Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Computer Sciences
Title: FusionNet: Fusing via Fully-Aware Attention with Application to Machine Comprehension
Abstract: This paper introduces a new neural structure called FusionNet, which extends existing attention approaches from three perspectives. First, it puts forward a novel concept of "history of word" to characterize attention information from the lowest word-level embedding up to the highest semantic-level representation. Second, it introduces an improved attention scoring function that better utilizes the "history of word" concept. Third, it proposes a fully-aware multi-level attention mechanism to capture the complete information in one text (such as a question) and exploit it in its counterpart (such as context or passage) layer by layer. We apply FusionNet to the Stanford Question Answering Dataset (SQuAD) and it achieves the first position for both single and ensemble model on the official SQuAD leaderboard at the time of writing (Oct. 4th, 2017). Meanwhile, we verify the generalization of FusionNet with two adversarial SQuAD datasets and it sets up the new state-of-the-art on both datasets: on AddSent, FusionNet increases the best F1 metric from 46.6% to 51.4%; on AddOneSent, FusionNet boosts the best F1 metric from 56.0% to 60.7%.
Extracted statement — has model: FusionNet
Research field: Computer Sciences
Title: HyperNetworks
Abstract: This work explores hypernetworks: an approach of using one network, also known as a hypernetwork, to generate the weights for another network. We apply hypernetworks to generate adaptive weights for recurrent networks. In this case, hypernetworks can be viewed as a relaxed form of weight-sharing across layers. In our implementation, hypernetworks are are trained jointly with the main network in an end-to-end fashion. Our main result is that hypernetworks can generate non-shared weights for LSTM and achieve state-of-the-art results on a variety of sequence modelling tasks including character-level language modelling, handwriting generation and neural machine translation, challenging the weight-sharing paradigm for recurrent networks.
Extracted statement — has model: Hypernetworks
Research field: Computer Sciences
Title: Perceiver: General Perception with Iterative Attention
Abstract: Biological systems understand the world by simultaneously processing high-dimensional inputs from modalities as diverse as vision, audition, touch, proprioception, etc. The perception models used in deep learning on the other hand are designed for individual modalities, often relying on domain-specific assumptions such as the local grid structures exploited by virtually all existing vision models. These priors introduce helpful inductive biases, but also lock models to individual modalities. In this paper we introduce the Perceiver – a model that builds upon Transformers and hence makes few architectural assumptions about the relationship between its inputs, but that also scales to hundreds of thousands of inputs, like ConvNets. The model leverages an asymmetric attention mechanism to iteratively distill inputs into a tight latent bottleneck, allowing it to scale to handle very large inputs. We show that this architecture performs competitively or beyond strong, specialized models on classification tasks across various modalities: images, point clouds, audio, video and video+audio. The Perceiver obtains performance comparable to ResNet-50 on ImageNet without convolutions and by directly attending to 50,000 pixels. It also surpasses state-of-the-art results for all modalities in AudioSet.
Extracted statement — has model: Perceiver
Research field: Computer Sciences
Title: Crowdsourced semantic annotation of scientific publications and tabular data in PDF
Abstract: Significant amounts of knowledge in science and technology have so far not been published as Linked Open Data but are contained in the text and tables of legacy PDF publications. Making such information available as RDF would, for example, provide direct access to claims and facilitate surveys of related work. A lot of valuable tabular information that till now only existed in PDF documents would also finally become machine understandable. Instead of studying scientific literature or engineering patents for months, it would be possible to collect such input by simple SPARQL queries. The SemAnn approach enables collaborative annotation of text and tables in PDF documents, a format that is still the common denominator of publishing, thus maximising the potential user base. The resulting annotations in RDF format are available for querying through a SPARQL endpoint. To incentivise users with an immediate benefit for making the effort of annotation, SemAnn recommends related papers, taking into account the hierarchical context of annotations in a novel way. We evaluated the usability of SemAnn and the usefulness of its recommendations by analysing annotations resulting from tasks assigned to test users and by interviewing them. While the evaluation shows that even few annotations lead to a good recall, we also observed unexpected, serendipitous recommendations, which confirms the merit of our low-threshold annotation support for the crowd.
Extracted statement — Name: SemAnn
Research field: Computer Sciences
Title: Span-based Joint Entity and Relation Extraction with Transformer Pre-training
Abstract: We introduce SpERT, an attention model for span-based joint entity and relation extraction. Our key contribution is a light-weight reasoning on BERT embeddings, which features entity recognition and filtering, as well as relation classification with a localized, marker-free context representation. The model is trained using strong within-sentence negative samples, which are efficiently extracted in a single BERT pass. These aspects facilitate a search over all spans in the sentence. In ablation studies, we demonstrate the benefits of pre-training, strong negative sampling and localized context. Our model outperforms prior work by up to 2.6% F1 score on several datasets for joint entity and relation extraction.
Extracted statement — has model: SpERT
Research field: Computer Sciences
Title: TableSeer: automatic table metadata extraction and searching in digital libraries
Abstract: Tables are ubiquitous in digital libraries. In scientific documents, tables are widely used to present experimental results or statistical data in a condensed fashion. However, current search engines do not support table search. The difficulty of automatic extracting tables from un-tagged documents, the lack of a universal table metadata specification, and the limitation of the existing ranking schemes make table search problem challenging. In this paper, we describe TableSeer, a search engine for tables. TableSeer crawls digital libraries, detects tables from documents, extracts tables metadata, indexes and ranks tables, and provides a user-friendly search interface. We propose an extensive set of medium-independent metadata for tables that scientists and other users can adopt for representing table information. In addition, we devise a novel page box-cutting method to improve the performance of the table detection. Given a query, TableSeer ranks the matched tables using an innovative ranking algorithm - TableRank. TableRank rates each ⃭query, tableℂ pair with a tailored vector space model and a specific term weighting scheme. Overall, TableSeer eliminates the burden of manually extract table data from digital libraries and enables users to automatically examine tables. We demonstrate the value of TableSeer with empirical studies on scientific documents.
Extracted statement — Name: TableSeer
Research field: Computer Sciences
Title: Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders
Abstract: Named entity recognition and relation extraction are two important fundamental problems. Joint learning algorithms have been proposed to solve both tasks simultaneously, and many of them cast the joint task as a table-filling problem. However, they typically focused on learning a single encoder (usually learning representation in the form of a table) to capture information required for both tasks within the same space. We argue that it can be beneficial to design two distinct encoders to capture such two different types of information in the learning process. In this work, we propose the novel {\em table-sequence encoders} where two different encoders -- a table encoder and a sequence encoder are designed to help each other in the representation learning process. Our experiments confirm the advantages of having {\em two} encoders over {\em one} encoder. On several standard datasets, our model shows significant improvements over existing approaches.
Extracted statement — has model: Table-Sequence
Research field: Computer Sciences
Title: R-Transformer: Recurrent Neural Network Enhanced Transformer
Abstract: Recurrent Neural Networks have long been the dominating choice for sequence modeling. However, it severely suffers from two issues: impotent in capturing very long-term dependencies and unable to parallelize the sequential computation procedure. Therefore, many non-recurrent sequence models that are built on convolution and attention operations have been proposed recently. Notably, models with multi-head attention such as Transformer have demonstrated extreme effectiveness in capturing long-term dependencies in a variety of sequence modeling tasks. Despite their success, however, these models lack necessary components to model local structures in sequences and heavily rely on position embeddings that have limited effects and require a considerable amount of design efforts. In this paper, we propose the R-Transformer which enjoys the advantages of both RNNs and the multi-head attention mechanism while avoids their respective drawbacks. The proposed model can effectively capture both local structures and global long-term dependencies in sequences without any use of position embeddings. We evaluate R-Transformer through extensive experiments with data from a wide range of domains and the empirical results show that R-Transformer outperforms the state-of-the-art methods by a large margin in most of the tasks. We have made the code publicly available at \url{this https URL}.
Extracted statement — has model: Transformer
Research field: Computer Sciences
Title: Unsupervised Learning of Semantic Audio Representations
Abstract: Even in the absence of any explicit semantic annotation, vast collections of audio recordings provide valuable information for learning the categorical structure of sounds. We consider several class-agnostic semantic constraints that apply to unlabeled nonspeech audio: (i) noise and translations in time do not change the underlying sound category, (ii) a mixture of two sound events inherits the categories of the constituents, and (iii) the categories of events in close temporal proximity are likely to be the same or related. Without labels to ground them, these constraints are incompatible with classification loss functions. However, they may still be leveraged to identify geometric inequalities needed for triplet loss-based training of convolutional neural networks. The result is low-dimensional embeddings of the input spectrograms that recover 41% and 84% of the performance of their fully-supervised counterparts when applied to downstream query-by-example sound retrieval and sound event classification tasks, respectively. Moreover, in limited-supervision settings, our unsupervised embeddings double the state-of-the-art classification performance.
Extracted statement — has model: Triplet
Research field: Computer Sciences
Title: Piano Skills Assessment
Abstract: Can a computer determine a piano player’s skill level? Is it preferable to base this assessment on visual analysis of the player’s performance or should we trust our ears over our eyes? Since current convolutional neural networks (CNNs) have difficulty processing long video videos, how can shorter clips be sampled to best reflect the players skill level? In this work, we collect and release a first-of-its-kind dataset for multimodal skill assessment focusing on assessing piano player’s skill level, answer the asked questions, initiate work in automated evaluation of piano playing skills and provide baselines for future work. Dataset can be accessed from: https://github.com/ParitoshParmar/Piano-Skills-Assessment.
Extracted statement — has model: Video
Research field: Computer Sciences
Title: K-isomorphism: privacy preserving network publication against structural attacks
Abstract: Serious concerns on privacy protection in social networks have been raised in recent years; however, research in this area is still in its infancy. The problem is challenging due to the diversity and complexity of graph data, on which an adversary can use many types of background knowledge to conduct an attack. One popular type of attacks as studied by pioneer work [2] is the use of embedding subgraphs. We follow this line of work and identify two realistic targets of attacks, namely, NodeInfo and LinkInfo. Our investigations show that k-isomorphism, or anonymization by forming k pairwise isomorphic subgraphs, is both sufficient and necessary for the protection. The problem is shown to be NP-hard. We devise a number of techniques to enhance the anonymization efficiency while retaining the data utility. A compound vertex ID mechanism is also introduced for privacy preservation over multiple data releases. The satisfactory performance on a number of real datasets, including HEP-Th, EUemail and LiveJournal, illustrates that the high symmetry of social networks is very helpful in mitigating the difficulty of the problem.
Extracted statement — Anonymistion algorithm/method: k-isomorphism
Research field: Computer Vision and Pattern Recognition
Title: A Haar-Cascade classifier based Smart Parking System
Abstract: In this paper, we present the implementation of a Haar–Cascade based classifier for car detection. This constitutes the detection sub-module of the framework for a Smart Parking System (SParkSys). In high density cities, finding available parking can be time consuming and results in traffic congestions as drivers cruise to find parking. The computer vision based smart parking solution presented in this paper has the advantage of being the least intrusive of most car sensing technologies. It is scalable for use with large parking facilities. Functional code from OpenCV was used in conjunction with custom Python code to implement the algorithm. Our tests show mixed results, with excellent true positive detections along with some with false negatives. Remarkable is that the classification algorithm learnt features that are common across a wide range of objects of interest.
Extracted statement — Software platform: OpenCV
Research field: Computer Vision and Pattern Recognition
Title: A Haar-Cascade classifier based Smart Parking System
Abstract: In this paper, we present the implementation of a Haar–Cascade based classifier for car detection. This constitutes the detection sub-module of the framework for a Smart Parking System (SParkSys). In high density cities, finding available parking can be time consuming and results in traffic congestions as drivers cruise to find parking. The computer vision based smart parking solution presented in this paper has the advantage of being the least intrusive of most car sensing technologies. It is scalable for use with large parking facilities. Functional code from OpenCV was used in conjunction with custom Python code to implement the algorithm. Our tests show mixed results, with excellent true positive detections along with some with false negatives. Remarkable is that the classification algorithm learnt features that are common across a wide range of objects of interest.
Extracted statement — Software platform: Python
Research field: Cultural History
Title: Public History and Contested Heritage: Archival Memories of the Bombing of Italy
Abstract: This article presents a case study of a collaborative public history project between participants in two countries, the United Kingdom and Italy. Its subject matter is the bombing war in Europe, 1939-1945, which is remembered and commemorated in very different ways in these two countries: the sensitivities involved thus constitute not only a case of public history conducted at the national level but also one involving contested heritage. An account of the ways in which public history has developed in the UK and Italy is presented. This is followed by an explanation of how the bombing war has been remembered in each country. In the UK, veterans of RAF Bomber Command have long felt a sense of neglect, largely because the deliberate targeting of civilians has not fitted comfortably into the dominant victor narrative. In Italy, recollections of being bombed have remained profoundly dissonant within the received liberation discourse. The International Bomber Command Centre Digital Archive (or Archive) is then described as a case study that employs a public history approach, focusing on various aspects of its inclusive ethos, intended to preserve multiple perspectives. The Italian component of the project is highlighted, problematising the digitisation of contested heritage within the broader context of twentieth-century history. Reflections on the use of digital archiving practices and working in partnership are offered, as well as a brief account of user analytics of the Archive through its first eighteen months online.
Extracted statement — Country of study:  Italy.
Research field: Cultural History
Title: Digital Media, Participatory Culture, and Difficult Heritage: Online Remediation and the Trans-Atlantic Slave Trade
Abstract: A diverse and changing array of digital media have been used to present heritage online. While websites have been created for online heritage outreach for nearly two decades, social media is employed increasingly to complement and in some cases replace the use of websites. These same social media are used by stakeholders as a form of participatory culture, to create communities and to discuss heritage independently of narratives offered by official institutions such as museums, memorials, and universities. With difficult or “dark” heritage—places of memory centering on deaths, disasters, and atrocities—these online representations and conversations can be deeply contested. Examining the websites and social media of difficult heritage, with an emphasis on the trans-Atlantic slave trade provides insights into the efficacy of online resources provided by official institutions, as well as the unofficial, participatory communities of stakeholders who use social media for collective memories.
Extracted statement — has stakeholder: communities
Research field: Cultural History
Title: The Role of Smart City Characteristics in the Plans of Fifteen Cities
Abstract: ABSTRACT This paper identifies the characteristics of smart cities as they emerge from the recent literature. It then examines whether and in what way these characteristics are present in the smart city plans of 15 cities: Amsterdam, Barcelona, London, PlanIT Valley, Stockholm, Cyberjaya, Singapore, King Abdullah Economic City, Masdar, Skolkovo, Songdo, Chicago, New York, Rio de Janeiro, and Konza. The results are presented with respect to each smart city characteristic. As expected, most strategies emphasize the role of information and communication technologies in improving the functionality of urban systems and advancing knowledge transfer and innovation networks. However, this research yields other interesting findings that may not yet have been documented across multiple case studies; for example, most smart city strategies fail to incorporate bottom-up approaches, are poorly adapted to accommodate the local needs of their area, and consider issues of privacy and security inadequately.
Extracted statement — has smart city instance: Cyberjaya
Research field: Cultural History
Title: Digital heritage interpretation: a conceptual framework
Abstract: ABSTRACT ‘Heritage Interpretation’ has always been considered as an effective learning, communication and management tool that increases visitors’ awareness of and empathy to heritage sites or artefacts. Yet the definition of ‘digital heritage interpretation’ is still wide and so far, no significant method and objective are evident within the domain of ‘digital heritage’ theory and discourse. Considering ‘digital heritage interpretation’ as a process rather than as a tool to present or communicate with end-users, this paper presents a critical application of a theoretical construct ascertained from multiple disciplines and explicates four objectives for a comprehensive interpretive process. A conceptual model is proposed and further developed into a conceptual framework with fifteen considerations. This framework is then implemented and tested on an online platform to assess its impact on end-users’ interpretation level. We believe the presented interpretive framework (PrEDiC) will help heritage professionals and media designers to develop interpretive heritage project.
Extracted statement — Material: end-users
Research field: Cultural History
Title: The Role of Smart City Characteristics in the Plans of Fifteen Cities
Abstract: ABSTRACT This paper identifies the characteristics of smart cities as they emerge from the recent literature. It then examines whether and in what way these characteristics are present in the smart city plans of 15 cities: Amsterdam, Barcelona, London, PlanIT Valley, Stockholm, Cyberjaya, Singapore, King Abdullah Economic City, Masdar, Skolkovo, Songdo, Chicago, New York, Rio de Janeiro, and Konza. The results are presented with respect to each smart city characteristic. As expected, most strategies emphasize the role of information and communication technologies in improving the functionality of urban systems and advancing knowledge transfer and innovation networks. However, this research yields other interesting findings that may not yet have been documented across multiple case studies; for example, most smart city strategies fail to incorporate bottom-up approaches, are poorly adapted to accommodate the local needs of their area, and consider issues of privacy and security inadequately.
Extracted statement — has smart city instance: Masdar
Research field: Cultural History
Title: SMART CITIES AND HERITAGE CONSERVATION: DEVELOPING A SMARTHERITAGE AGENDA FOR SUSTAINABLE INCLUSIVE COMMUNITIES
Abstract: <jats:p>This paper discusses the potential of current advancements in Information Communication Technologies (ICT) for cultural heritage preservation, valorization and management within contemporary cities. The paper highlights the potential of virtual environments to assess the impacts of heritage policies on urban development. It does so by discussing the implications of virtual globes and crowdsourcing to support the participatory valuation and management of cultural heritage assets. To this purpose, a review of available valuation techniques is here presented together with a discussion on how these techniques might be coupled with ICT tools to promote inclusive governance. </jats:p>
Extracted statement — Has method: review