Reviewerly/exHarmony huggingface dataset:
Convolutional Neural Network for Visual Security Evaluation

The visual security index (VSI) is a quantized indicator for objective visual security evaluation of selectively encrypted images. One challenging problem in current research is that the performance of VSIs is highly sensitive to the extracted features and the method of similarity measurement, and it is hard to choose appropriate handcrafted features from encrypted images, as well as to find an effective similarity measurement. In this paper, we make the first attempt to present a novel convolutional neural network-based visual security index (CNNVSI). Our proposed CNNVSI is purely data-driven and trained end-to-end. We propose three specialized designs to make the approach work for encrypted low-quality images without any handcrafted features or prior knowledge about the human vision system (HVS). First, we present a patch labeling algorithm to assign each encrypted patch a visual security score. Second, we design a multiscale attention residual network (MARNet) for feature learning. Last, we propose to fuse the learned features from plain images, encrypted images and their discrepancy images. Extensive and systematic experiments are conducted on five publicly available image databases to analyze the performance of our proposed CNNVSI, and the experimental results and their analysis demonstrate that our proposed CNNVSI significantly outperforms the existing state-of-the-art methods in terms of accuracy and stability.
Decomposing word embedding with the capsule network

Word sense disambiguation tries to learn the appropriate sense of an ambiguous word in a given context. The existing pre-trained language methods and the methods based on multi-embeddings of word did not explore the power of the unsupervised word embedding sufficiently. In this paper, we discuss a capsule network-based approach, taking advantage of capsule’s potential for recognizing highly overlapping features and dealing with segmentation. We propose a capsule network-based method to decompose the unsupervised word embedding of an ambiguous word into context specific sense embedding, called CapsDecE2S. In this approach, the unsupervised ambiguous embedding is fed into capsule network to produce its multiple morpheme-like vectors, which are defined as the basic semantic language units of meaning. With attention operations, CapsDecE2S integrates the word context to reconstruct the multiple morpheme-like vectors into the context-specific sense embedding. To train CapsDecE2S, we propose a sense matching training method. In this method, we convert the sense learning into a binary classification that explicitly learns the relation between senses by the label of matching and non-matching. The CapsDecE2S was experimentally evaluated on two sense learning tasks, i.e., word in context and word sense disambiguation. Results on two public corpora Word-in-Context and English all-words Word Sense Disambiguation show that, the CapsDecE2S model achieves the new state-of-the-art for the word in context and word sense disambiguation tasks. The source code can be downloaded from the Github page1 .
Accurately Quantifying a Billion Instances per Second

Quantification is a thriving research area that develops methods to estimate the class prior probabilities in an unlabelled set of observations. Quantification and classification share several similarities. For instance, the most straightforward quantification method, Classify & Count (CC), directly counts the output of a classifier. However, CC has a systematic bias that makes it increasingly misestimate the counts as the class distribution drifts away from a distribution it perfectly quantifies. This issue has motivated the development of more reliable quantification methods. Such newer methods can consistently outperform CC at the cost of a significant increase in processing requirements. Yet, for a large number of applications, quantification speed is an additional criterion that must be considered. Frequently, quantification methods need to deal with large amounts of data or fast-paced streams, as it is the case of news feeding, tweets and sensor data. In this paper, we propose Sample Mean Matching (SMM), a highly efficient algorithm able to quantify billions of data instances per second. We compare SMM to a set of 14 established and state-of-the-art quantifiers in an empirical analysis comprising 25 benchmark and real-world datasets. We show that SMM is competitive with state-of-the-art methods with no statistical difference in counting accuracy, and it is orders of magnitude faster than the vast majority of the algorithms.
Unsupervised method of clustering and labeling of the online product based on reviews

This paper presents an unsupervised approach to cluster reviews of products collected from Amazon and then generates its labels of each cluster. Instead of using a complete review, this paper splits a review into sentences and considers all sentences from the reviews as inputs for Clustering. Hierarchical Agglomerative Clustering (HAC) is used to cluster sentences. The approaches of cluster labeling are also unsupervised. For labeling, three different methods have been used to find a limited number of essential words for each cluster. Extracted essential words are used to construct phrases. Constructed phrases are used as labels for each cluster. This paper compares the result of the labeling method with baseline labeling. In the result evaluation, all the labeling methods outperform the baseline method. The aim of this research is cluster labeling that makes a set of labels to describe a cluster content and distinguishes the labels from other cluster labels.
Field surveillance of fuel dispensers using IoT-based metering and blockchains

The field surveillance of fuel dispensers is an activity of Legal Metrology that checks these measuring instruments' correct behavior. However, it constitutes a complex challenge because malicious entities can tamper with fuel dispensers to get undue economic advantages. This paper proposes a distributed and decentralized solution. We use IoT-based vehicle simple meters to estimate the fuel amount in refilling events. Although these estimates can be inaccurate, we explore properties of the Law of the Large Numbers to evaluate the fuel dispenser's accuracy. We also use blockchains to avoid collusion attacks and provide a truly distributed and decentralized surveillance solution that implements statistical surveillance analysis as smart contracts. We develop a case study based on the vehicular fleet and fuel dispensers in São Paulo, Brazil. We perform our experiment using the Hyperledger Fabric platform with Byzantine fault-tolerant consensus. In a hypothetical scenario where vehicular meters present error rates below 5%, and each vehicle refuels more than ten times on average, we can identify tampered fuel dispensers with sensitivity and specificity over 95%. We also demonstrate that our blockchain deployment can support a workload of 600 concurrent clients with a throughput higher than 350 tps and latency lower than 1 s. These results attest to our framework suitability in terms of accuracy and performance. They provide promising perspectives on using our idea in the metrological surveillance of other measuring instruments.
DSNet: an efficient CNN for road scene segmentation

Road scene understanding is a critical component in an autonomous driving system. Although the deep learning-based road scene segmentation can achieve very high accuracy, its complexity is also very high for developing real-time applications. It is challenging to design a neural net with high accuracy and low computational complexity. To address this issue, we investigate the advantages and disadvantages of several popular convolutional neural network (CNN) architectures in terms of speed, storage, and segmentation accuracy. We start from the fully convolutional network with VGG, and then we study ResNet and DenseNet. Through detailed experiments, we pick up the favorable components from the existing architectures and at the end, we construct a light-weight network architecture based on the DenseNet. Our proposed network, called DSNet, demonstrates a real-time testing (inferencing) ability (on the popular GPU platform) and it maintains an accuracy comparable with most previous systems. We test our system on several datasets including the challenging Cityscapes dataset (resolution of 1024 × 512) with an Mean Intersection over Union (mIoU) of about 69.1% and runtime of 0.0147 s/image on a single GTX 1080Ti. We also design a more accurate model but at the price of a slower speed, which has an mIoU of about 72.6% on the CamVid dataset.
Fast Graph Filters for Decentralized Subspace Projection

A number of inference problems with sensor networks involve projecting a measured signal onto a given subspace. In existing decentralized approaches, sensors communicate with their local neighbors to obtain a sequence of iterates that asymptotically converges to the desired projection. In contrast, the present paper develops methods that produce these projections in a finite and approximately minimal number of iterations. Building upon tools from graph signal processing, the problem is cast as the design of a graph filter which, in turn, is reduced to the design of a suitable graph shift operator. Exploiting the eigenstructure of the projection and shift matrices leads to an objective whose minimization yields approximately minimum-order graph filters. To cope with the fact that this problem is not convex, the present work introduces a novel convex relaxation of the number of distinct eigenvalues of a matrix based on the nuclear norm of a Kronecker difference. To tackle the case where there exists no graph filter capable of implementing a certain subspace projection with a given network topology, a second optimization criterion is presented to approximate the desired projection while trading the number of iterations for approximation error. Two algorithms are proposed to optimize the aforementioned criteria based on the alternating-direction method of multipliers. An exhaustive simulation study demonstrates that the obtained filters can effectively obtain subspace projections markedly faster than existing algorithms.
Clone Detection in Secure Messaging: Improving Post-Compromise Security in Practice

We investigate whether modern messaging apps achieve the strong post-compromise security guarantees offered by their underlying protocols. In particular, we perform a black-box experiment in which a user becomes the victim of a clone attack; in this attack, the user's full state (including identity keys) is compromised by an attacker who clones their device and then later attempts to impersonate them, using the app through its user interface.
Unselfie: Translating Selfies to Neutral-Pose Portraits in the Wild

Due to the ubiquity of smartphones, it is popular to take photos of one's self, or "selfies." Such photos are convenient to take, because they do not require specialized equipment or a third-party photographer. However, in selfies, constraints such as human arm length often make the body pose look unnatural. To address this issue, we introduce unselfie, a novel photographic transformation that automatically translates a selfie into a neutral-pose portrait. To achieve this, we first collect an unpaired dataset, and introduce a way to synthesize paired training data for self-supervised learning. Then, to unselfie a photo, we propose a new three-stage pipeline, where we first find a target neutral pose, inpaint the body texture, and finally refine and composite the person on the background. To obtain a suitable target neutral pose, we propose a novel nearest pose search module that makes the reposing task easier and enables the generation of multiple neutral-pose results among which users can choose the best one they like. Qualitative and quantitative evaluations show the superiority of our pipeline over alternatives.
Bayesian Inference by Symbolic Model Checking

This paper applies probabilistic model checking techniques for discrete Markov chains to inference in Bayesian networks. We present a simple translation from Bayesian networks into tree-like Markov chains such that inference can be reduced to computing reachability probabilities. Using a prototypical implementation on top of the Storm model checker, we show that symbolic data structures such as multi-terminal BDDs (MTBDDs) are very effective to perform inference on large Bayesian network benchmarks. We compare our result to inference using probabilistic sentential decision diagrams and vtrees, a scalable symbolic technique in AI inference tools.
Tangibility is Overrated: Comparing Learning Experiences of Physical Setups and their Virtual Equivalent in Augmented Reality

Augmented Reality (AR) is gaining increasing importance in science, education, and entertainment. A fundamental characteristic of AR is blending the virtual and physical world into a coherent environment. In this paper, we examine the effect of substituting the physical components of lab experiments with tangible replicas and virtual representations. We conducted a user study with thirty participants who carried out the experiment in three different abstraction levels (original lab equipment, non-functional tangible props, virtual representation). We compared the users’ performance regarding setup time, experienced workload, quality of measurements, and concept comprehension of the learning task. We found no effect on comprehension but significant differences in setup time and quality of measures. The results indicate that substitution reduces the experiment setup duration without affecting knowledge transfer. These results help to shape future AR learning environments, and we offer insights for creating complex mixed reality learning materials.
Visual Analysis of Ontology Matching Results with the MELT Dashboard

In this demo, we introduce MELT Dashboard, an interactive Web user interface for ontology alignment evaluation which is created with the existing Matching EvaLuation Toolkit (MELT). Compared to existing, static evaluation interfaces in the ontology matching domain, our dashboard allows for interactive self-service analyses such as a drill down into the matcher performance for data type properties or into the performance of matchers within a certain confidence threshold. In addition, the dashboard offers detailed group evaluation capabilities that allow for the application in broad evaluation campaigns such as the Ontology Alignment Evaluation Initiative (OAEI).
An Improved Fast Convergent Artificial Bee Colony Algorithm for Unmanned Aerial Vehicle Path Planning in Battlefield Environment

Unmanned aerial vehicle (UAV) path planning aims to seek an optimal path from the starting point to the terminal point with the threats and constraints in the battlefield environment well considered. In this work, an improved fast convergent artificial bee colony algorithm (FC-ABC) is proposed for this optimization problem. In the proposed FC-ABC algorithm, the physical constraints information of UAV is fully utilized when exploring the solution space. In the process of population initialization, an innovative method is introduced to ensure the diversity of the population and improve the search efficiency. In the neighborhood exploration phase, a heuristic adaptive neighborhood search method combining physical constraints and optimal nectar source information is applied to accelerate the algorithm convergence and improve the accuracy of the optimal solution. During the selection of employed bee, the ε-Boltzmann selection strategy is adopted to enhance the global search capability and avoid falling into local optimization. Simulation results demonstrate that the FCABC algorithm shows stronger stability, faster convergence and higher convergence accuracy than the conventional ABC algorithm and two other modified ABC algorithms.
Building ICU In-hospital Mortality Prediction Model with Federated Learning

In-hospital mortality prediction is a crucial task in the clinical settings. Nevertheless, individual hospitals alone often have limited amount of local data to build a robust model. Usually domain transfer of an in-hospital mortality prediction model built with publicly-accessible dataset is conducted. The study in [6] shows quantitatively that with more datasets from different hospitals being shared, the generalizability and performance of domain transfer improves. We see this as an area that Federated Learning could help. It enables collaborative modelling to take place in a decentralized manner, without the need for aggregating all datasets in one place. This chapter reports a recent pilot of building an in-hospital mortality model with Federated Learning. It empirically shows that Federated Learning does achieve a similar level of performance with centralized training, but with additional benefit of no dataset exchanging among different hospitals. It also compares the performance of two common federated aggregation algorithms empirically in the Intensive Care Unit (ICU) setting, namely FedAvg and FedProx.
Learning a Deep Regression Forest for Head Pose Estimation from a Single Depth Image

Robust head pose estimation significantly improves the performance of applications related to face analysis in Cyber-Physical Systems (CPS) such as driving assistance and expression recognition. However, there exist two main challenges in this issue, i.e., the large pose variations and the property of inhomogeneous facial feature space. Head pose in large variations makes the distinguished facial features, such as nose or lips, invisible, especially in extreme cases. Additionally, features extracted from a head do not change in a stationary manner with respect to the head pose, which results in an inhomogeneous feature space. To deal with the above problems, we propose an end-to-end framework to estimate the head pose from a single depth image. To be specific, the PointNet network is adopted to automatically select distinguished facial feature points from visible surface of a head and to extract discriminative features. The Deep Regression Forest is utilized to handle the nonstationary property of the facial feature space and to learn the head pose distributions. Experimental results show that our proposed method achieves the state-of-the-art performance on the Biwi Kinect Head Pose Dataset, the Pandora Dataset and the ICT-3DHP Dataset.
Mixed-Level Neural Machine Translation

Building the first Russian-Vietnamese neural machine translation system, we faced the problem of choosing a translation unit system on which source and target embeddings are based. Available homogeneous translation unit systems with the same translation unit on the source and target sides do not perfectly suit the investigated language pair. To solve the problem, in this paper, we propose a novel heterogeneous translation unit system, considering linguistic characteristics of the synthetic Russian language and the analytic Vietnamese language. Specifically, we decrease the embedding level on the source side by splitting token into subtokens and increase the embedding level on the target side by merging neighboring tokens into supertoken. The experiment results show that the proposed heterogeneous system improves over the existing best homogeneous Russian-Vietnamese translation system by 1.17 BLEU. Our approach could be applied to building translation bots for language pairs with different linguistic characteristics.
Detect Professional Malicious User With Metric Learning in Recommender Systems

In e-commerce, online retailers are usually suffering from professional malicious users (PMUs), who utilize negative reviews and low ratings to their consumed products on purpose to threaten the retailers for illegal profits. PMUs are difficult to be detected because they utilize masking strategies to disguise themselves as normal users. Specifically, there are three challenges for PMU detection: 1) professional malicious users do not conduct any abnormal or illegal interactions (they never concurrently leave too many negative reviews and low ratings at the same time), and they conduct masking strategies to disguise themselves. Therefore, conventional outlier detection methods are confused by their masking strategies. 2) the PMU detection model should take both ratings and reviews into consideration, which makes PMU detection a multi-modal problem. 3) there are no datasets with labels for professional malicious users in public, which makes PMU detection an unsupervised learning problem. To this end, we propose an unsupervised multi-modal learning model: MMD, which employs Metric learning for professional Malicious users Detection with both ratings and reviews. MMD first utilizes a modified RNN to project the informational review into a sentiment score, which jointly considers the ratings and reviews. Then professional malicious user profiling (MUP) is proposed to catch the sentiment gap between sentiment scores and ratings. MUP filters the users and builds a candidate PMU set. We apply a metric learning-based clustering to learn a proper metric matrix for PMU detection. Finally, we can utilize this metric and labeled users to detect PMUs. Specifically, we apply the attention mechanism in metric learning to improve the model’s performance. The extensive experiments in four datasets demonstrate that our proposed method can solve this unsupervised detection problem. Moreover, the performance of the state-of-the-art recommender models is enhanced by taking MMD as a preprocessing stage.
Colorization of Line Drawings with Empty Pupils

Abstract Many studies have recently applied deep learning to the automatic colorization of line drawings. However, it is difficult to paint empty pupils using existing methods because the convolutional neural network are trained with pupils that have edges, which are generated from color images using image processing. Most actual line drawings have empty pupils that artists must paint in. In this paper, we propose a novel network model that transfers the pupil details in a reference color image to input line drawings with empty pupils. We also propose a method for accurately and automatically colorizing eyes. In this method, eye patches are extracted from a reference color image and automatically added to an input line drawing as color hints using our pupil position estimation network.
Hierarchical X-Ray Report Generation via Pathology Tags and Multi Head Attention

Examining radiology images, such as X-Ray images as accurately as possible, forms a crucial step in providing the best healthcare facilities. However, this requires high expertise and clinical experience. Even for experienced radiologists, this is a time-consuming task. Hence, the automated generation of accurate radiology reports from chest X-Ray images is gaining popularity. Compared to other image captioning tasks where coherence is the key criterion, medical image captioning requires high accuracy in detecting anomalies and extracting information along with coherence. That is, the report must be easy to read and convey medical facts accurately. We propose a deep neural network to achieve this. Given a set of Chest X-Ray images of the patient, the proposed network predicts the medical tags and generates a readable radiology report. For generating the report and tags, the proposed network learns to extract salient features of the image from a deep CNN and generates tag embeddings for each patient's X-Ray images. We use transformers for learning self and cross attention. We encode the image and tag features with self-attention to get a finer representation. Use both the above features in cross attention with the input sequence to generate the report's Findings. Then, cross attention is applied between the generated Findings and the input sequence to generate the report's Impressions. We use a publicly available dataset to evaluate the proposed network. The performance indicates that we can generate a readable radiology report, with a relatively higher BLEU score over SOTA. The code and trained models are available at https://medicalcaption.github.io.