of the entire network. The optimized machine learning technology scheme given in this paper can effectively identify the fault data, so as to ensure the effective operation of WSN.
Global Graph Attention Embedding Network for Relation Prediction in Knowledge Graphs

The incompleteness of knowledge graphs triggers considerable research interest in relation prediction. As the key to predicting relations among entities, many efforts have been devoted to learning the embeddings of entities and relations by incorporating a variety of neighbors’ information which includes not only the information from direct outgoing and incoming neighbors but also the ones from the indirect neighbors on the multihop paths. However, previous models usually consider entity paths of limited length or ignore sequential information of the paths. Either simplification will make the model lack a global understanding of knowledge graphs and may result in the loss of important and indispensable information. In this article, we propose a novel global graph attention embedding network (GGAE) for relation prediction by combining global information from both direct neighbors and multihop neighbors. Concretely, given a knowledge graph, we first introduce the path construction algorithms to obtain meaningful paths, then design path modeling methods to capture the potential long-distance sequential information in the multihop paths, final propose an entity graph attention and a relation graph attention mechanisms to obtain entity embeddings and relation embeddings. Moreover, an entity graph attention mechanism is proposed to calculate the entity embeddings by aggregating direct incoming and outgoing neighbors from: 1) an original knowledge graph with the original entity and relation embeddings and 2) a new knowledge graph constructed by the paths whose embeddings are updated by path modeling methods. for each relation, we construct a new graph with related entities and present a relation graph attention to learn the features. Therefore, our model can encapsulate the information from different distance neighbors, and enable the embeddings of entities and relations to better capture all-sided semantic information. The experimental results on benchmark datasets verify the superiority of our model over the state-of-the-art ones.
Fully Hyperbolic Neural Networks

Hyperbolic neural networks have shown great potential for modeling complex data.However, existing hyperbolic networks are not completely hyperbolic, as they encode features in the hyperbolic space yet formalize most of their operations in the tangent space (a Euclidean subspace) at the origin of the hyperbolic model.This hybrid method greatly limits the modeling ability of networks.In this paper, we propose a fully hyperbolic framework to build hyperbolic networks based on the Lorentz model by adapting the Lorentz transformations (including boost and rotation) to formalize essential operations of neural networks.Moreover, we also prove that linear transformation in tangent spaces used by existing hyperbolic networks is a relaxation of the Lorentz rotation and does not include the boost, implicitly limiting the capabilities of existing hyperbolic networks.The experimental results on four NLP tasks show that our method has better performance for building both shallow and deep networks.Our code is released to facilitate follow-up research 1 . IntroductionVarious recent efforts have explored hyperbolic neural networks to learn complex non-Euclidean data properties.Nickel and Kiela (2017); Cvetkovski and Crovella (2016); Verbeek and Suri (2014) learn hierarchical representations in a hyperbolic space and show that hyperbolic geometry * Equal contribution.
A Sliding-Window Approach to Automatic Creation of Meeting Minutes

Meeting minutes record any subject matters discussed, decisions reached and actions taken at meetings.The importance of minuting cannot be overemphasized in a time when a significant number of meetings take place in the virtual space.In this paper, we present a sliding window approach to automatic generation of meeting minutes.It aims to tackle issues associated with the nature of spoken text, including lengthy transcripts and lack of document structure, which make it difficult to identify salient content to be included in the meeting minutes.Our approach combines a sliding window and a neural abstractive summarizer to navigate through the transcripts to find salient content.The approach is evaluated on transcripts of natural meeting conversations, where we compare results obtained for human transcripts and two versions of automatic transcripts and discuss how and to what extent the summarizer succeeds at capturing salient content.
Sparsely-labeled source assisted domain adaptation

Domain Adaptation (DA) aims to generalize the classifier learned from a well-labeled source domain to an unlabeled target domain. Existing DA methods usually assume that rich labels could be available in the source domain. However, we usually confront the source domain with a large number of unlabeled data but only a few labeled data, and thus, how to transfer knowledge from this sparsely-labeled source domain to the target domain is still a challenge, which greatly limits its application in the wild. This paper proposes a novel Sparsely-Labeled Source Assisted Domain Adaptation (SLSA-DA) algorithm to address the challenge with limited labeled source domain samples. Specifically, due to the label scarcity problem, the projected clustering is first conducted on both the source and target domains, so that the discriminative structures of data could be exploited elegantly. Then label propagation is adopted to propagate the labels from those limited labeled source samples to the whole unlabeled data progressively, so that the cluster labels are revealed correctly. Finally, we jointly align the marginal and conditional distributions to mitigate the cross-domain mismatching problem, and optimize those three procedures iteratively. However, it is nontrivial to incorporate the above three procedures into a unified optimization framework seamlessly since some variables to be optimized are implicitly involved in their formulas, thus they could not benefit to each other. Remarkably, we prove that the projected clustering and conditional distribution alignment could be reformulated into other formulations, thus the implicit variables are embedded in different optimization steps. As such, the variables related to those three quantities could be optimized in a unified optimization framework and benefit to each other, and improve the recognition performance obviously. Extensive experiments have verified that our approach could deal with the challenge in the SLSA-DA setting, and achieve the best performances across different real-world cross-domain visual recognition tasks. Our preliminary Matlab code is available at https://github.com/WWLoveTransfer/SLSA-DA/.
Complex Robotic Manipulation via Graph-Based Hindsight Goal Generation

Reinforcement learning algorithms, such as hindsight experience replay (HER) and hindsight goal generation (HGG), have been able to solve challenging robotic manipulation tasks in multigoal settings with sparse rewards. HER achieves its training success through hindsight replays of past experience with heuristic goals but underperforms in challenging tasks in which goals are difficult to explore. HGG enhances HER by selecting intermediate goals that are easy to achieve in the short term and promising to lead to target goals in the long term. This guided exploration makes HGG applicable to tasks in which target goals are far away from the object’s initial position. However, the vanilla HGG is not applicable to manipulation tasks with obstacles because the Euclidean metric used for HGG is not an accurate distance metric in such an environment. Although, with the guidance of a handcrafted distance grid, grid-based HGG can solve manipulation tasks with obstacles, a more feasible method that can solve such tasks automatically is still in demand. In this article, we propose graph-based hindsight goal generation (G-HGG), an extension of HGG selecting hindsight goals based on shortest distances in an obstacle-avoiding graph, which is a discrete representation of the environment. We evaluated G-HGG on four challenging manipulation tasks with obstacles, where significant enhancements in both sample efficiency and overall success rate are shown over HGG and HER. Videos can be viewed at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://videoviewsite.wixsite.com/ghgg</uri> .