Reviewerly/exHarmony huggingface dataset:
SUMA: A Partial Materialization-Based Scalable Query Answering in OWL 2 DL

Abstract Ontology-mediated querying (OMQ) provides a paradigm for query answering according to which users not only query records at the database but also query implicit information inferred from ontology. A key challenge in OMQ is that the implicit information may be infinite, which cannot be stored at the database and queried by off -the -shelf query engine. The commonly adopted technique to deal with infinite entailments is query rewriting, which, however, comes at the cost of query rewriting at runtime. In this work, the partial materialization method is proposed to ensure that the extension is always finite. The partial materialization technology does not rewrite query but instead computes partial consequences entailed by ontology before the online query. Besides, a query analysis algorithm is designed to ensure the completeness of querying rooted and Boolean conjunctive queries over partial materialization. We also soundly and incompletely expand our method to support highly expressive ontology language, OWL 2 DL. Finally, we further optimize the materialization efficiency by role rewriting algorithm and implement our approach as a prototype system SUMA by integrating off-the-shelf efficient SPARQL query engine. The experiments show that SUMA is complete on each test ontology and each test query, which is the same as Pellet and outperforms PAGOdA. Besides, SUMA is highly scalable on large datasets.
Prostate and tumor segmentation on PET/CT using Dual Mask R-CNN

We propose a learning-based method to automatically segment prostate and its dominant intraprostatic lesions (DILs) from positron emission tomography (PET)/computed tomography (CT) images. A dual attention mask R-CNN is introduced to enable end-to-end segmentation. To avoid the effect of useless region, mask R-CNN is used to get rid of non-tumor regions via fist locate the tumor region-of-interest (ROI) and then segment tumor within that ROI. Dual attention networks are used as backbone in mask R-CNN to extract comprehensive features from both CT and PET images. The binary mask of tumor of an arrival patient’s CT and PET image is generated by the well-trained network. To evaluate the proposed method, we retrospectively investigate 25 PET/CT datasets. On each dataset, prostate and DILs were delineated by physicians and was served as ground truth and training target. The proposed method was trained and evaluated by a Five-fold cross validation strategy. The average centroid distance, volume difference and DSC value for prostate/DIL among all 25 patients are 0.83&plusmn;0.91mm, -0.01&plusmn;0.79 and 0.84&plusmn;0.09, respectively. The proposed method has great potential in improving the efficiency and mitigating the observer-dependence in prostate and DIL contouring for DIL focal boost radiation therapy
A Solution for Power Crisis and Environment Pollution from Electricity Generation - A Study of Sub-tropical Regions

Air pollution is a primary environmental concern worldwide. The major sources of this pollution are the manufacturing industry, vehicles, diesel generators, and thermal power plants. For limiting this pollution, the resolution is adopted in Paris Climate Agreement to reduce the power dependency on thermal power plants. This work aims to find a solution for phasing out the thermal plant with solar plants for sub-tropical regions of the world. Moreover, the effect of the Air Pollution Index (API) on solar radiation is also predicted with time-series neural network. As a case study, to replace thermal power plant in sub-tropic regions, four metropolitan cities of India are selected. The performance ratio and energy yield of the proposed solar power plants have an average of 76.05% and 1,167,447 MWh/year values, respectively, which shows that the proposed power plants are a viable solution to address the aforementioned problem. The potential of the proposed power plant for pollution reduction is also quantified and discussed in detail, and the effect of PM2.5 on solar generation is also reported.
Hand-Gun Detection in Images with Transfer Learning-Based Convolutional Neural Networks

This study aims to evaluate the performance of different deep learning methods based on CNN (Convolutional Neural Networks) for hand-gun detection from images. Within the context of object detection, which is an application of CNN, this study further focusses on hand-gun detection from images. The application of a CNN trained from scratch, transfer learning method using VGG-16 (Visual Geometry Group) model and fine-tuning based on the same model are applied on the same image dataset and the results are evaluated using various evaluation metrics. The results on 8300 images with and without a hand-gun showed that the highest accuracies are obtained with fine tuning method. Besides, it has been observed that the CNN model obtained with fine-tuning method gave balanced accuracies on the images containing and not containing hand-gun.
HIFI: Anomaly Detection for Multivariate Time Series with High-order Feature Interactions

Monitoring complex systems results in massive multivariate time series data, and anomaly detection of these data is very important to maintain the normal operation of the systems. Despite the recent emergence of a large number of anomaly detection algorithms for multivariate time series, most of them ignore the correlation modeling among multivariate, which can often lead to poor anomaly detection results. In this work, we propose a novel anomaly detection model for multivariate time series with HIgh-order Feature Interactions (HIFI). More specifically, HIFI builds multivariate feature interaction graph automatically and uses the graph convolutional neural network to achieve high-order feature interactions, in which the long-term temporal dependencies are modeled by attention mechanisms and a variational encoding technique is utilized to improve the model performance and robustness. Extensive experiments on three publicly available datasets demonstrate the superiority of our framework compared with state-of-the-art approaches.
Exploring Clustering-Based Reinforcement Learning for Personalized Book Recommendation in Digital Library

Digital library as one of the most important ways in helping students acquire professional knowledge and improve their professional level has gained great attention in recent years. However, its large collection (especially the book resources) hinders students from finding the resources that they are interested in. To overcome this challenge, many researchers have already turned to recommendation algorithms. Compared with traditional recommendation tasks, in the digital library, there are two challenges in book recommendation problems. The first is that users may borrow books that they are not interested in (i.e., noisy borrowing behaviours), such as borrowing books for classmates. The second is that the number of books in a digital library is usually very large, which means one student can only borrow a small set of books in history (i.e., data sparsity issue). As the noisy interactions in students’ borrowing sequences may harm the recommendation performance of a book recommender, we focus on refining recommendations via filtering out data noises. Moreover, due to the the lack of direct supervision information, we treat noise filtering in sequences as a decision-making process and innovatively introduce a reinforcement learning method as our recommendation framework. Furthermore, to overcome the sparsity issue of students’ borrowing behaviours, a clustering-based reinforcement learning algorithm is further developed. Experimental results on two real-world datasets demonstrate the superiority of our proposed method compared with several state-of-the-art recommendation methods.
Consensus Control for Decentralized Deep Learning

Decentralized training of deep learning models enables on-device learning over networks, as well as efficient scaling to large compute clusters. Experiments in earlier works reveal that, even in a data-center setup, decentralized training often suffers from the degradation in the quality of the model: the training and test performance of models trained in a decentralized fashion is in general worse than that of models trained in a centralized fashion, and this performance drop is impacted by parameters such as network size, communication topology and data partitioning. We identify the changing consensus distance between devices as a key parameter to explain the gap between centralized and decentralized training. We show in theory that when the training consensus distance is lower than a critical quantity, decentralized training converges as fast as the centralized counterpart. We empirically validate that the relation between generalization performance and consensus distance is consistent with this theoretical observation. Our empirical insights allow the principled design of better decentralized training schemes that mitigate the performance drop. To this end, we provide practical training guidelines and exemplify its effectiveness on the data-center setup as the important first step.
On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation

Adapter-based tuning has recently arisen as an alternative to fine-tuning. It works by adding light-weight adapter modules to a pretrained language model (PrLM) and only updating the parameters of adapter modules when learning on a downstream task. As such, it adds only a few trainable parameters per new task, allowing a high degree of parameter sharing. Prior studies have shown that adapter-based tuning often achieves comparable results to fine-tuning. However, existing work only focuses on the parameter-efficient aspect of adapter-based tuning while lacking further investigation on its effectiveness. In this paper, we study the latter. We first show that adapter-based tuning better mitigates forgetting issues than fine-tuning since it yields representations with less deviation from those generated by the initial PrLM. We then empirically compare the two tuning methods on several downstream NLP tasks and settings. We demonstrate that 1) adapter-based tuning outperforms fine-tuning on low-resource and cross-lingual tasks; 2) it is more robust to overfitting and less sensitive to changes in learning rates.
Entity Resolution in Open-domain Conversations

Mingyue Shang, Tong Wang, Mihail Eric, Jiangning Chen, Jiyang Wang, Matthew Welch, Tiantong Deng, Akshay Grewal, Han Wang, Yue Liu, Yang Liu, Dilek Hakkani-Tur. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers. 2021.
RepSum: Unsupervised Dialogue Summarization based on Replacement Strategy

In the field of dialogue summarization, due to the lack of training data, it is often difficult for supervised summary generation methods to learn vital information from dialogue context.Several works on unsupervised summarization for document by leveraging semantic information solely or auto-encoder strategy (i.e., sentence compression), they however cannot be adapted to the dialogue scene due to the limited words in utterances and huge gap between the dialogue and its summary.In this study, we propose a novel unsupervised strategy to address this challenge, which roots from the hypothetical foundation that a superior summary approximates a replacement of the original dialogue, and they are roughly equivalent for auxiliary (self-supervised) tasks, e.g., dialogue generation.The proposed strategy Rep-Sum is applied to generate both extractive and abstractive summary with the guidance of the followed n th utterance generation and classification tasks.Extensive experiments on various datasets demonstrate the superiority of the proposed model compared with other unsupervised methods.
Generation and Annotation of Simulation-Real Ship Images for Convolutional Neural Networks Training and Testing

Large amounts of high-quality image data are the basis and premise of the high accuracy detection of objects in the field of convolutional neural networks (CNN). It is challenging to collect various high-quality ship image data based on the marine environment. A novel method based on CNN is proposed to generate a large number of high-quality ship images to address this. We obtained ship images with different perspectives and different sizes by adjusting the ships’ postures and sizes in three-dimensional (3D) simulation software, then 3D ship data were transformed into 2D ship image according to the principle of pinhole imaging. We selected specific experimental scenes as background images, and the target ships of the 2D ship images were superimposed onto the background images to generate “Simulation–Real” ship images (named SRS images hereafter). Additionally, an image annotation method based on SRS images was designed. Finally, the target detection algorithm based on CNN was used to train and test the generated SRS images. The proposed method is suitable for generating a large number of high-quality ship image samples and annotation data of corresponding ship images quickly to significantly improve the accuracy of ship detection. The annotation method proposed is superior to the annotation methods that label images with the image annotation software of Label-me and Label-img in terms of labeling the SRS images.
PM-LSH: a fast and accurate in-memory framework for high-dimensional approximate NN and closest pair search

Nearest neighbor (NN) search is inherently computationally expensive in high-dimensional spaces due to the curse of dimensionality. As a well-known solution, locality-sensitive hashing (LSH) is able to answer c-approximate NN (c-ANN) queries in sublinear time with constant probability. Existing LSH methods focus mainly on building hash bucket-based indexing such that the candidate points can be retrieved quickly. However, existing coarse-grained structures fail to offer accurate distance estimation for candidate points, which translates into additional computational overhead when having to examine unnecessary points. This in turn reduces the performance of query processing. In contrast, we propose a fast and accurate in-memory LSH framework, called PM-LSH, that aims to compute the c-ANN query on large-scale, high-dimensional datasets. First, we adopt a simple yet effective PM-tree to index the data points. Second, we develop a tunable confidence interval to achieve accurate distance estimation and guarantee high result quality. Third, we propose an efficient algorithm on top of the PM-tree to improve the performance of computing c-ANN queries. In addition, we extend PM-LSH to support closest pair (CP) search in high-dimensional spaces. Here, we again adopt the PM-tree to organize the points in a low-dimensional space, and we propose a branch and bound algorithm together with a radius pruning technique to improve the performance of computing c-approximate closest pair (c-ACP) queries. Extensive experiments with real-world data offer evidence that PM-LSH is capable of outperforming existing proposals with respect to both efficiency and accuracy for both NN and CP search.
Adaptative Balanced Distribution for Domain Adaptation With Strong Alignment

Aligning and balancing the marginal and conditional feature distributions are two critical procedures for unsupervised domain adaptation (UDA) problems. However, existing methods usually consider the former while ignoring the latter. To improve the problems of instability and imbalance, we propose the Adaptative Joint Distribution Adaptation Network (AJDAN) by analyzing the multi-modal interactions between the two types of distributions and adding a self-learning network to simultaneously balance them. Furthermore, we give higher weights to samples that are far away from the domain boundary (easy-to-classify samples) using Strong Binary Cross-Entropy (SBCE). The strong alignment strategy is adjustable and allows the network to better train easy-to-classify samples than traditional Binary Cross-Entropy (BCE) in various scenarios. The experiment shows that AJDAN with SBCE (AJDAN+S) provides an average of 68.3% accuracy on the Office-Home dataset, and 89.1% accuracy on the Office31 dataset, showing its superiority by 2~3 percent above the existing state-of-the-art methods.
Aggregate Distributed Photovoltaic Power Joint Prediction Method Based on LSTM

Accurate prediction of photovoltaic output fluctuations is the key to daily dispatch management and safe and stable operation of the power grid. In this paper, a short-term photovoltaic power prediction model based on Kmeans and LSTM is proposed. Kmeans is used to cluster the photovoltaic power generation of the initial training set and the prediction day, and the LSTM is trained on the initial training set data of each category, and the corresponding LSTM is used to predict the photovoltaic power according to the category of the predicted sample. Finally, the actual historical data of the power grid is used to simulate and analyze the proposed method. The results show that the proposed method can provide predictive data support for the distributed photovoltaic output fluctuation model of the power grid.
Modelling Virtual Bargaining using Logical Representation Change

We discuss work in progress on the computational modelling of virtual bargaining: inference-driven human coordination under severe communicative constraints. For this initial work we model variants of a two-player coordination game of item selection and avoidance taken from the current virtual bargaining literature. In this range of games, human participants collaborate to select items (e.g. bananas) or avoid items (e.g. scorpions), based on signalling conventions constructed and updated from shared assumptions, with minimal information exchange. We model behaviours in these games using logic programs interpretable as logical theories. From an initial theory comprised of rules, background assumptions and a basic signalling convention, we use automated theory repair to jointly adapt that basic signalling convention to novel contexts, with no explicit coordination between players. Our ABC system for theory repair delivers spontaneous adaptation, using reasoning failures to replace established conventions with better alternatives, matching human players’ own reasoning across several games.
FlexTileSource: An OpenSeadragon Extension for Efficient Whole-Slide Image Visualization

Background: Web-based digital slide viewers for pathology commonly use OpenSlide and OpenSeadragon (OSD) to access, visualize, and navigate whole-slide images (WSI). Their standard settings represent WSI as deep zoom images (DZI), a generic image pyramid structure that differs from the proprietary pyramid structure in the WSI files. The transformation from WSI to DZI is an additional, time-consuming step when rendering digital slides in the viewer, and inefficiency of digital slide viewers is a major criticism for digital pathology. Aims: To increase efficiency of digital slide visualization by serving tiles directly from the native WSI pyramid, making the transformation from WSI to DZI obsolete. Methods: We implemented a new flexible tile source for OSD that accepts arbitrary native pyramid structures instead of DZI levels. We measured its performance on a data set of 8104 WSI reviewed by 207 pathologists over 40 days in a web-based digital slide viewer used for routine diagnostics. Results: The new FlexTileSource accelerates the display of a field of view in general by 67 ms and even by 117 ms if the block size of the WSI and the tile size of the viewer is increased to 1024 px. We provide the code of our open-source library freely on https://github.com/schuefflerlab/openseadragon. Conclusions: This is the first study to quantify visualization performance on a web-based slide viewer at scale, taking block size and tile size of digital slides into account. Quantifying performance will enable to compare and improve web-based viewers and therewith facilitate the adoption of digital pathology.
Reach Out and Touch: Eliciting the Sense of Touch Through Gesture-Based Interaction

With the development of gesture-based interaction technologies (e.g., touchscreen devices and kinetic controllers), consumers can directly use their hands to interact with web interfaces, which may assist in creating a sense of touch for consumers. Drawing on feelings-as-information theory, this study investigates the impacts of two types of gesture-based interaction (i.e., touchscreen interaction and mid-air interaction) on consumers’ sense of touch. Results from a laboratory experiment showed that touchscreen interaction elicited a higher sense of touch than mid-air interaction when the importance of product haptic information was high. However, touchscreen interaction did not differ from mid-air interaction in terms of eliciting consumers’ sense of touch when the importance of product haptic information was low. Furthermore, consumers’ sense of touch improved their shopping experience satisfaction by reducing uncertainty about products and fostering attachment to products. Theoretically, this study contributes to the existing literature by empirically investigating the effects of gesture-based interaction on consumers’ bodily sensations, elucidating the role of the sense of touch in affecting consumers’ virtual product experience, and highlighting the impact of the interaction method on consumer behavior. This study also provides practical insights into the application of gesture-based interaction technologies.
A Multi-parameter Video Quality Assessment Model Based on 3D Convolutional Neural Network on the Cloud

As the rapid development of big data and the artificial intelligence technology, users prefer uploading more and more local files to the cloud server to reduce the pressure of local storage, but when users upload more and more duplicate files , not only wasting the network bandwidth, but also bringing much more inconvenience to the server management, especially images and videos. To solve the problems above, we design a multi-parameter video quality assessment model based on 3D convolutional neural network in the video deduplication system, we use a method similar to analytic hierarchy process to comprehensively evaluate the impact of packet loss rate, codec, frame rate, bit rate, resolution on video quality, and build a two-stream 3D convolutional neural network from the spatial flow and timing flow to capture the details of video distortion, set the coding layer to remove redundant distortion information. Finally, the LIVE and CSIQ data sets are used for experimental verification, we compare the performance of the proposed scheme with the V-BLIINDS scheme and VIDEO scheme under different packet loss rates. We also use the part of data set to simulate the interaction process between the client and the server, then test the time cost of the scheme. On the whole, the scheme proposed in this paper has a high quality assessment efficiency.
Implementation of Pruned Backpropagation Neural Network Based on Photonic Integrated Circuits

We demonstrate a pruned high-speed and energy-efficient optical backpropagation (BP) neural network. The micro-ring resonator (MRR) banks, as the core of the weight matrix operation, are used for large-scale weighted summation. We find that tuning a pruned MRR weight banks model gives an equivalent performance in training with the model of random initialization. Results show that the overall accuracy of the optical neural network on the MNIST dataset is 93.49% after pruning six-layer MRR weight banks on the condition of low insertion loss. This work is scalable to much more complex networks, such as convolutional neural networks and recurrent neural networks, and provides a potential guide for truly large-scale optical neural networks.