Reviewerly/exHarmony huggingface dataset:
GERE: Generative Evidence Retrieval for Fact Verification

Fact verification (FV) is a challenging task which aims to verify a claim using multiple evidential sentences from trustworthy corpora, e.g., Wikipedia. Most existing approaches follow a three-step pipeline framework, including document retrieval, sentence retrieval and claim verification. High-quality evidences provided by the first two steps are the foundation of the effective reasoning in the last step. Despite being important, high-quality evidences are rarely studied by existing works for FV, which often adopt the off-the-shelf models to retrieve relevant documents and sentences in an "index-retrieve-then-rank" fashion. This classical approach has clear drawbacks as follows: i) a large document index as well as a complicated search process is required, leading to considerable memory and computational overhead; ii) independent scoring paradigms fail to capture the interactions among documents and sentences in ranking; iii) a fixed number of sentences are selected to form the final evidence set. In this work, we propose GERE, the first system that retrieves evidences in a generative fashion, i.e., generating the document titles as well as evidence sentence identifiers. This enables us to mitigate the aforementioned technical issues since: i) the memory and computational cost is greatly reduced because the document index is eliminated and the heavy ranking process is replaced by a light generative process; ii) the dependency between documents and that between sentences could be captured via sequential generation process; iii) the generative formulation allows us to dynamically select a precise set of relevant evidences for each claim. The experimental results on the FEVER dataset show that GERE achieves significant improvements over the state-of-the-art baselines, with both time-efficiency and memory-efficiency.
Crypto-assets, corruption, and capital controls: Cross-country correlations

Empirical investigation of the factors underlying the growing usage of crypto-assets is in its infancy, owing to data limitations. In this paper, we present a simple cross-country analysis drawing on recently released survey-based data. We explore the correlation of crypto-asset usage with indicators of corruption, capital controls, a history of high inflation, and other factors. We find that crypto-asset usage is significantly and positively associated with corruption and capital controls. Notwithstanding the data limitations, the results support the case for regulating crypto-assets, including know-your-customer approaches, as opposed to taking a laissez-faire stance.
Field evaluation of the diagnostic performance of EasyScan GO: a digital malaria microscopy device based on machine-learning

Microscopic examination of Giemsa-stained blood films remains the reference standard for malaria parasite detection and quantification, but is undermined by difficulties in ensuring high-quality manual reading and inter-reader reliability. Automated parasite detection and quantification may address this issue.A multi-centre, observational study was conducted during 2018 and 2019 at 11 sites to assess the performance of the EasyScan Go, a microscopy device employing machine-learning-based image analysis. Sensitivity, specificity, accuracy of species detection and parasite density estimation were assessed with expert microscopy as the reference. Intra- and inter-device reliability of the device was also evaluated by comparing results from repeat reads on the same and two different devices. This study has been reported in accordance with the Standards for Reporting Diagnostic accuracy studies (STARD) checklist.In total, 2250 Giemsa-stained blood films were prepared and read independently by expert microscopists and the EasyScan Go device. The diagnostic sensitivity of EasyScan Go was 91.1% (95% CI 88.9-92.7), and specificity 75.6% (95% CI 73.1-78.0). With good quality slides sensitivity was similar (89.1%, 95%CI 86.2-91.5), but specificity increased to 85.1% (95%CI 82.6-87.4). Sensitivity increased with parasitaemia rising from 57% at < 200 parasite/µL, to ≥ 90% at > 200-200,000 parasite/µL. Species were identified accurately in 93% of Plasmodium falciparum samples (kappa = 0.76, 95% CI 0.69-0.83), and in 92% of Plasmodium vivax samples (kappa = 0.73, 95% CI 0.66-0.80). Parasite density estimates by the EasyScan Go were within ± 25% of the microscopic reference counts in 23% of slides.The performance of the EasyScan Go in parasite detection and species identification accuracy fulfil WHO-TDR Research Malaria Microscopy competence level 2 criteria. In terms of parasite quantification and false positive rate, it meets the level 4 WHO-TDR Research Malaria Microscopy criteria. All performance parameters were significantly affected by slide quality. Further software improvement is required to improve sensitivity at low parasitaemia and parasite density estimations. Trial registration ClinicalTrials.gov number NCT03512678.
Blockchain and network governance: learning from applications in the supply chain sector

Blockchain applications have the potential to greatly improve operational efficiency and effectiveness along the supply chain. Although we know what the barriers to the adoption of blockchain are, we know little about how firms overcome these barriers to reap the benefits of the technology. A particular challenge in adopting blockchain applications is the need to build and implement them among a network of users, requiring firms to collaborate. To manage and advance such collaborative efforts, blockchain projects install a centralised leadership. There is thus a tension between the need for centralised leadership and decentralised control to justify the use of blockchain technology. In this study, we investigate how blockchain projects navigate this tension. We employ a multiple case study methodology to compare five collaborative blockchain applications that are live today. Our findings indicate that the case applications all combine centralised management with decentralised oversight in a similar manner. We argue that this combination of centralised and decentralised control is a great benefit for the successful development and implementation of blockchain applications. The results underline that to benefit from blockchain technology in supply chain applications, an important collaborative organisational effort is necessary.
DeePOF: A hybrid approach of deep convolutional neural network and friendship to Point‐of‐Interest (POI) recommendation system in location‐based social networks

Abstract Today, millions of active users spend a percentage of their time on location‐based social networks like Yelp and Gowalla and share their rich information. They can easily learn about their friends' behaviors and where they are visiting and be influenced by their style. As a result, the existence of personalized recommendations and the investigation of meaningful features of users and Point of Interests (POIs), given the challenges of rich contents and data sparsity, is a substantial task to accurately recommend the POIs and interests of users in location‐based social networks (LBSNs). This work proposes a novel pipeline of POI recommendations named DeePOF based on deep learning and the convolutional neural network. This approach only takes into consideration the influence of the most similar pattern of friendship instead of the friendship of all users. The mean‐shift clustering technique is used to detect similarity. The most similar friends' spatial and temporal features are fed into our deep CNN technique. The output of several proposed layers can predict latitude and longitude and the ID of subsequent appropriate places, and then using the friendship interval of a similar pattern, the lowest distance venues are chosen. This combination method is estimated on two popular datasets of LBSNs. Experimental results demonstrate that analyzing similar friendships could make recommendations more accurate and the suggested model for recommending a sequence of top‐k POIs outperforms state‐of‐the‐art approaches.
FCMNet: Frequency-aware cross-modality attention networks for RGB-D salient object detection

RGB-D saliency detection aims to comprehensively use RGB images and depth maps to detect object saliency. This field still faces two challenges: 1) how to extract representative multimodal features and 2) how to effectively fuse them. Most of the previous methods in this field equally treat RGB and depth information as two modalities, while not considering the difference in the frequency domain of the two modalities, and may lose some complementary information. In this paper, we introduce the frequency channel attention mechanism into the fusion process. First, we design a frequency-aware cross-modality attention (FACMA) module to interweave adequate channel features and select representative features. In the FACMA module, we also propose a spatial frequency channel attention (SFCA) module to introduce more complementary information in different channels. Second, we develop a weighted cross-modality fusion (WCMF) module to adaptively fuse multimodality features by learning the content-dependent weight maps. Comprehensive experiments on several benchmark datasets demonstrate that the proposed framework outperforms seventeen state-of-the-art methods.
Fast Segmentation of Metastatic Foci in H&amp;E Whole-Slide Images for Breast Cancer Diagnosis

Breast cancer is the leading cause of death for women globally. In clinical practice, pathologists visually scan over enormous amounts of gigapixel microscopic tissue slide images, which is a tedious and challenging task. In breast cancer diagnosis, micro-metastases and especially isolated tumor cells are extremely difficult to detect and are easily neglected because tiny metastatic foci might be missed in visual examinations by medical doctors. However, the literature poorly explores the detection of isolated tumor cells, which could be recognized as a viable marker to determine the prognosis for T1NoMo breast cancer patients. To address these issues, we present a deep learning-based framework for efficient and robust lymph node metastasis segmentation in routinely used histopathological hematoxylin−eosin-stained (H−E) whole-slide images (WSI) in minutes, and a quantitative evaluation is conducted using 188 WSIs, containing 94 pairs of H−E-stained WSIs and immunohistochemical CK(AE1/AE3)-stained WSIs, which are used to produce a reliable and objective reference standard. The quantitative results demonstrate that the proposed method achieves 89.6% precision, 83.8% recall, 84.4% F1-score, and 74.9% mIoU, and that it performs significantly better than eight deep learning approaches, including two recently published models (v3_DCNN and Xception-65), and three variants of Deeplabv3+ with three different backbones, namely, U-Net, SegNet, and FCN, in precision, recall, F1-score, and mIoU (p<0.001). Importantly, the proposed system is shown to be capable of identifying tiny metastatic foci in challenging cases, for which there are high probabilities of misdiagnosis in visual inspection, while the baseline approaches tend to fail in detecting tiny metastatic foci. For computational time comparison, the proposed method takes 2.4 min for processing a WSI utilizing four NVIDIA Geforce GTX 1080Ti GPU cards and 9.6 min using a single NVIDIA Geforce GTX 1080Ti GPU card, and is notably faster than the baseline methods (4-times faster than U-Net and SegNet, 5-times faster than FCN, 2-times faster than the 3 different variants of Deeplabv3+, 1.4-times faster than v3_DCNN, and 41-times faster than Xception-65).
A spectral grouping-based deep learning model for haze removal of hyperspectral images

Haze contamination is a common issue in optical remote sensing images, including hyperspectral images (HSIs), which can distort the spectral features of land cover objects. Over the last decades, although many haze removal solutions have been developed, very few studies have focused on haze removal of HSIs. Moreover, most of these methods cannot fully explore the abundant spectral information of HSIs in haze removal. To cope with the issues, a data driven method is proposed for haze removal of HSIs in this paper. Specifically, we design a spectral grouping network (SG-Net) to fully utilize the useful information in each spectral band during the reconstruction. To facilitate the relationship construction between hazy image and the corresponding haze-clear image, the proposed SG-Net first groups each HSI into several spectral subsets based on the intra-spectral correlations. Then, these subsets are convoluted in parallel with multiple branches for feature extraction. Furthermore, a novel attention block is designed to connect the adjacent branches for feature transmission, which can distill the useful information (e.g., uncontaminated information in longer wavelength bands) of each subset and assist the reconstruction of HSIs. Comprehensive experiments on both simulated and real haze HSIs showed that SG-Net is more accurate than seven state-of-the-art haze removal methods and is also more robust to different haze levels and shapes.
Scaling-up AR: University Campus as a Physical-Digital Metaverse

The metaverse promises large-scale and persistent environments for users to share experiences at the intersection between physical and digital. Augmented reality (AR) is one of the primary technologies that supports blending digital content with physical environments. However, most AR applications are currently single-user and present significant constraints in terms of time and location. Scaling up the technology to large-sized environments and massively multi-user experiences thus represents one of the primary challenges to achieving such a vision. Due to these constraints, current proposals for the metaverse primarily consist of virtual worlds with little duality between physical and digital. This paper studies the feasibility of a large-scale and persistent AR experience shared among all visitors on our university campus. We define an integrated framework to enable the first AR campus metaverse by considering the nature of the various environments and the available sensing platforms.
Sentiment Analysis of the Harry Potter Series Using a Lexicon-Based Approach

The objective of this chapter is to conduct a sentiment analysis of the Harry Potter novel series written by British author J.K. Rowling. The text of the series is collected from GitHub as an R package provided by Bradley Boehmke. The chapter analyzed the text by R programming to explore dominant sentiments using a lexicon approach of natural language processing (NLP). The results revealed that Professor Slughorn scored the most positive sentiment among the main characters that have heroic qualities; Death Eaters had the most negative sentiment among the anti-hero characters; negative sentiment in the text around the anti-hero characters increased significantly, while the positive sentiment around the hero characters remained constant as the story progressed throughout the series; among the series of novels, The Deathly Hallows contained the most negative sentiment; among all the houses of Hogwarts School of Witchcraft and Wizardry, Hufflepuff had the most positive sentiment; and each book of the series appeared negative until the final chapter, which always ended with a positive sentiment.
SI-News: Integrating social information for news recommendation with attention-based graph convolutional network

High-quality news recommendation heavily relies on accurate and timely representations of news documents and user interests. Social information, which usually contains the most recent information about the activities of users and their friends, naturally reflects the dynamics and diversities of user interests. However, existing news recommendation approaches often overlook these dynamic items, and thus lead to suboptimal performance. In this paper, we propose a novel approach by embedding users’ interests from their social information by attentional graph convolutional network (GCN). We also improve news representations by jointly optimizing the titles and contents of news via attention mechanisms. Extensive experiments on three benchmark datasets show that our approach effectively improves news recommendation performance compared with state-of-the-art baselines. We also evaluate our model on a real-world dataset and the results demonstrate the superior performance of the proposed techniques in industry-level applications.
Evaluating the job shop scheduling problem on a D-wave quantum annealer

Job Shop Scheduling is a combinatorial optimization problem of particular importance for production environments where the goal is to complete a production task in the shortest possible time given limitations in the resources available. Due to its computational complexity it quickly becomes intractable for problems of interesting size. The emerging technology of Quantum Annealing provides an alternative computational architecture that promises improved scalability and solution quality. However, several limitations as well as open research questions exist in this relatively new and rapidly developing technology. This paper studies the application of quantum annealing to solve the job shop scheduling problem, describing each step required from the problem formulation to the fine-tuning of the quantum annealer and compares the solution quality with various classical solvers. Particular attention is devoted to aspects that are often overlooked, such as the computational cost of representing the problem in the formulation required by the quantum annealer, the relative qubits requirements and how to mitigate chain breaks. Furthermore, the impact of advanced tools such as reverse annealing is presented and its effectiveness discussed. The results indicate several challenges emerging at various stages of the experimental pipeline which bring forward important research questions and directions of improvement.
Animal Kingdom: A Large and Diverse Dataset for Animal Behavior Understanding

Understanding animals' behaviors is significant for a wide range of applications. However, existing animal behavior datasets have limitations in multiple aspects, including limited numbers of animal classes, data samples and provided tasks, and also limited variations in environmental conditions and viewpoints. To address these limitations, we create a large and diverse dataset, Animal Kingdom, that provides multiple annotated tasks to enable a more thorough understanding of natural animal behaviors. The wild animal footages used in our dataset record different times of the day in extensive range of environments containing variations in backgrounds, viewpoints, illumination and weather conditions. More specifically, our dataset contains 50 hours of annotated videos to localize relevant animal behavior segments in long videos for the video grounding task, 30K video sequences for the fine-grained multi-label action recognition task, and 33K frames for the pose estimation task, which correspond to a diverse range of animals with 850 species across 6 major animal classes. Such a challenging and comprehensive dataset shall be able to facilitate the community to develop, adapt, and evaluate various types of advanced methods for animal behavior analysis. Moreover, we propose a Collaborative Action Recognition (CARe) model that learns general and specific features for action recognition with unseen new animals. This method achieves promising performance in our experiments. Our dataset can be found at https://sutdcv.github.io/Animal-Kingdom.
Multi-object tracking in traffic environments: A systematic literature review

The use of computer vision techniques to detect objects in images has grown in recent years. These techniques are especially useful to automatically extract and analyze information from an image or a sequence of them. One of the problems addressed by computer vision is multi-object tracking over frames sequences. To know the path and direction of objects can be crucial for some areas like traffic control and supervision; by doing that the system can be able to reduce traffic jams or redirect vehicles over less condensed areas. These algorithms include several aspects to have in mind in order to start a new development or research in this area, for instance, is important to review the current state-of-the art techniques, the hardware requirements, the main evaluation metrics, the commonly used datasets, among others. Therefore, the objective of this research is to present a systematic literature review which analyzes the recent works developed in the area of multi-object tracking in traffic environments. This paper reviews the techniques, hardware, datasets, metrics, and open lines of research in this area.
Local government debt risk assessment: A deep learning-based perspective

The research assesses local government debt risks in China with deep learning methods. We perform natural language processing and sentiment classification on all publicly available prefecture-level governments' annual work reports from the previous three years. Then, for each of these cities, we calculated sentiment scores related to debt risks and examined the regional distribution of risks. Our empirical findings indicate that special attention should be paid to China's inland areas, where local government debt risks are highly concentrated. This paper extends the existing literature on discourse analysis with quantitative methods to the research of political economy.
Multiple Choice Questions based Multi-Interest Policy Learning for Conversational Recommendation

Conversational recommendation system (CRS) is able to obtain fine-grained and dynamic user preferences based on interactive dialogue. Previous CRS assumes that the user has a clear target item, which often deviates from the real scenario, that is for many users who resort to CRS, they might not have a clear idea about what they really like. Specifically, the user may have a clear single preference for some attribute types (e.g. brand) of items, while for other attribute types (e.g. color), the user may have multiple preferences or even no clear preferences, which leads to multiple acceptable attribute instances (e.g. black and red) of one attribute type. Therefore, the users could show their preferences over items under multiple combinations of attribute instances rather than a single item with unique combination of all attribute instances. As a result, we first propose a more realistic conversational recommendation learning setting, namely Multi-Interest Multi-round Conversational Recommendation (MIMCR), where users may have multiple interests in attribute instance combinations and accept multiple items with partially overlapped combinations of attribute instances. To effectively cope with the new CRS learning setting, in this paper, we propose a novel learning framework, namely Multiple Choice questions based Multi-Interest Policy Learning (MCMIPL). In order to obtain user preferences more efficiently, the agent generates multiple choice questions rather than binary yes/no ones on specific attribute instance. Furthermore, we propose a union set strategy to select candidate items instead of existing intersection set strategy in order to overcome over-filtering items during the conversation. Finally, we design a Multi-Interest Policy Learning (MIPL) module, which utilizes captured multiple interests of the user to decide next action, either asking attribute instances or recommending items. Extensive experimental results on four datasets demonstrate the superiority of our method for the proposed MIMCR setting.