Reviewerly/exHarmony huggingface dataset:
A Novel Framework for Robustness Analysis of Visual QA Models

Deep neural networks have been playing an essential role in many computer vision tasks including Visual Question Answering (VQA). Until recently, the study of their accuracy was the main focus of research but now there is a trend toward assessing the robustness of these models against adversarial attacks by evaluating their tolerance to varying noise levels. In VQA, adversarial attacks can target the image and/or the proposed main question and yet there is a lack of proper analysis of the later. In this work, we propose a flexible framework that focuses on the language part of VQA that uses semantically relevant questions, dubbed basic questions, acting as controllable noise to evaluate the robustness of VQA models. We hypothesize that the level of noise is negatively correlated to the similarity of a basic question to the main question. Hence, to apply noise on any given main question, we rank a pool of basic questions based on their similarity by casting this ranking task as a LASSO optimization problem. Then, we propose a novel robustness measure Rscore and two largescale basic question datasets (BQDs) in order to standardize robustness analysis for VQA models.
One-Time Shot-Noise Unit Calibration Method for Continuous-Variable Quantum Key Distribution

The shot-noise unit in continuous-variable quantum key distribution plays an important and fundamental role in experimental implementation, as it is used as a normalization parameter that contributes to performing security analyses and distilling key information. However, the traditional calibration procedure and detector model cannot cover all system noise in practical applications, which results in loopholes and influences the practical security. What is more, the traditional procedure is also rather complicated and has difficulty in providing compatibility with automatic operating systems. In this paper, we propose a calibration model based on a proposed trusted detector model, which could naturally close the loopholes in practical applications. It can help to identify the shot-noise unit in only one step, which can not only effectively simplify the evaluation process but also reduce the statistical fluctuations, while two steps are needed in the traditional method. We prove its feasibility and derive a complete version of the corresponding entanglement-based model. A detailed security analysis against arbitrary collective attacks and numerous simulation results in both the asymptotic-limit regime and the finite-size regime are provided. A proof-of-principle experiment is implemented, and the results indicate that our one-time calibration model can be employed as a powerful alternative method for calibrating the shot-noise unit. Our method paves the way for the deployment of continuous-variable quantum key distribution with real-time calibration and automatic operation.
Deep Reinforcement Learning For Modeling Chit-Chat Dialog With Discrete Attributes

Open domain dialog systems face the challenge of being repetitive and producing generic responses.In this paper, we demonstrate that by conditioning the response generation on interpretable discrete dialog attributes and composed attributes, it helps improve the model perplexity and results in diverse and interesting non-redundant responses.We propose to formulate the dialog attribute prediction as a reinforcement learning (RL) problem and use policy gradients methods to optimize utterance generation using long-term rewards.Unlike existing RL approaches which formulate the token prediction as a policy, our method reduces the complexity of the policy optimization by limiting the action space to dialog attributes, thereby making the policy optimization more practical and sample efficient.We demonstrate this with experimental and human evaluations.
Weird Inflects but OK: Making Sense of Morphological Generation Errors

We conduct a manual error analysis of the CoNLL-SIGMORPHON 2017 Shared Task on Morphological Reinflection.In this task, systems are given a word in citation form (e.g., hug) and asked to produce the corresponding inflected form (e.g., the simple past hugged).This design lets us analyze errors much like we might analyze children's production errors.We propose an error taxonomy and use it to annotate errors made by the top two systems across twelve languages.Many of the observed errors are related to inflectional patterns sensitive to inherent linguistic properties such as animacy or affect; many others are failures to predict truly unpredictable inflectional behaviors.We also find nearly one quarter of the residual "errors" reflect errors in the gold data.
A Hierarchical Machine Learning Model to Discover Gleason Grade-Specific Biomarkers in Prostate Cancer

(1) Background:One of the most common cancers that affect North American men and men worldwide is prostate cancer. The Gleason score is a pathological grading system to examine the potential aggressiveness of the disease in the prostate tissue. Advancements in computing and next-generation sequencing technology now allow us to study the genomic profiles of patients in association with their different Gleason scores more accurately and effectively. (2) Methods: In this study, we used a novel machine learning method to analyse gene expression of prostate tumours with different Gleason scores, and identify potential genetic biomarkers for each Gleason group. We obtained a publicly-available RNA-Seq dataset of a cohort of 104 prostate cancer patients from the National Center for Biotechnology Information’s (NCBI) Gene Expression Omnibus (GEO) repository, and categorised patients based on their Gleason scores to create a hierarchy of disease progression. A hierarchical model with standard classifiers in different Gleason groups, also known as nodes, was developed to identify and predict nodes based on their mRNA or gene expression. In each node, patient samples were analysed via class imbalance and hybrid feature selection techniques to build the prediction model. The outcome from analysis of each node was a set of genes that could differentiate each Gleason group from the remaining groups. To validate the proposed method, the set of identified genes were used to classify a second dataset of 499 prostate cancer patients collected from cBioportal. (3) Results: The overall accuracy of applying this novel method to the first dataset was 93.3%; the method was further validated to have 87% accuracy using the second dataset. This method also identified genes that were not previously reported as potential biomarkers for specific Gleason groups. In particular, PIAS3 was identified as a potential biomarker for Gleason score 4 + 3 = 7, and UBE2V2 for Gleason score 6. (4) Insight: Previous reports show that the genes predicted by this newly proposed method strongly correlate with prostate cancer development and progression. Furthermore, pathway analysis shows that both PIAS3 and UBE2V2 share similar protein interaction pathways, the JAK/STAT signaling process.
Context Modulated Dynamic Networks for Actor and Action Video Segmentation with Language Queries

Actor and action video segmentation with language queries aims to segment out the expression referred objects in the video. This process requires comprehensive language reasoning and fine-grained video understanding. Previous methods mainly leverage dynamic convolutional networks to match visual and semantic representations. However, the dynamic convolution neglects spatial context when processing each region in the frame and is thus challenging to segment similar objects in the complex scenarios. To address such limitation, we construct a context modulated dynamic convolutional network. Specifically, we propose a context modulated dynamic convolutional operation in the proposed framework. The kernels for the specific region are generated from both language sentences and surrounding context features. Moreover, we devise a temporal encoder to incorporate motions into the visual features to further match the query descriptions. Extensive experiments on two benchmark datasets, Actor-Action Dataset Sentences (A2D Sentences) and J-HMDB Sentences, demonstrate that our proposed approach notably outperforms state-of-the-art methods.
Learning Diverse Stochastic Human-Action Generators by Learning Smooth Latent Transitions

Human-motion generation is a long-standing challenging task due to the requirement of accurately modeling complex and diverse dynamic patterns. Most existing methods adopt sequence models such as RNN to directly model transitions in the original action space. Due to high dimensionality and potential noise, such modeling of action transitions is particularly challenging. In this paper, we focus on skeleton-based action generation and propose to model smooth and diverse transitions on a latent space of action sequences with much lower dimensionality. Conditioned on a latent sequence, actions are generated by a frame-wise decoder shared by all latent action-poses. Specifically, an implicit RNN is defined to model smooth latent sequences, whose randomness (diversity) is controlled by noise from the input. Different from standard action-prediction methods, our model can generate action sequences from pure noise without any conditional action poses. Remarkably, it can also generate unseen actions from mixed classes during training. Our model is learned with a bi-directional generative-adversarial-net framework, which can not only generate diverse action sequences of a particular class or mix classes, but also learns to classify action sequences within the same model. Experimental results show the superiority of our method in both diverse action-sequence generation and classification, relative to existing methods.
Locally Private <i>k</i>-Means Clustering

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms (SODA)Locally Private k-Means ClusteringUri StemmerUri Stemmerpp.548 - 559Chapter DOI:https://doi.org/10.1137/1.9781611975994.33PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract We design a new algorithm for the Euclidean k-means problem that operates in the local model of differential privacy. Unlike in the non-private literature, differentially private algorithms for the k-means incur both additive and multiplicative errors. Our algorithm significantly reduces the additive error while keeping the multiplicative error the same as in previous state-of-the-art results. Specifically, on a database of size n, our algorithm guarantees O(1) multiplicative error and ≈ n1/2+α additive error for an arbitrarily small constant a > 0. All previous algorithms in the local model had additive error ≈ n2/3+a. We show that the additive error we obtain is almost optimal in terms of its dependency in the database size n. Specifically, we give a simple lower bound showing that every locally-private algorithm for the k-means must have additive error at least ≈ . Previous chapter Next chapter RelatedDetails Published:2020eISBN:978-1-61197-599-4 https://doi.org/10.1137/1.9781611975994Book Series Name:ProceedingsBook Code:PRDA20Book Pages:xxii + 3011
Efficient linear optical generation of a multipartite <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>W</mml:mi></mml:math> state via a quantum eraser

Entanglement among multiple particles is a keystone for not only fundamental research on quantum information but also various practical quantum information applications. In particular, the $W$ state has attracted a lot of attention due to the robustness against particle loss and the applications in multiparty quantum communication. However, it is challenging to generate a photonic $W$ state with a large number of photons, since the $N$-photon $W$ state requires superposition among $N$ probability amplitudes. In this paper, we propose an efficient linear optical scheme to generate $N$-photon $W$ states via quantum erasure. The success probability of our protocol polynomially decreases as the number of photons increases. We also discuss the experimental feasibility of our protocol and anticipate that one can efficiently generate tens of photonic $W$ states with our scheme using currently available quantum photonics technologies.
Utility-Driven Data Analytics on Uncertain Data

Modern Internet of Things (IoT) applications generate massive amounts of data, much of them in the form of objects/items of readings, events, and log entries. Specifically, most of the objects in these IoT data contain rich embedded information (e.g., frequency and uncertainty) and different levels of importance (e.g., unit risk/utility of items, interestingness, cost, or weight). Many existing approaches in data mining and analytics have limitations, such as only the binary attribute is considered within a transaction, as well as all the objects/items having equal weights or importance. To solve these drawbacks, a novel utility-driven data analytics algorithm named HUPNU is presented in this article. As a general utility-driven uncertain data mining model, HUPNU can extract High-Utility patterns by considering both Positive and Negative unit utilities from Uncertain data. The qualified high-utility patterns can be effectively discovered for intrusion detection, risk prediction, manufacturing management, and decision-making, among others. By using the developed vertical Probability-Utility list with the positive and negative utilities structure, as well as several effective pruning strategies, experiments showed that the developed HUPNU approach with the pruning strategies performed great in mining the qualified patterns efficiently and effectively.
Smart Logistics: Blockchain trends and applications

The recent increase of new technologies and their involvement into all management processes call into question the smart logistics current models, in which massive amounts of data is collected and controlled.Smart Logistics is considered as fundamental pillar of the 4th industrial revolution 'industry 4.0'.This revolution is based on different concepts including the blockchain technology.Blockchain remains one of the buzzwords in the technological world.So that all sectors are focus on concrete use cases.However, few actors can boast of having devised revolutionary solutions.For good reasons, blockchain technology is still very complex to understand.The purpose of this study is to define the various applications of Blockchain in Smart Logistics, as well as to present concrete examples of these applications.This work was done by classifying the applications according to four clusters: Information, Transport, Finance, and Management, in addition to presenting the applications of each cluster.
Optimizing Single-Photon Avalanche Photodiodes for Dynamic Quantum Key Distribution Networks

Quantum key distribution (QKD) networks can provide unconditional secure communications among many remote users. In real QKD networks, with the dynamic access and quit of nodes, qualities of various user links and the link lengths among different users, such as transmission loss, are different and changing. Customized optimization that aims at various channel environments offers a network performance benefit. In this paper, we propose including the detection efficiency, dark-count probability, and afterpulse rate of the single-photon avalanche photodiode (SPAD) into the parameter optimization by controlling its voltage and temperature to optimize the secure key rates in dynamic QKD networks. The result shows that our method improves the network adaptability to the variation and asymmetry of channel and profits the establishment of QKD networks in a complex channel environment. This method is suitable for arbitrary SPAD-based QKD system and network architecture.
Multimodal Meme Dataset (MultiOFF) for Identifying Offensive Content in Image and Text

A meme is a form of media that spreads an idea or emotion across the internet. As posting meme has become a new form of communication of the web, due to the multimodal nature of memes, postings of hateful memes or related events like trolling, cyberbullying are increasing day by day. Hate speech, offensive content and aggression content detection have been extensively explored in a single modality such as text or image. However, combining two modalities to detect offensive content is still a developing area. Memes make it even more challenging since they express humour and sarcasm in an implicit way, because of which the meme may not be offensive if we only consider the text or the image. Therefore, it is necessary to combine both modalities to identify whether a given meme is offensive or not. Since there was no publicly available dataset for multimodal offensive meme content detection, we leveraged the memes related to the 2016 U.S. presidential election and created the MultiOFF multimodal meme dataset for offensive content detection dataset. We subsequently developed a classifier for this task using the MultiOFF dataset. We use an early fusion technique to combine the image and text modality and compare it with a text- and an image-only baseline to investigate its effectiveness. Our results show improvements in terms of Precision, Recall, and F-Score. The code and dataset for this paper is published in https://github.com/bharathichezhiyan/Multimodal-Meme-Classification-Identifying-Offensive-Content-in-Image-and-Text
Multi-Scale CNN for Fine-Grained Image Recognition

Most conventional fine-grained image recognitions are based on a two-stream model of object-level and part-level CNNs, where the part-level CNN is responsible for learning the object-parts and their spatial relationships. To train the part-level CNN, we first need to separate parts from an object. However, there exist sub-level objects with no distinctive and separable parts. In this paper, a multi-scale CNN with a baseline Object-level and multiple Part-level CNNs is proposed for the fine-grained image recognition with no separable object-parts. The basic idea to train different CNNs of the multi-scale CNNs is to adopt different scales in resizing the training images. That is, the training images are resized such that the entire object appears as much as possible for the Object-level CNN, while only a local part of the object is to be included for the Part-level CNN. This scale-specific image resizing approach requires a scale-controllable parameter in the image resizing process. In this paper, a scale-controllable parameter is introduced for the linear-scaling and random-cropping method. Also, a line-based image resizing method with a scale-controllable parameter is employed for the part-level CNNs. The proposed multi-scale CNN is applied to a food image classification, which belongs to a fine-grained classification problem with no separable object-parts. Experimental results on the public food image datasets show that the classification accuracy improves substantially when the predicted scores of the multi-scale CNN are fused together. This reveals that the object-level and part-level CNNs work harmoniously in differentiating subtle differences of the sub-level objects.
Identification of QR Code Perspective Distortion Based on Edge Directions and Edge Projections Analysis

QR (quick response) Codes are one of the most popular types of two-dimensional (2D) matrix codes currently used in a wide variety of fields. Two-dimensional matrix codes, compared to 1D bar codes, can encode significantly more data in the same area. We have compared algorithms capable of localizing multiple QR Codes in an image using typical finder patterns, which are present in three corners of a QR Code. Finally, we present a novel approach to identify perspective distortion by analyzing the direction of horizontal and vertical edges and by maximizing the standard deviation of horizontal and vertical projections of these edges. This algorithm is computationally efficient, works well for low-resolution images, and is also suited to real-time processing.
Point Cloud Reduction and Denoising Based on Optimized Downsampling and Bilateral Filtering

For the issue of using the center of gravity during down-sampling, some points of their feature will be lost. We propose a new method, FWD(Farthest point Weighted mean Down-sampling), this method uses down-sampling to find the center of gravity, it is added to the furthest point sampling and performed ten iterations. The obtained 11-point distance is weighted average to find the feature point. Influences of environmental noise and self-noises on the subsequent processing of point cloud are considered. A PWB (Principal component analysis Wavelet function Bilateral Filtering) method is proposed. The normal vector of points is calculated by PCA. The distance between two points in the optimal neighborhood is obtained by the particle swarm optimization(PSO) method. This method performs wavelet smoothing and utilizes the Gaussian function to retain the edge eigenvalues. FWD simplified 90840 points in 48 seconds in the case of retaining the complete feature points. Compared with other latest methods, better results have been obtained. PWB reached de-noising precision of 0.9696 within 72.31s. Accuracy of de-noising is superior to the latest method. The loss of feature points is completed by FWD, the removal of noise is by PWB. Images of de-noising precision prove the priority of the method. The verification shows that the feature points are retained and the noise is eliminated.
Predicting Election Results from Twitter Using Machine Learning Algorithms

Introduction: In the present scenario, the social media network plays a significant role in sharing information between individuals. This incorporates information about news and events that are presently occurring in the worldwide. Anticipating election results is presently turning into a fascinating research topic through social media. In this article, we proposed a strategy to anticipate election results by consolidating sub-event discovery and sentimental analysis in micro-blogs to break down as well as imagine political inclinations uncovered by those social media users. Methods: This approach discovers and investigates sentimental data from micro-blogs to anticipate the popularity of contestants. In general, many organizations and media houses conduct a pre-poll review and obtain expert’s perspectives to anticipate the result of the election, but for our model, we use Twitter data to predict the result of an election by gathering information from Twitter and evaluate it to anticipate the result of the election by analyzing the sentiment of twitter information about the contestants. Results: The number of seats won by the first, second, and third party in AP Assembly Election 2019 has been determined by utilizing Positive Sentiment Scores (PSS’s) of the parties. The actual results of the election and our predicted values of the proposed model are compared, and the outcomes are very close to actual results. We utilized machine learning-based sentimental analysis to discover user emotions in tweets, anticipate sentiment score, and then convert this sentiment score to parties' seat score. Comprehensive experiments are conducted to check out the performance of our model based on a Twitter dataset. Conclusions: Our outcomes state that the proposed model can precisely forecast the election results with accuracy (94.2 %) over the given baselines. The experimental outcomes are very closer to actual election results and contrasted with conventional strategies utilized by various survey agencies for exit polls and approval of results demonstrated that social media data can foresee with better exactness. Discussion: In the future, we might want to expand this work into different areas and nations of the reality where Twitter is picking up prevalence as a political battling tool, and where politicians and individuals are turning towards micro-blogs for political communication and data. We would likewise expand this research into various fields other than general elections and from politicians to state organizations.