Reviewerly/exHarmony huggingface dataset:
Winning Solutions and Post-Challenge Analyses of the ChaLearn AutoDL Challenge 2019

This paper reports the results and post-challenge analyses of ChaLearn's AutoDL challenge series, which helped sorting out a profusion of AutoML solutions for Deep Learning (DL) that had been introduced in a variety of settings, but lacked fair comparisons. All input data modalities (time series, images, videos, text, tabular) were formatted as tensors and all tasks were multi-label classification problems. Code submissions were executed on hidden tasks, with limited time and computational resources, pushing solutions that get results quickly. In this setting, DL methods dominated, though popular Neural Architecture Search (NAS) was impractical. Solutions relied on fine-tuned pre-trained networks, with architectures matching data modality. Post-challenge tests did not reveal improvements beyond the imposed time limit. While no component is particularly original or novel, a high level modular organization emerged featuring a “meta-learner”, “data ingestor”, “model selector”, “model/learner”, and “evaluator”. This modularity enabled ablation studies, which revealed the importance of (off-platform) meta-learning, ensembling, and efficient data management. Experiments on heterogeneous module combinations further confirm the (local) optimality of the winning solutions. Our challenge legacy includes an ever-lasting benchmark (http://autodl.chalearn.org), the open-sourced code of the winners, and a free “AutoDL self-service.”
Computing Branching Distances Using Quantitative Games

We lay out a general method for computing branching distances between labeled transition systems. We translate the quantitative games used for defining these distances to other, path-building games which are amenable to methods from the theory of quantitative games. We then show for all common types of branching distances how the resulting path-building games can be solved. In the end, we achieve a method which can be used to compute all branching distances in the linear-time–branching-time spectrum.
Within-project and cross-project software defect prediction based on improved transfer Naive Bayes algorithm

With the continuous expansion of software scale, software update and maintenance have become more and more important. However, frequent software code updates will make the software more likely to introduce new defects. So how to predict the defects quickly and accurately on the software change has become an important problem for software developers. Current defect prediction methods often cannot reflect the feature information of the defect comprehensively, and the detection effect is not ideal enough. Therefore, we propose a novel defect prediction model named ITNB (Improved Transfer Naive Bayes) based on improved transfer Naive Bayesian algorithm in this paper, which mainly considers the following two aspects: (1) Considering that the edge data of the test set may affect the similarity calculation and final prediction result, we remove the edge data of the test set when calculating the data similarity between the training set and the test set; (2) Considering that each feature dimension has different effects on defect prediction, we construct the calculation formula of training data weight based on feature dimension weight and data gravity, and then calculate the prior probability and the conditional probability of training data from the weight information, so as to construct the weighted bayesian classifier for software defect prediction. To evaluate the performance of the ITNB model, we use six datasets from large open source projects, namely Bugzilla, Columba, Mozilla, JDT, Platform and PostgreSQL. We compare the ITNB model with the transfer Naive Bayesian (TNB) model. The experimental results show that our ITNB model can achieve better results than the TNB model in terms of accurary, precision and pd for within-project and cross-project defect prediction.
Effect of Electret Process Parameters on Filtration Performance of Polypropylene Melt-Blown Nonwoven Fabric

Abstract With the spread of the novel coronavirus, masks become daily necessities, and their protective effect mainly depends on the core materials, so improving the performance of the core material polypropylene melt-blown nonwoven fabric has become the current research hotspot. The filtration performance of PP melt-blown nonwoven fabric mainly depends on the electret process parameters. The electret treatment of PP melt-blown nonwoven fabric is carried out by using high voltage corona discharge. The research on the influence of the electret process parameters, such as charging time, charging voltage and charging distance, on the filtration performance of PP melt-blown nonwoven fabric will have guiding significance for the practical production. In this paper, after a lot of research and experimental work, find out the influence of process parameters on the filtration performance of nonwoven fabrics. It is found that the best condition for the sample is the charge time of 12 min, the charge voltage of 15 kV and the charge distance of 4 cm. The filter efficiency is 90.2%, which is more than 1.5 times higher than that before the sample. The air permeability of melt-blown nonwovens after electret treatment did not decrease.
Multipartite Entanglement Detection Via Projective Tensor Norms

We introduce and study a class of entanglement criteria based on the idea of applying local contractions to an input multipartite state, and then computing the projective tensor norm of the output. More precisely, we apply to a mixed quantum state a tensor product of contractions from the Schatten class $$S_1$$ to the Euclidean space $$\ell _2$$ , which we call entanglement testers. We analyze the performance of this type of criteria on bipartite and multipartite systems, for general pure and mixed quantum states, as well as on some important classes of symmetric quantum states. We also show that previously studied entanglement criteria, such as the realignment and the SIC POVM criteria, can be viewed inside this framework. This allows us to answer in the positive two conjectures of Shang, Asadian, Zhu, and Gühne by deriving systematic relations between the performance of these two criteria.
Tracking and Speed Estimation of Ground Vehicles Using Aerial-view Videos

With the rapid technology development in autonomous navigation of Unmanned Aerial Vehicles (UAVs) and robust object detection based on deep neural networks, the field of traffic analysis through aerial video has attracted widespread attention. In this paper, we investigate the problems of ground vehicle tracking and speed estimation using aerial view videos. At the first stage, the vehicle detection is performed through the YOLOv3 network, which is the state-of-the-art object detector. Then, a tracking-by-detection method is designed to tracking the traffic vehicles. Furthermore, in order to estimate the vehicle speed in traffic while the UAV navigating in different heights, the least square algorithm is utilized to fit the measurement data and determine the power function mapping relationship between the vehicle pixel distance and the actual distance, which further improves the accuracy of speed estimation effectively.
Private data sharing between decentralized users through the privGAN architecture

More data is almost always beneficial for analysis and machine learning tasks. In many realistic situations however, an enterprise cannot share its data, either to keep a competitive advantage or to protect the privacy of the data sources, the enterprise's clients for example. We propose a method for data owners to share synthetic or fake versions of their data without sharing the actual data, nor the parameters of models that have direct access to the data. The method proposed is based on the privGAN architecture where local GANs are trained on their respective data subsets with an extra penalty from a central discriminator aiming to discriminate the origin of a given fake sample. We demonstrate that this approach, when applied to subsets of various sizes, leads to better utility for the owners than the utility from their real small datasets. The only shared pieces of information are the parameter updates of the central discriminator. The privacy is demonstrated with white-box attacks on the most vulnerable elements of the architecture and the results are close to random guessing. This method would apply naturally in a federated learning setting.
Usability Testing within a Devsecops Environment

Software Development, Security, and Operations, or "DevSecOps", is a concept that has been implemented in the engineering domain to enable faster iteration release and increased fluidity with enhanced security. As iterations become smaller and more frequent, less risk is involved with each deployment. Reducing risk is a significant part of any engineering endeavor, particularly in the aviation domain. Of the fundamental DevSecOps elements, the meshing of teams enables more free-flowing communication. DevSecOps is also an ideal methodology for inviting change in how teams operate during product creation. A team’s interpretation of a product does not always align with the needs of end-users and their requirements. Formally bringing end-users into the feedback loop would be the logical step for amending this misalignment. One fundamental aspect is ensuring a vehicle exists to bring user feedback into the team's field of view. The solution to this is to integrate a formalized testing methodology that invokes this feedback from future users. An ideal method of accomplishing this is through a process called usability testing. This process involves inviting representative users to utilize major touchpoints and features, ensuring safety and effectivity. Usability testing is best performed "early and often" to allow corrective measures to be taken if needed. As the DevSecOps cycle is iterative in nature, this poses the ideal opportunity to include user-based testing, enabling user facing modifications to become more dynamically engineered and honed to the area of interest, while maintaining built-in security. By testing software and user-facing elements in multiple times within each release the team is afforded more granular insight into the holistic state of the product without negating security considerations.
Noise and Dissipation in a Circuit Quantum Electrodynamic Description of a Josephson Traveling-Wave Parametric Amplifier

In this paper, we are constructing a circuit quantum electrodynamic model describing dissipative effects in a Josephson traveling-wave parametric amplifier (JTWPA). Losses and noise are treated using the quantum Langevin method. The total Hamiltonian of our model describes the coupling of the modes in a parametric four-wave-mixing scheme, as well as the interaction with the environment. Based on the Heisenberg equations of motion, we obtain the temporal evolution of the signal photon number and analyze the signal gain of the JTWPA.
Social Practices based characters in a Robotic Storytelling System

In this work, we present a robotic storytelling system, where the characters have been modelled as cognitive agents embodied in Pepper and NAO robots. The characters have been designed by exploiting the ACT-R architecture, taking into account knowledge, behaviours, norms, and expectations typical of social practices and desires resulting from their personality. The characters explain their reasoning processes during the narration, through a sort of internal dialogue that generate a high level of credibility experienced over the audience.
Retrieve, Rerank, Read, then Iterate: Answering Open-Domain Questions of Arbitrary Complexity from Text.

Current approaches to open-domain question answering often make crucial assumptions that prevent them from generalizing to real-world settings, including the access to parameterized retrieval systems well-tuned for the task, access to structured metadata like knowledge bases and web links, or a priori knowledge of the complexity of questions to be answered (e.g., single-hop or multi-hop). To address these limitations, we propose a unified system to answer open-domain questions of arbitrary complexity directly from text that works with off-the-shelf retrieval systems on arbitrary text collections. We employ a single multi-task model to perform all the necessary subtasks---retrieving supporting facts, reranking them, and predicting the answer from all retrieved documents---in an iterative fashion. To emulate a more realistic setting, we also constructed a new unified benchmark by collecting about 200 multi-hop questions that require three Wikipedia pages to answer, and combining them with existing datasets. We show that our model not only outperforms state-of-the-art systems on several existing benchmarks that exclusively feature single-hop or multi-hop open-domain questions, but also achieves strong performance on the new benchmark.
Blind Equalization for Chaotic Signals Based on Echo State Network and Kalman Filter Under Nonlinear Channels

For nonlinear channels, we propose a new blind equalization method for chaotic signals based on echo state network(ESN) and Kalman filter(KF) in this letter. Using the short-term predictability of chaotic signals, ESN, a new feedback neural network, is used to approximate unknown chaotic maps and predict the output of the equalizer. Furthermore, KF combined with the finite impulse response(FIR) is used as the equalizer in this letter, and its parameters are adjusted by minimizing the prediction error of ESN. Simulation results show that compared with other blind equalization methods for chaotic signals, the proposed method has the advantages of smaller mean square error and faster convergence rate, and is suitable for strong nonlinear channels.
Control Parameter Importance and Sensitivity Analysis of the Multi-Guide Particle Swarm Optimization Algorithm

The multi-guide particle swarm optimization (MGPSO) algorithm is a multi-objective optimization algorithm that uses multiple swarms, each swarm focusing on an individual objective. This paper conducts an importance and sensitivity analysis on the MGPSO control parameters using functional analysis of variance (fANOVA). The fANOVA process quantifies the control parameter importance through analysing variance in the objective function values associated with a change in control parameter values. The results indicate that the inertia component value has the greatest sensitivity and is the most important control parameter to tune when optimizing the MGPSO.
End-to-End Low-Resource Speech Recognition with a Deep CNN-LSTM Encoder

The performance of large vocabulary continuous automatic speech recognition (ASR) has improved tremendously due to the application of deep learning. However, building a low-resource ASR system remains a challenging task due to the difficulty of data collection and the lack of linguistic knowledge in the low-resource language. In this paper, we proposed an end-to-end low-resource speech recognition method and validated using the Tibetan language as an example. We firstly designed a Tibetan text corpus, and we also recorded a Tibetan speech corpus. Then, we extracted the spectrogram as a feature of each speech. The encoder is a single structure of the deep convolutional neural network (CNN) based on the VGG network and a hybrid network of deep CNN and long short-term memory (LSTM) network. The connectionist temporal classification (CTC) network sits on top of the encoder to infer the alignments between speech and label sequences. The experimental results show that the single structure encoder achieved a word error rate of 36.85%. The hybrid structure encoder achieves a 6% word error rate reduction compared to a single structure encoder. Also, when increasing the number of CNN layers in the encoder, the word error rate is further reduced.
Investigation of the Suitability of Adaptive Extended Kalman Filter for Power System Harmonics Estimation

This paper address the task of harmonic estimation of a power signal which is perturbed with Gaussian white noise. Along with the harmonic parameters, the fundamental frequency is also needs to be estimated which makes the estimation problem significantly nonlinear. Harmonic estimation problems often suffers from the incomplete knowledge of process noise covariance (Q). This paper investigates the estimation performance of two variants of Q-Adaptive Extended Kalman filter (QA-EKF) in simulation which have been rarely employed for harmonics estimation. Based on extensive simulation results the authors recommend a suitable variety of QA-EKF that demonstrates superior estimation performance over its competing estimation algorithms.
Serious Storytelling in Virtual Reality

Virtual Reality (VR) can administer a narrative rich, high-fidelity environment that provides a fully immersive and interactive experience for use in serious games. VR serious games are increasingly used in training and evacuation preparedness for natural hazards. Understanding players' behavior is critical to improving the experience of these VR serious games. Incorporating narrative in SG has proven to make them more effective; however, its influence on mediated presence and players' behavioral response is not studied extensively. This preliminary study investigates digital narratives' role on mediated presence, cognitive behavior, and engagement in an evacuation training VR serious game. Participants (N=32) were exposed to virtual flooding in a virtual environment designed for evacuation training with and without a serious storytelling context. Quantitative analysis of players' experience revealed that VR implemented with digital narrative improved the sense of mediated presence and players' behavioral response.
Source Code based On-demand Class Documentation Generation

In this paper, we present OpenAPIDocGen2, a tool that generates on-demand class documentation based on source code and documentation analysis. For a given class, OpenAPIDocGen2 generates a combined documentation for it, which includes functionality descriptions, directives, domain concepts, usage examples, class/method roles, key methods, relevant classes/methods, characteristics and concepts classification, and usage scenarios.
Rantanplan, Fast and Accurate Syllabification and Scansion of Spanish Poetry

Automated analysis of Spanish poetry corpora lacks the richness of tools available for English. The existing options suffer from a number of issues: are limited to fixed-metre hendecasyllabic verses, are not publicly available, the syllabification procedure underneath is not thoroughly tested, and their speed is questionable. This paper introduces new methods to alleviate these concerns. For syllabification, we contribute with our own method and manually crafted corpus. For scansion, our approach is based on a heuristic for the application of rhetorical figures that alter metrical length. Experimental evaluation shows that both fixed-metre and mixed-metre poetry can be successfully analyzed, producing metrical patterns more accurately (increasing accuracy by 2% and 15%, respectively), and at a fraction of the time other methods need (running at least 100 times faster).
On the need for automatic knowledge management in modern collaboration tools to improve software maintenance

The adoption of modern collaboration tools such as Slack has reduced email communication and discussions in other forums in software organizations. The absence of automatic knowledge management in these tools can significantly impact software maintenance and developer productivity in the long run. In this paper, we discuss the need and characteristics of such knowledge management systems.
In vitro vs in vivo: does the study’s interface design influence crowdsourced video QoE?

Abstract Evaluating the Quality of Experience (QoE) of video streaming and its influence factors has become paramount for streaming providers, as they want to maintain high satisfaction for their customers. In this context, crowdsourced user studies became a valuable tool to evaluate different factors which can affect the perceived user experience on a large scale. In general, most of these crowdsourcing studies either use, what we refer to, as an in vivo or an in vitro interface design. In vivo design means that the study participant has to rate the QoE of a video that is embedded in an application similar to a real streaming service, e.g., YouTube or Netflix. In vitro design refers to a setting, in which the video stream is separated from a specific service and thus, the video plays on a plain background. Although these interface designs vary widely, the results are often compared and generalized. In this work, we use a crowdsourcing study to investigate the influence of three interface design alternatives, an in vitro and two in vivo designs with different levels of interactiveness, on the perceived video QoE. Contrary to our expectations, the results indicate that there is no significant influence of the study’s interface design in general on the video experience. Furthermore, we found that the in vivo design does not reduce the test takers’ attentiveness. However, we observed that participants who interacted with the test interface reported a higher video QoE than other groups.
Designing a game to explore human artefact ecologies for assistive robotics

This paper concerns the creation of design games made to explore and uncover the daily lives and artefact ecologies of users living with paralysis from the neck down. The paper reviews previous notable literature on making design games and presents a novel approach for basing a design game on an activity theoretical framework named the Human Artefact Model. After discussing the creation of the games, the paper reflects on the results and experiences attained whilst using the design games to gather data in the wild. Last, this paper proposes several points of attention to reflect upon when creating design games based on formal theories.
Knowledge-creation Processes in Crafts-based HCI Research

Crafts-based approaches in Human-Computer Interaction (HCI) blend analog crafts and materials with digital technologies. In addition to introducing novel ways of creating artifacts, they also present us with alternative modes of inquiry and knowledge creation but we still lack frameworks for understanding the forms of knowledge created through them. We therefore introduce a sympoietic framework for articulating and analyzing knowledge generation in crafts-based research projects in HCI, which integrates concepts from craft theory with HCI. The framework extends from knowledge processes in the making of an artifact to encompass the wider research process. This includes processes that occur within the ‘lab’, e.g. research question articulation and experimentation, as well as what happens when an artifact is deployed in and enters into dialogue with the world. We exemplify the potentials of the framework with analyses of two cases, a photonic fabric and a kinetic wearable.
Sentiment analysis of open-ended student feedback

In this paper, we perform a sentiment analysis on a large set of open-ended course feedback from university courses collected between 2016 and 2019. We used the R programming language and environment for statistical computing to categorize feedback texts by their sentiment values (positive, negative). Additionally, we calculate the NRC Emotion values, which categorize the feedback according to eight basic emotions. We present analysis on the trends of how the feedback evolved through the years. Finally we compare the findings from our data to existing literature.