Reviewerly/exHarmony huggingface dataset:
Fine-Grained Instance-Level Sketch-Based Video Retrieval

Existing sketch-analysis work studies sketches depicting static objects or scenes. In this work, we propose a novel cross-modal retrieval problem of fine-grained instance-level sketch-based video retrieval (FG-SBVR), where a sketch sequence is used as a query to retrieve a specific target video instance. Compared with sketch-based still image retrieval, and coarse-grained category-level video retrieval, this is more challenging as both visual appearance and motion need to be simultaneously matched at a fine-grained level. We contribute the first FG-SBVR dataset with rich annotations. We then introduce a novel multi-stream multi-modality deep network to perform FG-SBVR under both strong and weakly supervised settings. The key component of the network is a relation module, designed to prevent model overfitting given scarce training data. We show that this model significantly outperforms a number of existing state-of-the-art models designed for video analysis.
Focus on the Visible Regions: Semantic-Guided Alignment Model for Occluded Person Re-Identification

The occlusion problem is very common in pedestrian retrieval scenarios. When persons are occluded by various obstacles, the noise caused by the occluded area greatly affects the retrieval results. However, many previous pedestrian re-identification (Re-ID) methods ignore this problem. To solve it, we propose a semantic-guided alignment model that uses image semantic information to separate useful information from occlusion noise. In the image preprocessing phase, we use a human semantic parsing network to generate probability maps. These maps show which regions of images are occluded, and the model automatically crops images to preserve the visible parts. In the construction phase, we fuse the probability maps with the global features of the image, and semantic information guides the model to focus on visible human regions and extract local features. During the matching process, we propose a measurement strategy that only calculates the distance of public areas (visible human areas on both images) between images, thereby suppressing the spatial misalignment caused by non-public areas. Experimental results on a series of public datasets confirm that our method outperforms previous occluded Re-ID methods, and it achieves top performance in the holistic Re-ID problem.
Improved Faster RCNN Based on Feature Amplification and Oversampling Data Augmentation for Oriented Vehicle Detection in Aerial Images

Vehicles in aerial images are generally with small sizes and unbalanced number of samples, which leads to the poor performances of the existing vehicle detection algorithms. Therefore, an oriented vehicle detection framework based on improved Faster RCNN is proposed for aerial images. First of all, we propose an oversampling and stitching data augmentation method to decrease the negative effect of category imbalance in the training dataset and construct a new dataset with balanced number of samples. Then considering that the pooling operation may loss the discriminative ability of features for small objects, we propose to amplify the feature map so that detailed information hidden in the last feature map can be enriched. Finally, we design a joint training loss function including center loss for both horizontal and oriented bounding boxes, and reduce the impact of small inter-class diversity on vehicle detection. The proposed framework is evaluated on the VEDAI dataset that consists of 9 vehicle categories. The experimental results show that the proposed framework outperforms previous approaches with a mean average precision of 60.4% and 60.1% in detecting horizontal and oriented bounding boxes respectively, which is about 8% better than Faster RCNN.
Deterministic Generation of Large Fock States

We present a protocol to deterministically prepare the electromagnetic field in a large photon number state. The field starts in a coherent state and, through resonant interaction with one or few two-level systems, it evolves into a coherently displaced Fock state, without any post-selection. We show the feasibility of the scheme under realistic parameters. The presented method opens a door to reach Fock states with $n\sim100$ and optimal fidelities above $70$%, blurring the line between macroscopic and quantum states of the field.
On the need of preserving order of data when validating within-project defect classifiers

Abstract We are in the shoes of a practitioner who uses previous project releases’ data to predict which classes of the current release are defect-prone. In this scenario, the practitioner would like to use the most accurate classifier among the many available ones. A validation technique, hereinafter “technique”, defines how to measure the prediction accuracy of a classifier. Several previous research efforts analyzed several techniques. However, no previous study compared validation techniques in the within-project across-release class-level context or considered techniques that preserve the order of data. In this paper, we investigate which technique recommends the most accurate classifier. We use the last release of a project as the ground truth to evaluate the classifier’s accuracy and hence the ability of a technique to recommend an accurate classifier. We consider nine classifiers, two industry and 13 open projects, and three validation techniques: namely 10-fold cross-validation (i.e., the most used technique), bootstrap (i.e., the recommended technique), and walk-forward (i.e., a technique preserving the order of data). Our results show that: 1) classifiers differ in accuracy in all datasets regardless of their entity per value, 2) walk-forward outperforms both 10-fold cross-validation and bootstrap statistically in all three accuracy metrics: AUC of the selected classifier, bias and absolute bias, 3) surprisingly, all techniques resulted to be more prone to overestimate than to underestimate the performances of classifiers, and 3) the defect rate resulted in changing between the second and first half in both industry projects and 83% of open-source datasets. This study recommends the use of techniques that preserve the order of data such as walk-forward over 10-fold cross-validation and bootstrap in the within-project across-release class-level context given the above empirical results and that walk-forward is by nature more simple, inexpensive, and stable than the other two techniques.
Text documents clustering using data mining techniques

Increasing progress in numerous research fields and information technologies, led to an increase in the publication of research papers. Therefore, researchers take a lot of time to find interesting research papers that are close to their field of specialization. Consequently, in this paper we have proposed documents classification approach that can cluster the text documents of research papers into the meaningful categories in which contain a similar scientific field. Our presented approach based on essential focus and scopes of the target categories, where each of these categories includes many topics. Accordingly, we extract word tokens from these topics that relate to a specific category, separately. The frequency of word tokens in documents impacts on weight of document that calculated by using a numerical statistic of term frequency-inverse document frequency (TF-IDF). The proposed approach uses title, abstract, and keywords of the paper, in addition to the categories topics to perform the classification process. Subsequently, documents are classified and clustered into the primary categories based on the highest measure of cosine similarity between category weight and documents weights.
Measuring Decentrality in Blockchain Based Systems

Blockchain promises to provide a distributed and decentralized means of trust among untrusted users.However, in recent years, a shift from decentrality to centrality has been observed in the most accepted Blockchain system, i.e., Bitcoin.This shift has motivated researchers to identify the cause of decentrality, quantify decentrality and analyze the impact of decentrality.In this work, we take a holistic approach to identify and quantify decentrality in Blockchain based systems.First, we identify the emergence of centrality in three layers of Blockchain based systems, namely governance layer, network layer and storage layer.Then, we quantify decentrality in these layers using various metrics.At the governance layer, we measure decentrality in terms of fairness, entropy, Gini coefficient, Kullback-Leibler divergence, etc.Similarly, in the network layer, we measure decentrality by using degree centrality, betweenness centrality and closeness centrality.At the storage layer, we apply a distribution index to define centrality.Subsequently, we evaluate the decentrality in Bitcoin and Ethereum networks and discuss our observations.We noticed that, with time, both Bitcoin and Ethereum networks tend to behave like centralized systems where a few nodes govern the whole network.
A Scheme of Color Image Multithreshold Segmentation Based on Improved Moth-Flame Algorithm

A recently developed swarm intelligence algorithm by studying the natural moth's biological behavior is called Moth-Flame Optimization (MFO). The advantages of MFO conclude a simple structure and a robust selection capability. Still, it is easy to be trapped falling into optimal local, and slow search converges. This study suggests a new process improving MFO by hybridizing Lévy flight and logarithmic functions for its formula of flame updating to enhance the optimization performance of the algorithm. In the experimental section, a set of benchmark functions of CEC2013 and the multi threshold image segmentation are used to evaluate the proposed method performance. Compared results of the proposed methods with the different algorithms in the same condition scenarios show that the suggested approach provides better results than the various algorithms in the competitions.
An experimental study on classification of thyroid histopathology images using transfer learning

CAD systems for histopathology image analysis using machine learning is a well researched subject. Deep learning is playing a major role in advancing this research in the recent years. This paper presents an automated thyroid histopathology image classification system with deep neural networks using the theory of transfer learning and popular pre-trained convolutional neural networks (CNNs). In this experiment-based study, two forms of transfer learning namely feature extraction and fine tuning are applied on popular state-of-the-art CNN architectures such as VGGNet, ResNet, InceptionNet and DenseNet to classify thyroid histopathology images. Accuracy, precision, sensitivity, specificity, area under receiver operating characteristic (AUROC) analysis and F1-score are used to evaluate the performance of the architectures. The results are promising and demonstrate the feasibility of transfer learning for thyroid histopathology image analysis.
Hate Speech Detection in Twitter using Transformer Methods

Social media networks such as Twitter are increasingly utilized to propagate hate speech while facilitating mass communication.Recent studies have highlighted a strong correlation between hate speech propagation and hate crimes such as xenophobic attacks.Due to the size of social media and the consequences of hate speech in society, it is essential to develop automated methods for hate speech detection in different social media platforms.Several studies have investigated the application of different machine learning algorithms for hate speech detection.However, the performance of these algorithms is generally hampered by inefficient sequence transduction.The Vanilla recurrent neural networks and recurrent neural networks with attention have been established as state-of-the-art methods for the assignments of sequence modeling and sequence transduction.Unfortunately, these methods suffer from intrinsic problems such as long-term dependency and lack of parallelization.In this study, we investigate a transformer-based method and tested it on a publicly available multiclass hate speech corpus containing 24783 labeled tweets.DistilBERT transformer method was compared against attention-based recurrent neural networks and other transformer baselines for hate speech detection in Twitter documents.The study results show that DistilBERT transformer outperformed the baseline algorithms while allowing parallelization.
Grid Search Tuning of Hyperparameters in Random Forest Classifier for Customer Feedback Sentiment Prediction

Text classification is a common task in machine learning.One of the supervised classification algorithm called Random Forest has been generally used for this task.There is a group of parameters in Random Forest classifier which need to be tuned.If proper tuning is performed on these hyperparameters, the classifier will give a better result.This paper proposes a hybrid approach of Random Forest classifier and Grid Search method for customer feedback data analysis.The tuning approach of Grid Search is applied for tuning the hyperparameters of Random Forest classifier.The Random Forest classifier is used for customer feedback data analysis and then the result is compared with the results which get after applying Grid Search method.The proposed approach provided a promising result in customer feedback data analysis.The experiments in this work show that the accuracy of the proposed model to predict the sentiment on customer feedback data is greater than the performance accuracy obtained by the model without applying parameter tuning.
Evaluating visitor experience of digital interpretation and presentation technologies at cultural heritage sites: a case study of the old town, Zuoying

Abstract Digital technologies are increasingly used in the interpretation and presentation process at cultural heritage sites, shifting the manner of heritage display from previous ways, which tended to be monolithic and static, to a more diversified, experiential, and interactive approach and enabling new connections between heritage and the public. Digital experience is gradually becoming an important component of visitors’ heritage experience. However, little attention has been paid to the influence and effects that these technologies have on the visitor experience, and a corresponding measurement framework for the heritage digital experience is still lacking. To contribute to the design, implementation, and management of digital display systems at cultural heritage sites, this research aims to evaluate visitors’ expectations, acceptance, and experience of digital interpretation and presentation technologies. A mixed methodology, combining questionnaires based on a digital experience evaluation framework and semi-structured interviews, was adopted and used in Old Zuoying City (OZC), a typical heritage site for the application of digital display technologies in Taiwan. Our study indicates that digital display technologies received high acceptance from heritage visitors and had a positive impact on some specific aspects, such as encouraging their exploration of and further learning about the site, helping them better visualize the richness of heritage, and creating new experiences of perceiving, engaging, and communicating with history. In addition, based on the survey findings, a number of recommendations for the current stage of digital display design are presented. The survey results and evaluation framework can be used as a reference for other research on heritage visitors and to improve digital interpretation and presentation design.
A Novel Diagonal Class Entropy-Based Multilevel Image Thresholding Using Coral Reef Optimization

In the normal image thresholding methods based on two-dimensional histogram, the edge information of the regions is not maintained because of the local averaging activity used. Moreover, the computation time increases with the increase in the level of thresholds. This paper focusses on retaining more edge information by calculating the image entropy along the diagonal regions of the gray level co-occurrence matrix inspired from the partitioned design structure matrix, which is a novel idea. In addition, the key to our success is the theoretical investigation of a novel diagonal class entropy (DCE) concept that utilizes the minimum area for computation. The benefits of the proposed method are: 1) improved results; 2) efficient to preserve more precise shape of the edges; and 3) the computation time decreases with the increase in the threshold levels. The optimal thresholds are obtained by minimizing the DCE using coral reef optimization (CRO). A first hand fitness function for multilevel image thresholding is derived. The fight for space and the efficient reproduction characteristics of the CRO makes it attractive for this application. Benchmark images from the Berkley segmentation dataset are taken to experiment. Our results are compared with other state-of-the-art thresholding methods. The results obtained are encouraging and may set the path for further investigation in the domain of multilevel thresholding.
Collision Avoidance Interaction Between Human and a Hidden Robot Based on Kinect and Robot Data Fusion

Human-Robot Interaction (HRI) is a largely addressed subject today. In order to ensure co-existence and space sharing between human and robot, collision avoidance is one of the main strategies for interaction between them without contact. It is thus usual to use a 3D depth camera sensor (Microsoft Kinect V2) which may involve issues related to occluded robot in the camera view. While several works overcame this issue by applying infinite depth principle or increasing the number of cameras, in the current work we developed and applied an original new approach that combines data of one 3D depth sensor (Kinect) and proprioceptive robot sensors. This method uses the principle of limited safety contour around the obstacle to dynamically estimate the robot-obstacle distance, and then generate the repulsive force that controls the robot. For validation, our approach is applied in real time to avoid collisions between dynamical obstacles (humans or objects) and the end-effector of a real 7-dof Kuka LBR iiwa collaborative robot. Our method is experimentally compared with existing methods based on infinite depth principle when the robot is hidden by the obstacle with respect to the camera view. Results showed smoother behavior and more stability of the robot using our method. Extensive experiments of our method, using several strategies based on distancing and its combination with dodging were done. Results have shown a reactive and efficient collision avoidance, by ensuring a minimum obstacle-robot distance (of $\approx \text{240 mm}$), even when the robot is in an occluded zone in the Kinect camera view.
Self-Attention Encoding and Pooling for Speaker Recognition

The computing power of mobile devices limits the end-user applications in terms of storage size, processing, memory and energy consumption.These limitations motivate researchers for the design of more efficient deep models.On the other hand, self-attention networks based on Transformer architecture have attracted remarkable interests due to their high parallelization capabilities and strong performance on a variety of Natural Language Processing (NLP) applications.Inspired by the Transformer, we propose a tandem Self-Attention Encoding and Pooling (SAEP) mechanism to obtain a discriminative speaker embedding given non-fixed length speech utterances.SAEP is a stack of identical blocks solely relied on self-attention and position-wise feed-forward networks to create vector representation of speakers.This approach encodes short-term speaker spectral features into speaker embeddings to be used in text-independent speaker verification.We have evaluated this approach on both VoxCeleb1 & 2 datasets.The proposed architecture is able to outperform the baseline x-vector, and shows competitive performance to some other benchmarks based on convolutions, with a significant reduction in model size.It employs 94%, 95%, and 73% less parameters compared to ResNet-34, ResNet-50, and x-vector, respectively.This indicates that the proposed fully attention based architecture is more efficient in extracting time-invariant features from speaker utterances.
Correlation-Picture Approach to Open-Quantum-System Dynamics

By relating correlations between a quantum system and its environment to an uncorrelated description of the whole system, a new analytical technique provides a powerful tool for tracking correlations in open quantum system dynamics.
Electronic Bottleneck Suppression in Next‐Generation Networks with Integrated Photonic Digital‐to‐Analog Converters

Digital‐to‐analog converters (DAC) are indispensable functional units in signal processing instrumentation and wide‐band telecommunication links for both civil and military applications. As photonic systems are capable of high data throughput and low latency, an increasingly found system limitation stems from the required domain crossing such as digital to analog and electronic to optical. A photonic DAC implementation, in contrast, enables a seamless signal conversion with respect to both energy efficiency and short signal delay, often requiring bulky discrete optical components and electric–optic transformation, hence introducing inefficiencies. Herein, a novel coherent parallel photonic DAC concept along with a 4‐bit experimental prototype capable of performing this DAC without optic–electric–optic domain crossing is introduced. This new paradigm guarantees a linear intensity weighting among bits when operating at high sampling rates (50 GHz), featuring an exceptional sampling efficiency (&gt; 100 GS ) and small footprint (≈1 mm 2 ) in an 8‐bit implementation. Importantly, this photonic DAC enables seamless interfaces of next‐generation data processing hardware with high relevance in data centers, task‐specific compute accelerators such as neuromorphic engines, and network edge processing applications.
Fill in the BLANC: Human-free quality estimation of document summaries

We present BLANC, a new approach to the automatic estimation of document summary quality.Our goal is to measure the functional performance of a summary with an objective, reproducible, and fully automated method.Our approach achieves this by measuring the performance boost gained by a pretrained language model with access to a document summary while carrying out its language understanding task on the document's text.We present evidence that BLANC scores have as good correlation with human evaluations as do the ROUGE family of summary quality measurements.And unlike ROUGE, the BLANC method does not require human-written reference summaries, allowing for fully humanfree summary quality estimation.