Reviewerly/exHarmony huggingface dataset:
Identifying Self-Admitted Technical Debts With Jitterbug: A Two-Step Approach

Keeping track of and managing Self-Admitted Technical Debts (SATDs) are important to maintaining a healthy software project. This requires much time and effort from human experts to identify the SATDs manually. The current automated solutions do not have satisfactory precision and recall in identifying SATDs to fully automate the process. To solve the above problems, we propose a two-step framework called Jitterbug for identifying SATDs. Jitterbug first identifies the "easy to find" SATDs automatically with close to 100% precision using a novel pattern recognition technique. Subsequently, machine learning techniques are applied to assist human experts in manually identifying the remaining "hard to find" SATDs with reduced human effort. Our simulation studies on ten software projects show that Jitterbug can identify SATDs more efficiently (with less human effort) than the prior state-of-the-art methods.
Semi-Device-Independent Heterodyne-Based Quantum Random-Number Generator

Randomness is a fundamental feature of quantum mechanics, which is an invaluable resource for both classical and quantum technologies. Practical quantum random number generators (QRNG) usually need to trust their devices, but their security can be jeopardized in case of imperfections or malicious external actions. In this work, we present a robust implementation of a Semi-Device-Independent QRNG that guarantees both security and fast generation rates. The system works in a prepare and measure scenario where measurement and source are untrusted, but a bound on the energy of the prepared states is assumed. Our implementation exploits heterodyne detection, which offers increased generation rate and improved long-term stability compared to alternative measurement strategies. In particular, due to the tomographic properties of heterodyne measurement, we can compensate for fast phase fluctuations via post-processing, avoiding complex active phase stabilization systems. As a result, our scheme combines high security and speed with a simple setup featuring only commercial-off-the-shelf components, making it an attractive solution in many practical scenarios.
Blockchain-Based Application Security Risks: A Systematic Literature Review

Although the blockchain-based applications are considered to be less vulnerable due to the nature of the distributed ledger, they did not become the silver bullet with respect to securing the information against different security risks. In this paper, we present a literature review on the security risks that can be mitigated by introducing the blockchain technology, and on the security risks that are identified in the blockchain-based applications. In addition, we highlight the application and technology domains where these security risks are observed. The results of this study could be seen as a preliminary checklist of security risks when implementing blockchain-based applications.
Experimental Realization of Shortcuts to Adiabaticity in a Nonintegrable Spin Chain by Local Counterdiabatic Driving

Counterdiabatic driving (CD) offers a fast and robust strategy to manipulate and prepare the quantum state in multibody systems. However, the exact CD term involving the spectral properties of the system is difficult to calculate and generally takes a complicated form for higher-dimensional complex systems. Recently, Sels and Polkovnikov [Proc. Natl. Acad. Sci. U. S. A. 114, E3909 (2017)] proposed a variational method to optimally approximate the CD term with no need for the full spectral information of the original Hamiltonian. Using a nuclear-magnetic-resonance setup, here we report an experimental demonstration of this method to construct available shortcuts to adiabaticity in a nonintegrable spin-$1/2$ chain via only local controls. In this process, the ground state of such a system is rapidly prepared and the final state fidelity is significantly increased compared with the traditional adiabatic method. Our experiments prove the feasibility of this variational method to design CD in practice, and thus it is a promising strategy for fast, high-fidelity quantum-state manipulation in complex and noisy quantum systems.
Phishing Email Detection Based on Binary Search Feature Selection

Phishing has appeared as a critical issue in the cybersecurity domain. Phishers adopt email as one of their major channels of communication to lure potential victims. This paper attempts to detect phishing emails by using binary search feature selection (BSFS) with a Pearson correlation coefficient algorithm as a ranking method. The proposed method utilizes four sets of features from the email subject, the body of the email, hyperlinks, and readability of contents. Overall, 41 features were selected from the aforementioned four dimensions. The result shows that the BSFS method evaluated the accuracy of 97.41% in comparison with SFFS (95.63%) and WFS (95.56%). This exploration shows that the SFFS requires more time to ascertain the optimum features set and the WFS requires the least time; however, the accuracy of WFS is very low in comparison with other algorithms. The significant finding of the experiment is that the BFSF requires the least time to evaluate the best feature set with better accuracy even though few features are removed from the feature corpus.
Motif-Preserving Temporal Network Embedding

Network embedding, mapping nodes in a network to a low-dimensional space, achieves powerful performance. An increasing number of works focus on static network embedding, however, seldom attention has been paid to temporal network embedding, especially without considering the effect of mesoscopic dynamics when the network evolves. In light of this, we concentrate on a particular motif --- triad --- and its temporal dynamics, to study the temporal network embedding. Specifically, we propose MTNE, a novel embedding model for temporal networks. MTNE not only integrates the Hawkes process to stimulate the triad evolution process that preserves motif-aware high-order proximities, but also combines attention mechanism to distinguish the importance of different types of triads better. Experiments on various real-world temporal networks demonstrate that, compared with several state-of-the-art methods, our model achieves the best performance in both static and dynamic tasks, including node classification, link prediction, and link recommendation.
Gated Recurrent Unit Neural Networks for Automatic Modulation Classification With Resource-Constrained End-Devices

The continuous increase in the number of mobile and Internet of Things (IoT) devices, as well as in the wireless data traffic they generate, represents an essential challenge in terms of spectral coexistence. As a result, these devices are now expected to make efficient and dynamic use of the spectrum by employing Cognitive Radio (CR) techniques. In this work, we focus on the Automatic Modulation Classification (AMC). AMC is essential to carry out multiple CR techniques, such as dynamic spectrum access, link adaptation and interference detection, aimed at improving communications throughput and reliability and, in turn, spectral efficiency. In recent years, multiple Deep Learning (DL) techniques have been proposed to address the AMC problem. These DL techniques have demonstrated better generalization, scalability and robustness capabilities compared to previous solutions. However, most of these techniques require high processing and storage capabilities that limit their applicability to energy- and computation-constrained end-devices. In this work, we propose a new gated recurrent unit neural network solution for AMC that has been specifically designed for resource-constrained IoT devices. We trained and tested our solution with over-the-air measurements of real radio signals. Our results show that the proposed solution has a memory footprint of 73.5 kBytes, 51.74% less than the reference model, and achieves a classification accuracy of 92.4%.
Gaussian bandwidth selection for manifold learning and classification

Kernel methods play a critical role in many machine learning algorithms. They are useful in manifold learning, classification, clustering and other data analysis tasks. Setting the kernel's scale parameter, also referred to as the kernel's bandwidth, highly affects the performance of the task in hand. We propose to set a scale parameter that is tailored to one of two types of tasks: classification and manifold learning. For manifold learning, we seek a scale which is best at capturing the manifold's intrinsic dimension. For classification, we propose three methods for estimating the scale, which optimize the classification results in different senses. The proposed frameworks are simulated on artificial and on real datasets. The results show a high correlation between optimal classification rates and the estimated scales. Finally, we demonstrate the approach on a seismic event classification task.
Marrying Medical Domain Knowledge With Deep Learning on Electronic Health Records: A Deep Visual Analytics Approach

Deep learning models have attracted significant interest from health care researchers during the last few decades. There have been many studies that apply deep learning to medical applications and achieve promising results. However, there are three limitations to the existing models: (1) most clinicians are unable to interpret the results from the existing models, (2) existing models cannot incorporate complicated medical domain knowledge (eg, a disease causes another disease), and (3) most existing models lack visual exploration and interaction. Both the electronic health record (EHR) data set and the deep model results are complex and abstract, which impedes clinicians from exploring and communicating with the model directly.The objective of this study is to develop an interpretable and accurate risk prediction model as well as an interactive clinical prediction system to support EHR data exploration, knowledge graph demonstration, and model interpretation.A domain-knowledge-guided recurrent neural network (DG-RNN) model is proposed to predict clinical risks. The model takes medical event sequences as input and incorporates medical domain knowledge by attending to a subgraph of the whole medical knowledge graph. A global pooling operation and a fully connected layer are used to output the clinical outcomes. The middle results and the parameters of the fully connected layer are helpful in identifying which medical events cause clinical risks. DG-Viz is also designed to support EHR data exploration, knowledge graph demonstration, and model interpretation.We conducted both risk prediction experiments and a case study on a real-world data set. A total of 554 patients with heart failure and 1662 control patients without heart failure were selected from the data set. The experimental results show that the proposed DG-RNN outperforms the state-of-the-art approaches by approximately 1.5%. The case study demonstrates how our medical physician collaborator can effectively explore the data and interpret the prediction results using DG-Viz.In this study, we present DG-Viz, an interactive clinical prediction system, which brings together the power of deep learning (ie, a DG-RNN-based model) and visual analytics to predict clinical risks and visually interpret the EHR prediction results. Experimental results and a case study on heart failure risk prediction tasks demonstrate the effectiveness and usefulness of the DG-Viz system. This study will pave the way for interactive, interpretable, and accurate clinical risk predictions.
Interpreting SVM for medical images using Quadtree

In this paper, we propose a quadtree based approach to capture the spatial information of medical images for explaining nonlinear SVM prediction. In medical image classification, interpretability becomes important to understand why the adopted model works. Explaining an SVM prediction is difficult due to implicit mapping done in kernel classification is uninformative about the position of data points in the feature space and the nature of the separating hyperplane in the original space. The proposed method finds ROIs which contain the discriminative regions behind the prediction. Localization of the discriminative region in small boxes can help in interpreting the prediction by SVM. Quadtree decomposition is applied recursively before applying SVMs on sub images and model identified ROIs are highlighted. Pictorial results of experiments on various medical image datasets prove the effectiveness of this approach. We validate the correctness of our method by applying occlusion methods.
The data representativeness criterion: Predicting the performance of supervised classification based on data set similarity

In a broad range of fields it may be desirable to reuse a supervised classification algorithm and apply it to a new data set. However, generalization of such an algorithm and thus achieving a similar classification performance is only possible when the training data used to build the algorithm is similar to new unseen data one wishes to apply it to. It is often unknown in advance how an algorithm will perform on new unseen data, being a crucial reason for not deploying an algorithm at all. Therefore, tools are needed to measure the similarity of data sets. In this paper, we propose the Data Representativeness Criterion (DRC) to determine how representative a training data set is of a new unseen data set. We present a proof of principle, to see whether the DRC can quantify the similarity of data sets and whether the DRC relates to the performance of a supervised classification algorithm. We compared a number of magnetic resonance imaging (MRI) data sets, ranging from subtle to severe difference is acquisition parameters. Results indicate that, based on the similarity of data sets, the DRC is able to give an indication as to when the performance of a supervised classifier decreases. The strictness of the DRC can be set by the user, depending on what one considers to be an acceptable underperformance.
Screen Content Video Quality Assessment: Subjective and Objective Study

In this paper, we make the first attempt to study the subjective and objective quality assessment for the screen content videos (SCVs). For that, we construct the first large-scale video quality assessment (VQA) database specifically for the SCVs, called the screen content video database (SCVD). This SCVD provides 16 reference SCVs, 800 distorted SCVs, and their corresponding subjective scores, and it is made publicly available for research usage. The distorted SCVs are generated from each reference SCV with 10 distortion types and 5 degradation levels for each distortion type. Each distorted SCV is rated by at least 32 subjects in the subjective test. Furthermore, we propose the first full-reference VQA model for the SCVs, called the spatiotemporal Gabor feature tensor-based model (SGFTM), to objectively evaluate the perceptual quality of the distorted SCVs. This is motivated by the observation that 3D-Gabor filter can well stimulate the visual functions of the human visual system (HVS) on perceiving videos, being more sensitive to the edge and motion information that are often-encountered in the SCVs. Specifically, the proposed SGFTM exploits 3D-Gabor filter to individually extract the spatiotemporal Gabor feature tensors from the reference and distorted SCVs, followed by measuring their similarities and later combining them together through the developed spatiotemporal feature tensor pooling strategy to obtain the final SGFTM score. Experimental results on SCVD have shown that the proposed SGFTM yields a high consistency on the subjective perception of SCV quality and consistently outperforms multiple classical and state-of-the-art image/video quality assessment models.
Thanks for inviting me to the party: Virtual poster sessions as a way to connect in a time of disconnection

Abstract COVID‐19 presented the world with trauma and isolation, but many people, including educators, have offered bright spots of creativity and engagement. As we confronted these issues in our own ecology classroom, we sought solutions to carry‐forward the learning objectives we set for our students in January 2020, yet encourage interaction with the sensitivity that a pandemic requires. In the rapid transition to online course delivery, we opted to retain the original end‐of‐semester poster project in our introductory ecology course. However, we experimented with a new virtual platform where students could disseminate their work and communicate with the community. In this paper, we discuss the Mozilla Hubs virtual reality platform that we used for our event. We also collected qualitative data to share the benefits and challenges of this experience felt by the students, the instructors, and external observers.
A Firefly Algorithm With Self-Adaptive Population Size for Global Path Planning of Mobile Robot

In the simulation experiment of path planning of mobile robot based on firefly algorithm, it is found that the matching relationship between the number of fireflies and obstacles in the iterative process has significant conflict impacts on exploration ability and computational complexity of the algorithm. In order to solve the above problem, an optimal method of path planning based on firefly algorithm with self-adaptive population size is proposed. Firstly, the evaluation of degree of collision is established at the cost of avoiding collision. Based on the degree of collision of the population, two nonlinear functions are proposed to determine the population size. Then, individuals are added or deleted for the firefly population. Individuals are added randomly. The feasible solution and the infeasible solution are distinguished in firefly population, and delete the fireflies in the infeasible solution first when performing the eliminating operation. Finally, on the basis of the existing methods for dealing with infeasible paths, a coefficient that is adaptively adjusted according to the population size is introduced to control the degree to which the infeasible path approaches the feasible area. Compared with fixed population size firefly algorithm, the proposed algorithm has better performance in terms of solution stability, convergence speed and running time.
Deep Learning-Based Crowd Scene Analysis Survey

Recently, our world witnessed major events that attracted a lot of attention towards the importance of automatic crowd scene analysis. For example, the COVID-19 breakout and public events require an automatic system to manage, count, secure, and track a crowd that shares the same area. However, analyzing crowd scenes is very challenging due to heavy occlusion, complex behaviors, and posture changes. This paper surveys deep learning-based methods for analyzing crowded scenes. The reviewed methods are categorized as (1) crowd counting and (2) crowd actions recognition. Moreover, crowd scene datasets are surveyed. In additional to the above surveys, this paper proposes an evaluation metric for crowd scene analysis methods. This metric estimates the difference between calculated crowed count and actual count in crowd scene videos.
Interaction design for multi-user virtual reality systems: An automotive case study

Virtual reality (VR) technology have become ever matured today. Various research and practice have demonstrated the potential benefits of using VR in different application area of manufacturing, such as in factory layout planning, product design, training, etc. However, along with the new possibilities brought by VR, comes with the new ways for users to communicate with the computer system. The human computer interaction design for these VR systems becomes pivotal to the smooth integration. In this paper, it reports the study that investigates interaction design strategies for the multi-user VR system used in manufacturing context though an automotive case study.
Anomaly Detection Based on Zero-Shot Outlier Synthesis and Hierarchical Feature Distillation

Anomaly detection suffers from unbalanced data since anomalies are quite rare. Synthetically generated anomalies are a solution to such ill or not fully defined data. However, synthesis requires an expressive representation to guarantee the quality of the generated data. In this article, we propose a two-level hierarchical latent space representation that distills inliers' feature descriptors [through autoencoders (AEs)] into more robust representations based on a variational family of distributions (through a variational AE) for zero-shot anomaly generation. From the learned latent distributions, we select those that lie on the outskirts of the training data as synthetic-outlier generators. Also, we synthesize from them, i.e., generate negative samples without seen them before, to train binary classifiers. We found that the use of the proposed hierarchical structure for feature distillation and fusion creates robust and general representations that allow us to synthesize pseudo outlier samples. Also, in turn, train robust binary classifiers for true outlier detection (without the need for actual outliers during training). We demonstrate the performance of our proposal on several benchmarks for anomaly detection.
A systematic literature review of modern software visualization

We report on the state-of-the-art of software visualization. To ensure reproducibility, we adopted the Systematic Literature Review methodology. That is, we analyzed 1440 entries from IEEE Xplore and ACM Digital Library databases. We selected 105 relevant full papers published in 2013–2019, which we classified based on the aspect of the software system that is supported (i.e., structure, behavior, and evolution). For each paper, we extracted main dimensions that characterize software visualizations, such as software engineering tasks, roles of users, information visualization techniques, and media used to display visualizations. We provide researchers in the field an overview of the state-of-the-art in software visualization and highlight research opportunities. We also help developers to identify suitable visualizations for their particular context by matching software visualizations to development concerns and concrete details to obtain available visualization tools.
Simultaneous Feature Aggregating and Hashing for Compact Binary Code Learning

Representing images by compact hash codes is an attractive approach for large-scale content-based image retrieval. In most state-of-the-art hashing-based image retrieval systems, for each image, local descriptors are first aggregated as a global representation vector. This global vector is then subjected to a hashing function to generate a binary hash code. In previous works, the aggregating and the hashing processes are designed independently. Hence these frameworks may generate suboptimal hash codes. In this paper, we first propose a novel unsupervised hashing framework in which feature aggregating and hashing are designed simultaneously and optimized jointly. Specifically, our joint optimization generates aggregated representations that can be better reconstructed by some binary codes. This leads to more discriminative binary hash codes and improved retrieval accuracy. In addition, the proposed method is flexible. It can be extended for supervised hashing. When the data label is available, the framework can be adapted to learn binary codes which minimize the reconstruction loss w.r.t. label vectors. Furthermore, we also propose a fast version of the state-of-the-art hashing method Binary Autoencoder to be used in our proposed frameworks. Extensive experiments on benchmark datasets under various settings show that the proposed methods outperform state-of-the-art unsupervised and supervised hashing methods.