Reviewerly/exHarmony huggingface dataset:
Scalable Decentralized Privacy-Preserving Usage-Based Insurance for Vehicles

Compared with traditional insurance schemes, usage-based insurance (UBI) for vehicles is more economic and accurate for drivers since its insurance premium calculation depends on how vehicles are driven. However, UBI requires sensitive driving data to determine insurance premiums, and this could result in serious privacy breach for drivers. Meanwhile, existing UBI solutions rely on a centralized entity (i.e., the insurance company) to manage insurances. In this article, we design a decentralized and privacy-preserving UBI scheme, called DUBI, based on the blockchain technology and zero-knowledge proof. In our scheme, a smart contract running over the blockchain serves as a “decentralized” insurance company, while drivers continuously upload their committed driving data to the blockchain. Periodically, the driver submits accumulated driving statistics with a zero-knowledge proof to the smart contract, which verifies the proof and calculates the insurance premium from the submitted statistics. We formulate an ideal functionality for DUBI under the universal composability framework, and then provide a formal security proof for DUBI. Furthermore, we give in-depth analysis and performance evaluation for DUBI with an implementation based on Ethereum. It shows that DUBI is highly efficient in processing UBI insurances in both storage and computation: DUBI is about seven times more efficient than existing schemes in storage, and proof generation and verification take only 7 and 30 ms, respectively.
A Deep Neural Network Algorithm for Detecting Credit Card Fraud

The credit card is easy to use for online transaction or shopping. However, the credit card fraud happens every day all over the world and it is hard for manual detection. Many organizations including banks, information companies and even government cost billions of dollars to invent an automatic fraud detection system. In this paper, we propose a deep network for fraud detection algorithm. The log transform is used to handle the data skew problems which exists in the dataset. The focal loss is added to the network for training the hard examples. The experiments show our neural network model outperforms the other classical models like support vector machine and logistic regression.
A knowledge-graph platform for newsrooms

Journalism is challenged by digitalisation and social media, resulting in lower subscription numbers and reduced advertising income. Information and communication techniques (ICT) offer new opportunities. Our research group is collaborating with a software developer of news production tools for the international market to explore how social, open, and other data sources can be leveraged for journalistic purposes. We have developed an architecture and prototype called News Hunter that uses knowledge graphs, natural-language processing (NLP), and machine learning (ML) together to support journalists. Our focus is on combining existing data sources and computation and storage techniques into a flexible architecture for news journalism. The paper presents News Hunter along with plans and possibilities for future work.
DAPAS : Denoising Autoencoder to Prevent Adversarial attack in Semantic Segmentation

Nowadays, deep learning techniques show dramatic performance in computer vision areas, and they even outperform humans on complex tasks such as ImageNet classification. But it turns out a deep learning based model is vulnerable to some small perturbation called an adversarial attack. This is a problem in the view of the safety and security of artificial intelligence, which has recently been studied a lot. These attacks have shown that they can easily fool models of image classification, semantic segmentation, and object detection. We focus on the adversarial attack in semantic segmentation tasks since there is little work in this task. We point out this attack can be protected by denoise autoencoder, which is used for denoising the perturbation and restoring the original images. We build a deep denoise autoencoder model for removing the adversarial perturbation and restoring the clean image. We experiment with various noise distributions and verify the effect of denoise autoencoder against adversarial attack in semantic segmentation task.
Multi-expert visual tracking using hierarchical convolutional feature fusion via contextual information

In the literature, numerous techniques have proposed to enhance the performance of tracking the visual objects and each method has its own merits and demerits. For instance, the existing tracking methods may lack in performance due to external disturbances that include background clutter, occlusion, and scale variations. In this article, we propose a multi-expert tracking framework that exploits feature fusion and contextual information of the target to improve the tracking accuracy and robustness. Specifically, we constitute an expert group by ensembling the features extracted from deep convolutional neural networks with different properties. Besides, each expert belonging to the constituted group helps to track target in all frames and the best expert with maximum robustness score is selected in each frame. Then, the contextual information of the target is introduced into the correlation filter to improve performance under complex interference. In addition, to further improve efficiency, more experts can be generated by fusing different type of features which leads to more robustness. Moreover, an adaptive model update strategy is introduced into the correlation filter to discriminate the unreliable samples effectively. Finally, extensive experimental results on OTB2013, OTB2015, TempleColor128 and UAVDT datasets demonstrate that the proposed method performs favourably against state-of-the-art methods.
Using Word2Vec Recommendation for Improved Purchase Prediction

Purchase prediction can help e-commerce planners plan their stock and personalised offers. Word2Vec is a well-known method to explore word relations in sentences for sentiment analysing by creating vector representation of words. Word2Vec models are used in many works for product recommendations. In this paper, we analyse the effect of item similarities in the sessions in purchase prediction performance. We choose the items from different position of the session, and we derive recommendations from selected items using Word2Vec model. We assess the similarities between items by analysing the number of common recommendations of selected items. We train classification algorithms after we include similarity calculations of the selected items as session features. Computational experiments show that using similarity values of the interacted items in the session improves the performance of purchase prediction in terms of F1 score.
Variational Clustering: Leveraging Variational Autoencoders for Image Clustering

Recent advances in deep learning have shown their ability to learn strong feature representations for images. The task of image clustering naturally requires good feature representations to capture the distribution of the data and subsequently differentiate data points from one another. Often these two aspects are dealt with independently and thus traditional feature learning alone does not suffice in partitioning the data meaningfully. Variational Autoencoders (VAEs) naturally lend themselves to learning data distributions in a latent space. Since we wish to efficiently discriminate between different clusters in the data, we propose a method based on VAEs where we use a Gaussian Mixture prior to help cluster the images accurately. We jointly learn the parameters of both the prior and the posterior distributions. Our method represents a true Gaussian Mixture VAE. This way, our method simultaneously learns a prior that captures the latent distribution of the images and a posterior to help discriminate well between data points. We also propose a novel reparametrization of the latent space consisting of a mixture of discrete and continuous variables. One key takeaway is that our method generalizes better across different datasets without using any pre-training or learnt models, unlike existing methods, allowing it to be trained from scratch in an end-to-end manner. We verify our efficacy and generalizability experimentally by achieving state-of-the-art results among unsupervised methods on a variety of datasets. To the best of our knowledge, we are the first to pursue image clustering using VAEs in a purely unsupervised manner on real image datasets.
DeU-Net: Deformable U-Net for 3D Cardiac MRI Video Segmentation

Automatic segmentation of cardiac magnetic resonance imaging (MRI) facilitates efficient and accurate volume measurement in clinical applications. However, due to anisotropic resolution and ambiguous border (e.g., right ventricular endocardium), existing methods suffer from the degradation of accuracy and robustness in 3D cardiac MRI video segmentation. In this paper, we propose a novel Deformable U-Net (DeU-Net) to fully exploit spatio-temporal information from 3D cardiac MRI video, including a Temporal Deformable Aggregation Module (TDAM) and a Deformable Global Position Attention (DGPA) network. First, the TDAM takes a cardiac MRI video clip as input with temporal information extracted by an offset prediction network. Then we fuse extracted temporal information via a temporal aggregation deformable convolution to produce fused feature maps. Furthermore, to aggregate meaningful features, we devise the DGPA network by employing deformable attention U-Net, which can encode a wider range of multi-dimensional contextual information into global and local features. Experimental results show that our DeU-Net achieves the state-of-the-art performance on commonly used evaluation metrics, especially for cardiac marginal information (ASSD and HD).
Event-Oriented 3D Convolutional Features Selection and Hash Codes Generation Using PCA for Video Retrieval

The extensive video surveillance networks gather an enormous amount of data exponentially on a daily basis and its management is a challenging task, requiring efficient and effective techniques for searching, indexing, and retrieval. The employed mainstream techniques are focusing on general category videos, where the important events in surveillance require fine-grained events retrieval. In this paper, we introduce an event-oriented feature selection mechanism by utilizing the intermediate convolutional layer of a pre-trained 3D-CNN model, that is selected after deep investigation of its weights and response to a particular event. The extracted exclusive features represent an event semantically and effectively eliminate those neurons which do not respond to an event. Furthermore, the event-oriented convolutional features are of very high-dimensions, requiring additional storage, and take more time in feature comparison for retrieval. Therefore, we generate compact binary codes from these features using principle component analysis (PCA) algorithm. This makes our system more efficient to retrieve videos from large scale database. We evaluated our approach on the challenging events of UCF101 and HMDB51 datasets for original features and generated compact codes to achieve reduced execution time and better precision and recall scores.
On-road Trajectory Planning with Spatio-temporal RRT* and Always-feasible Quadratic Program

On-road trajectory planning is a critical module in an autonomous driving system. Instead of using a path-velocity decomposition or longitudinal-lateral decomposition strategy, this work aims to find a trajectory directly. We adopt a sampleand-search planner to get a coarse trajectory and then polish it via numerical optimization. Among the predominant sampleand-search planners, most of the sampling operations are not flexible, which inevitably lead to a solution failure if the sampling density is low, and suffer from the curse of dimensionality if the sampling density is set high. This work proposes a modified RRT* for trajectory search, aiming to promote the sampling flexibility and to get rid of the search randomness. A quadratic program (QP) based smoother is proposed to refine the coarse trajectory. Herein, the scale of the QP problem is fixed and tractable, and the feasibility of the QP problem is always guaranteed.
Navigation-based candidate expansion and pretrained language models for citation recommendation

Citation recommendation systems for the scientific literature, to help authors find papers that should be cited, have the potential to speed up discoveries and uncover new routes for scientific exploration. We treat this task as a ranking problem, which we tackle with a two-stage approach: candidate generation followed by reranking. Within this framework, we adapt to the scientific domain a proven combination based on “bag of words” retrieval followed by rescoring with a BERT model. We experimentally show the effects of domain adaptation, both in terms of pretraining on in-domain data and exploiting in-domain vocabulary. In addition, we introduce a novel navigation-based document expansion strategy to enrich the candidate documents fed into our neural models. On three benchmark datasets, our methods achieve or rival the state of the art in the citation recommendation task.
Balancing Exploration and Exploitation: A novel active learner for imbalanced data

Active learning receives great interest from researchers with the aim of reducing the amount of time, cost, and efforts for labeling data in many applications. Active learning aims to generate/select the smallest possible amount of training data that ensures strong classification performance in the test phase. An active learner carries out two main steps: (i) selecting a set of promising queries from unlabeled data, and (ii) annotating the selected queries. Most active learners choose either the most informative or representative instances for annotation. In this paper, we combined these two criteria for query selection. First, in the exploration phase, the proposed algorithm explores the search space and tries in each iteration to visit new regions for better exploration. This improves the capability of exploring the space of minority classes with imbalanced data. Second, in the exploitation phase, the goal is to generate a new point in an uncertain region, which is expected to be around the decision boundaries of the target functions. Some variants of the proposed algorithm do not require any labeled or unlabeled data in advance. There is only comparably few existing work which addresses this scenario. Experiments on synthetic and real datasets with different dimensions and imbalance ratios indicate that the proposed algorithm has significant advantages compared to various well-known active learners.
Semantics-enabled query performance prediction for ad hoc table retrieval

Predicting the performance of a retrieval method for a given query is a highly important and challenging problem in information retrieval. Accurate Query Performance Prediction (QPP) plays an important role in real time handling of queries with varying levels of difficulty. While there have been several successful query performance predictors, no predictors have yet been introduced within the context of the ad hoc table retrieval task, which is concerned with answering a query with a ranked list of tables. In this paper, we propose to perform query performance prediction based on neural embedding techniques for ad hoc table retrieval and introduce three neural features. The neural features are based on neural embedding techniques and leverage the distance between tokens in the embedding space in order to capture their semantic similarity. We evaluate our proposed work based on a gold standard test collection and compare it with the state-of-the-art post-retrieval query performance prediction methods. We find that our neural features (1) are effective for predicting the performance of content-based ranking functions; and not as effective for feature-based ranking functions, and (2) show a synergistic impact on existing QPP methods and hence are able to increase their performance in practice.
Theoretical analysis and experimental validation of volume bias of soft Dice optimized segmentation maps in the context of inherent uncertainty

The clinical interest is often to measure the volume of a structure, which is typically derived from a segmentation. In order to evaluate and compare segmentation methods, the similarity between a segmentation and a predefined ground truth is measured using popular discrete metrics, such as the Dice score. Recent segmentation methods use a differentiable surrogate metric, such as soft Dice, as part of the loss function during the learning phase. In this work, we first briefly describe how to derive volume estimates from a segmentation that is, potentially, inherently uncertain or ambiguous. This is followed by a theoretical analysis and an experimental validation linking the inherent uncertainty to common loss functions for training CNNs, namely cross-entropy and soft Dice. We find that, even though soft Dice optimization leads to an improved performance with respect to the Dice score and other measures, it may introduce a volume bias for tasks with high inherent uncertainty. These findings indicate some of the method's clinical limitations and suggest doing a closer ad-hoc volume analysis with an optional re-calibration step.
MoonLight: A Lightweight Tool for Monitoring Spatio-Temporal Properties

We present MoonLight, a tool for monitoring temporal and spatio-temporal properties of mobile and spatially distributed cyber-physical systems (CPS). In the proposed framework, space is represented as a weighted graph, describing the topological configurations in which the single CPS entities (nodes of the graph) are arranged. Both nodes and edges have attributes modelling physical and logical quantities that can change in time. MoonLight is implemented in Java and supports the monitoring of Spatio-Temporal Reach and Escape Logic (STREL) introduced in []. MoonLight can be used as a standalone command line tool, as a Java API, or via Matlab ™interface. We provide here some examples using the Matlab ™interface and we evaluate the tool performance also by comparing with other tools specialized in monitoring only temporal properties.
Quantum Correlations beyond Entanglement and Discord

Dissimilar notions of quantum correlations have been established, each being motivated through particular applications in quantum information science and each competing for being recognized as the most relevant measure of quantumness. In this contribution, we experimentally realize a form of quantum correlation that exists even in the absence of entanglement and discord. We certify the presence of such quantum correlations via negativities in the regularized two-mode Glauber-Sudarshan function. Our data show compatibility with an incoherent mixture of orthonormal photon-number states, ruling out quantum coherence and other kinds of quantum resources. By construction, the quantumness of our state is robust against dephasing, thus requiring fewer experimental resources to ensure stability. In addition, we theoretically show how multimode entanglement can be activated based on the generated, nonentangled state. Therefore, we implement a robust kind of nonclassical photon-photon correlated state with useful applications in quantum information processing.
Multi-label Classification of Indonesian Hate Speech on Twitter Using Support Vector Machines

Hate speech has become a hot issue as it spreads massively on today's social media with specific targets, categories, and levels. In addition, hate speech can cause social conflict and even genocide. This research proposes a system that classifies hate speech written in Indonesian language on Twitter. It also handles the noisiness of twitter data, such as mixed languages and non-standard text. We not only use Support Vector Machines (SVM) as a classifier, but also compare it with other methods, such as deep learning, CNN and DistilBERT. Apart from standard text preprocessing, we propose to accommodate the effect of translating in handling the multilingual content. The data transformation methods used in the SVM model are Label Power-set (LP) and Classifier Chains (CC). The experiment result shows that the classification using the SVM and CC without stemming, stopword removal, and translation provides the best accuracy of 74.88%. The best SVM hyperparameter on multilabel classification is the sigmoid kernel, the regularization parameter value of 10, and the gamma value of 0.1. Stemming, stopword removal, and translation preprocessing are less effective in this research. Moreover, CNN has a flaw in predicting labels for the training data with a low occurrence rate.
IoT architecture for adaptation to transient devices

IoT environments are continuously changing. Changes may come from the service, connectivity, or physical layers of the IoT architecture. Therefore, to function appropriately, the system needs to dynamically adapt to its environment. In previous work, we posited eight challenges to foster adaptation through all architecture layers of IoT systems. In this paper, we address the challenges to manage the inclusion of new devices and devices' transient connection, by means of dynamic adaptations incorporated into our proposed software architecture for adaptive IoT systems. To manage dynamic adaptations, we extend the reference IoT architecture with our specialized components. In particular, we use (1) ontologies and instances to represent the domain knowledge; (2) a matching algorithm to pair services and IoT devices, taking into account their functional requirements, quality attributes and sensors properties; and (3) a match update algorithm used whenever sensors become (un)available. We evaluate the effectiveness of our solution with respect to the accuracy of matching services and IoT devices, and the response to environment changes.
Revisiting Image-Language Networks for Open-Ended Phrase Detection

Most existing work that grounds natural language phrases in images starts with the assumption that the phrase in question is relevant to the image. In this paper we address a more realistic version of the natural language grounding task where we must both identify whether the phrase is relevant to an image and localize the phrase. This can also be viewed as a generalization of object detection to an open-ended vocabulary, introducing elements of few- and zero-shot detection. We propose an approach for this task that extends Faster R-CNN to relate image regions and phrases. By carefully initializing the classification layers of our network using canonical correlation analysis (CCA), we encourage a solution that is more discerning when reasoning between similar phrases, resulting in over double the performance compared to a naive adaptation on three popular phrase grounding datasets, Flickr30K Entities, ReferIt Game, and Visual Genome, with test-time phrase vocabulary sizes of 5K, 32K, and 159K, respectively.