Reviewerly/exHarmony huggingface dataset:
Robust Rank-Constrained Sparse Learning: A Graph-Based Framework for Single View and Multiview Clustering

Graph-based clustering aims to partition the data according to a similarity graph, which has shown impressive performance on various kinds of tasks. The quality of similarity graph largely determines the clustering results, but it is difficult to produce a high-quality one, especially when data contain noises and outliers. To solve this problem, we propose a robust rank constrained sparse learning (RRCSL) method in this article. The L2,1 -norm is adopted into the objective function of sparse representation to learn the optimal graph with robustness. To preserve the data structure, we construct an initial graph and search the graph within its neighborhood. By incorporating a rank constraint, the learned graph can be directly used as the cluster indicator, and the final results are obtained without additional postprocessing. In addition, the proposed method cannot only be applied to single-view clustering but also extended to multiview clustering. Plenty of experiments on synthetic and real-world datasets have demonstrated the superiority and robustness of the proposed framework.
A 3-D Multi-Object Path Planning Method for Electric Vehicle Considering the Energy Consumption and Distance

The poor cruising range of electric vehicle (EV) is a problem preventing its popularity. To tackle this problem, methods such as battery technology, energy-based motion control technology are developed. This paper proposes a new solution from the perspective of path planning. Such a solution is called 3-D multi-object path planning method (3D-M method), in which both the energy consumption and distance are considered. The 3D-M method mainly realizes multi-object path planning by an energy consumption estimation model (ECEM) and a distance-integrated estimation model (DIEM). The ECEM can estimate the energy consumption between the neighbour position and the destination on the 3-D map, using a novel slope energy model considering energy consumption characteristic of the EV. The DIEM can estimate the integrated distance which includes the corresponding 2-D distance and 3-D distance, respectively. In the planning process, the outputs of ECEM and DIEM are combined to determine the cost of a path. In addition, a chaos-based multi-object optimizer (CBMOO) is used to search the optimal weights for the 3D-M method. The simulation experiments prove that the proposed method can generate an optimal path which saves much energy in comparison with the path provided by the distance-based method.
Live Gradient Compensation for Evading Stragglers in Distributed Learning

The training efficiency of distributed learning systems is vulnerable to stragglers, namely, those slow worker nodes. A naive strategy is performing the distributed learning by incor-porating the fastest K workers and ignoring these stragglers, which may induce high deviation for non-IID data. To tackle this, we develop a Live Gradient Compensation (LGC) strategy to incorporate the one-step delayed gradients from stragglers, aiming to accelerate learning process and utilize the stragglers simultaneously. In LGC framework, mini-batch data are divided into smaller blocks and processed separately, which makes the gradient computed based on partial work accessible. In addition, we provide theoretical convergence analysis of our algorithm for non-convex optimization problem under non-IID training data to show that LGC-SGD has almost the same convergence error as full synchronous SGD. The theoretical results also allow us to quantify a novel tradeoff in minimizing training time and error by selecting the optimal straggler threshold. Finally, extensive simulation experiments of image classification on CIFAR-10 dataset are conducted, and the numerical results demonstrate the effectiveness of our proposed strategy.
Development and Evaluation of Blockchain based Secure Application for Verification and Validation of Academic Certificates

Academic degrees are subject to corruptions, system flaws, forgeries, and imitations. In this paper we propose to develop a blockchain smart contract-based application using Ethereum Platform, to store, distribute and verify academic certificates. It constitutes a trusted, decentralized certificate’s management system that can offer a unified viewpoint for students, academic institutions, as well as for other potential stakeholders such as employers. The article describes the implementation of three main parts of our proposed solution that includes: verification application, university interface and accreditor interface. This application avoids administrative barriers, makes the process of deployment, verification, and validation of certificates faster, efficient, and more secure. Additionally, it offers confidentiality of the data by using AES encryption algorithm before creating transactions and allows bulk submission of multiple academic certificates.
Cross domain-based ontology construction via Jaccard Semantic Similarity with hybrid optimization model

• Ontology construction model under cross-domain application. • Proposed Jaccard Similarity Evaluation, Data filtering and Outlier Detection. • Semantic annotation, cluster, ontology construction with optimal centroid selection. • Hybrid algorithm of Whale Optimization Algorithm and Rider Optimization Algorithm. Semantic web technology seems to be in the infant stage as only little efforts have been taken on ontology construction with cross-domain application. This paper intends to take an effort on a new workspace, in which the ontology construction model under cross-domain application is performed. The core concern of this work is on two decision-making process namely data filtering and data annotation. Certain process is followed in this work: (i) Preprocessing (ii) Proposed Jaccard Similarity Evaluation (iii) Data filtering and Outlier Detection (iv) Semantic annotation and clustering. More particularly, data filtering is performed based on the evaluated similarity function. The outliers are identified and grouped separately. The data annotation is performed based on the semantics and thereby the clustering process takes place to form the ontology precisely. This clustering process obviously relies to the optimization crisis as the optimal centroid selection becomes the greatest issue. In order to solve this, this paper extends with the introduction of a hybrid algorithm named Circling Insisted-Rider Optimization Algorithm (CI-ROA), which hybrids the concept of Whale Optimization Algorithm (WOA) and Rider Optimization Algorithm (ROA), respectively. Finally, the performance of proposed work is compared and proved over other state-of-the-art models.
D-Score: Holistic Dialogue Evaluation Without Reference

In artistic gymnastics, difficulty score or D-score is used for judging performance. Starting from zero, an athlete earns points from different aspects such as composition requirement, difficulty, and connection between moves. The final score is a composition of the quality of various performance indicators. Similarly, when evaluating dialogue responses, human judges generally follow a number of criteria, among which language fluency, context coherence, logical consistency, and semantic appropriateness are on top of the agenda. In this paper, we propose an automatic dialogue evaluation framework called D-score that resembles the way gymnastics is evaluated. Following the four human judging criteria above, we devise a range of evaluation tasks and model them under a multi-task learning framework. The proposed framework, without relying on any human-written reference, learns to appreciate the overall quality of human-human conversations through a representation that is shared by all tasks without over-fitting to individual task domain. We evaluate D-score by performing comprehensive correlation analyses with human judgement on three dialogue evaluation datasets, among which two are from past DSTC series, and benchmark against state-of-the-art baselines. D-score not only outperforms the best baseline by a large margin in terms of system-level Spearman correlation but also represents an important step towards explainable dialogue scoring.
Research on Data Security in Big Data Cloud Computing Environment

In the big data cloud computing environment, data security issues have become a focus of attention. This paper delivers an overview of conceptions, characteristics and advanced technologies for big data cloud computing. Security issues of data quality and privacy control are elaborated pertaining to data access, data isolation, data integrity, data destruction, data transmission and data sharing. Eventually, a virtualization architecture and related strategies are proposed to against threats and enhance the data security in big data cloud environment.
Learning to Reconstruct 3D Non-Cuboid Room Layout from a Single RGB Image

Single-image room layout reconstruction aims to reconstruct the enclosed 3D structure of a room from a single image. Most previous work relies on the cuboid shape prior. This paper considers a more general indoor assumption, i.e., the room layout consists of a single ceiling, a single floor, and several vertical walls. To this end, we first employ Convolutional Neural Networks to detect planes and vertical lines between adjacent walls. Meanwhile, estimating the 3D parameters for each plane. Then, a simple yet effective geometric reasoning method is adopted to achieve room layout reconstruction. Furthermore, we optimize the 3D plane parameters to reconstruct a geometrically consistent room layout between planes and lines. The experimental results on public datasets validate the effectiveness and efficiency of our method.
Multilingual COVID-QA: Learning towards Global Information Sharing via Web Question Answering in Multiple Languages

Since late December 2019, it has been reported an outbreak of atypical pneumonia, now known as COVID-19 caused by the novel coronavirus. Cases have spread to more than 200 countries and regions internationally. World Health Organization (WHO) officially declares the coronavirus outbreak a pandemic and the public health emergency has caused world-wide impact to daily lives: people are advised to keep social distance, in-person events have been moved online, and some function facilitates have been locked-down. Alternatively, the Web becomes an active venue for people to share information. With respect to the on-going topic, people continuously post questions online and seek for answers. Yet, sharing global information conveyed in different languages is challenging because the language barrier is intrinsically unfriendly to monolingual speakers. In this paper, we propose a multilingual COVID-QA model to answer people's questions in their own languages while the model is able to absorb knowledge from other languages. Another challenge is that in most cases, the information to share does not have parallel data in multiple languages. To this end, we propose a novel framework which incorporates (unsupervised) translation alignment to learn as pseudo-parallel data. Then we train multilingual question-answering mapping and generation. We demonstrate the effectiveness of our proposed approach compared against a series of competitive baselines. In this way, we make it easier to share global information across the language barriers, and hopefully we contribute to the battle against COVID-19.
CopyCat: Near-Duplicates Within and Between the ClueWeb and the Common Crawl

The amount of near-duplicates in web crawls like the ClueWeb or Common Crawl demands from their users either to develop a preprocessing pipeline for deduplication, which is costly both computationally and in person hours, or accepting the undesired effects that near-duplicates have on reliability and validity of experiments. We introduce ChatNoir-CopyCat-21, which simplifies deduplication significantly. It comes in two parts: (1) A compilation of near-duplicate documents within the ClueWeb09, the ClueWeb12, and two Common Crawl snapshots, as well as between selections of these crawls, and (2) a software library that implements the deduplication of arbitrary document sets. Our analysis shows that 14--52, of the documents within a crawl and around~0.7--2.5, between the crawls are near-duplicates. Two showcases demonstrate the application and usefulness of our resource.
Handwritten Digit Recognition Using CNN

The issue of transcribed digit acknowledgment has for some time been an open issue in the field of example order. A few examined have demonstrated that Neural Network has an incredible execution in information arrangement. The fundamental target of this paper is to give effective and solid procedures to acknowledgment of transcribed numerical by looking at different existing arrangement models. This paper thinks about the exhibition of Convolutional Neural Network (CCN). Results demonstrate that CNN classifier beat over Neural Network with critical improved computational effectiveness without relinquishing execution. Handwritten digit recognition can be performed using the Convolutional neural network from Machine Learning. Using the MNIST (Modified National Institute of Standards and Technologies) database and compiling with the CNN gives the basic structure of my project development. So, basically to perform the model we need some libraries such as NumPy, `Pandas', TensorFlow, Keras. These are the main structure on which my main project stands. MNIST data contains about 70,000 images of handwritten digits from 0-9. So, it is a class 10 classification model. This dataset is divided into 2 parts i.e. Training and Test dataset. Image representation as 28*28 matrix where each cell contains grayscale pixel value.
A New JPEG Encryption Scheme Using Adaptive Block Size

An encryption scheme based on adaptive block size is presented in this paper through three different levels of encryption techniques, and all of them are JPEG friendly. First, according to the plain image’s local intensity information, the image is segmented into different sizes of blocks, and then we apply corresponding discrete cosine transform (DCT) to each block. Second, we convert different sizes of blocks to 8\(\times \)8 blocks using coefficients distribution followed by block permutation at the quantization stage. Finally, the segmentation information is embedded in AC coefficients at JPEG’s entropy coding stage for the third-level protection. BLAKE-256 hashing function is employed to generate sensitive keys by inputting the plain image, which has a good effect on resisting known plaintext attack. In our proposed scheme, the biggest DCT block size we apply reaches 64. Experimental results have shown that the proposed method is secure enough and has a better compression ratio and peak signal-to-noise ratio (PSNR) compared with other approaches from literature.
DP-Sniper: Black-Box Discovery of Differential Privacy Violations using Classifiers

We present DP-Sniper, a practical black-box method that automatically finds violations of differential privacy.DP-Sniper is based on two key ideas: (i) training a classifier to predict if an observed output was likely generated from one of two possible inputs, and (ii) transforming this classifier into an approximately optimal attack on differential privacy.Our experimental evaluation demonstrates that DP-Sniper obtains up to 12.4 times stronger guarantees than state-of-the-art, while being 15.5 times faster. Further, we show that DP-Sniper is effective in exploiting floating-point vulnerabilities of naively implemented algorithms: it detects that a supposedly 0.1-differentially private implementation of the Laplace mechanism actually does not satisfy even 0.25-differential privacy.
What Causes Wrong Sentiment Classifications of Game Reviews?

Sentiment analysis is a popular technique to identify the sentiment of a piece of text. Several different domains have been targeted by sentiment analysis research, such as Twitter, movie reviews, and mobile app reviews. Although several techniques have been proposed, the performance of current sentiment analysis techniques is still far from acceptable, mainly when applied in domains on which they were not trained. In addition, the causes of wrong classifications are not clear. In this article, we study how sentiment analysis performs on game reviews. We first report the results of a large-scale empirical study on the performance of widely used sentiment classifiers on game reviews. Then, we investigate the root causes for the wrong classifications and quantify the impact of each cause on the overall performance. We study three existing classifiers: <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Stanford CoreNLP</monospace> , <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NLTK</monospace> , and <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SentiStrength</monospace> . Our results show that most classifiers do not perform well on game reviews, with the best one being <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NLTK</monospace> (with an AUC of 0.70). We also identified four main causes for wrong classifications, such as reviews that point out advantages and disadvantages of the game, which might confuse the classifier. The identified causes are not trivial to be resolved and we call upon sentiment analysis and game researchers and developers to prioritize a research agenda that investigates how the performance of sentiment analysis of game reviews can be improved, for instance by developing techniques that can automatically deal with specific game-related issues of reviews (e.g., reviews with advantages and disadvantages). Finally, we show that training sentiment classifiers on reviews that are stratified by the game genre is effective.
Hierarchical Prediction of Registration Misalignment Using a Convolutional LSTM: Application to Chest CT Scans

In this paper we propose a supervised method to predict registration misalignment using convolutional neural networks (CNNs). This task is casted to a classification problem with multiple classes of misalignment: “correct” 0-3 mm, “poor” 3-6 mm and “wrong” over 6 mm. Rather than a direct prediction, we propose a hierarchical approach, where the prediction is gradually refined from coarse to fine. Our solution is based on a convolutional Long Short-Term Memory (LSTM), using hierarchical misalignment predictions on three resolutions of the image pair, leveraging the intrinsic strengths of an LSTM for this problem. The convolutional LSTM is trained on a set of artificially generated image pairs obtained from artificial displacement vector fields (DVFs). Results on chest CT scans show that incorporating multi-resolution information, and the hierarchical use via an LSTM for this, leads to overall better F1 scores, with fewer misclassifications in a well-tuned registration setup. The final system yields an accuracy of 87.1%, and an average F1 score of 66.4% aggregated in two independent chest CT scan studies.
Efficient Trajectory Planning for UAVs Using Hierarchical Optimization

Automatic generation of feasible trajectory is one of the key technologies for autonomous flying of unmanned aerial vehicles (UAVs).The existing path planning methods, such as swarm intelligence algorithm and graph-based algorithm, cannot incorporate the flying time and UAV dynamic model into evolution.To overcome such disadvantages, a hierarchical trajectory optimization scheme consisted by improved particle swarm optimization (PSO) and Gauss pseudo-spectral method (GPM) is investigated in this paper.Firstly, considering that traditional GPM is sensitive to initial values, we design an improved PSO for path planning in the first layer.By introducing adaptive parameter adjustment strategy and position mutation updating strategy, the rapidity and optimality of the improved PSO is enhanced.Then in the second layer, a fitted curve based on the path waypoints generated by improved PSO is constructed and served as the initial values for GPM.Comparing with random initial values, the designed curve can significant improve GPM efficiency.A multi-segment strategy is also put forward to further improve the efficiency.Finally, with the consideration of dynamic model and state constraints, the time minimum trajectory planning for quadrotor UAVs is solved.Plenty of simulations are carried out and the results illustrate that the proposed scheme guarantees much better efficiency.
Support vector machines with the known feature-evolution priors

In traditional SVMs (support vector machines), each feature involved in an object is assumed to contribute equally to the classification accuracy. However, the equal importance assumption for each feature is inappropriate for classifying the objects. The existing weighting strategies usually utilize the fixed scaling factors to measure the importance of each feature and hence they are not good fits for feature evolution. In this paper, with the assumption that a Known Feature-Evolution Prior (KFEP) can be employed to capture importance distribution from the perspective of both feature granularity and object granularity, SVM-based machines called KFEP-SVMs are developed to capture the importance of features dynamically. Also, it is revealed that SVMs with the known feature-evolution priors are equivalent to SVMs on a higher feature space. Besides, we theoretically demonstrate that the proposed machines KFEP-SVMs with the known feature-evolution prior indeed have enhanced generalization ability, compared with the classic SVMs. Experimental results show the effectiveness of the proposed machines KFEP-SVMs.
Early Gesture Recognition With Reliable Accuracy Based on High-Resolution IoT Radar Sensors

Early recognition of gestures gives a better user experience during human–computer interaction in a near real-time way. However, the inadequate information accumulated in the early stages of the gesture will make the machine ambiguous and respond wrongly. Therefore, the challenge of early recognition lies in choosing the right time to trigger gesture prediction and using limited information to achieve reliable accuracy. In this article, an early gesture recognition method is proposed to achieve almost the same accuracy as a complete gesture sequence. For this purpose, we design a new architecture to modify the current feature sequence according to the existing information and introduce a new loss function to maximize the probability of correct gesture label as early as possible. At the same time, gesture recognition is triggered when the maximum probability of model output is greater than a set threshold. The publicly available data set used for evaluation comes from Google’s Project Soli sensor. Experimental results show that the proposed method for early recognition can achieve a gesture recognition rate of 88.85% with an average time saving of 69.47%. Compared with the results of all sequence recognition, the recognition rate drops by only 1.7%. In addition, the proposed method based on the data set constructed by terahertz radar also shows similar early recognition performance and can be applied to other radar sensors.