Reviewerly/exHarmony huggingface dataset:
Software Quality Assessment of a Web Application for Biomedical Data Analysis

Data Science as a multidisciplinary discipline has seen a massive transformation in the direction of operationalisation of analysis workflows. Yet it can be observed that such a workflow consists of potentially many diverse components: like modules in different programming languages, database backends, or web frontends. In order to achieve high efficiency and reproducibility of the analysis, a sufficiently high level of software engineering for the different components as well as an overall software architecture that integrates and automates the different components is needed. For the use case of gene expression analysis, from a software quality point of view we analyze a newly developed web application that allows user-friendly access to the underlying workflow.
Social Event Recommendation Based on Social Relationship and Attention Mechanism

Event recommendation based on social relationship is a common method in EBSNs. This kind of algorithm can solve the problem of cold start and data sparsity to a certain extent, but it doesn‘’t consider the influence degree of diffrent friends and the influence degree of diffrent groups on events. In this paper, a novel method for social event recommendation based on social relationship and self-attention (AtSoRec) was developed. The algorithm gets the trust weights among different friends and users by the attention model training. In the process of training, the information of groups and events is integrated into the module. Then, the final Top-k recommendation is obtained by Matrix factorization algorithm. Through the experiments on meetup and plancast data set, it is proved that the algorithm has a good performance on solving the problem of cold start.
VLE Limits and Perspectives for Digital Integration in Teaching Practices

With the recent COVID-19 pandemic and general school closure, teachers had to teach remotely in an emergency. This study explores how this particular context enacts French Primary and Secondary Teachers’ technology integration, with a specific focus on the virtual learning environment (VLE), by exploiting 441 teachers’ answers to a survey and 13 in-depth interviews. The findings confirm previous studies: teachers intensified the practices they were already familiar with. But the need to keep a pedagogical link with students led them to more active, collaborative, and engaging learning forms. Our multidimensional analysis shows that teachers integrated technologies into their practices, according to two different logics: diversifying interaction with students and improving self-efficiency. VLE seems easier to use than other digital tools, yet it lacks the resources to fully support teachers’ professional development. Finally, this article presents three strategies to redesign the UX and open up the VLE to resource creation, to promote the integration of digital tools within teachers’ practices.
Numerical study on smart sloped rolling-type seismic isolators integrated with early prediction of peak velocity

The effectiveness of sloped rolling-type seismic isolators (SRI) in seismically protecting critical equipment from malfunction and damage has already been extensively demonstrated. To prevent the isolation displacement response of SRI from reaching a threshold, passively designing a large and conservative damping force for SRI is usually required, which accordingly leads to enlarged and even unacceptable transmitted acceleration responses. That is, for seismic isolation, there is always a trade-off between minimizing acceleration and displacement responses. Previous studies have indicated that by determining the damping force applied to SRI based on the possible information provided by an earthquake early warning system and adjusting it in a semi-active control manner, its acceleration and displacement responses can be controlled more satisfactorily. However, this is based on the premise that the parameters of input excitation needed for determining the damping force are predicted promptly and accurately. Besides, among the discussed parameters, the peak velocity (PV) was most recommended. To further improve this, in this study, for the first few seconds after the arrival of primary waves (P-wave), the prediction models of PV are developed using the artificial neural network (ANN) approach. Based on the early prediction of PV as well as the proposed control law, the required damping force for SRI can be determined merely several seconds after P-wave arrival. The control performances of SRI, whose damping forces are determined using the predictions of PV at different times after P-wave arrival, are numerically examined. Through studies under a large number of conditions based on different earthquake records together with the ground motions recorded in an independent damaging earthquake event, a combination of ANN models at different, suitable times after P-wave arrival is recommended to determine the damping force applied to SRI.
Exploring the Use of Chatrooms by Developers: An Empirical Study on Slack and Gitter

Communication is critical for the software development teams to maintain project awareness, facilitate project co-ordination and avoid misunderstandings. The features offered in the chatrooms, such as private messaging, group conversations, and code sharing help accommodate the communication needs of the software development teams. Therefore, chatrooms have been increasingly adopted among the developers. Since the last study on Slack performed by (Lin <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> 2016), the audience of Slack has more than doubled possibly leading to an evolution of the ways Slack is used; while another rich community formed around Gitter and remains unstudied. In this paper, we perform an investigative study using qualitative and quantitative techniques to gain insights on the use of popular modern chatrooms, specifically Slack and Gitter. Based on the survey responses from 163 developers, the interviews with 21 developers, and the chatroom data collected from 11 Slack and 770 Gitter rooms, we are able to uncover the reasons behind the use of Slack and Gitter, the perceived impact on the associated projects, and the quality determinants of the two chatrooms. We find that the developers seek knowledge from the chatrooms to obtain timely feedback from experts, and in return share their expertise to build the project community and their reputations. Furthermore, it is perceived by the Gitter developers that the chatrooms have an impact on prioritizing the new features and the bug fixes. In Slack, the most reported impact concerns an increased project awareness, in terms of a better tracking of the work progress. As reported on the developers’ survey, both Slack and Gitter chat services have a visible impact on mentoring developers, and sharing the best practices. In terms of quality determinants, a non-ephemeral history and a better history management (e.g., advanced search) could be keys for both chat services to reach their full potential.
Assured learning‐enabled autonomy: A metacognitive reinforcement learning framework

Summary Reinforcement learning (RL) agents with pre‐specified reward functions cannot provide guaranteed safety across variety of circumstances that an uncertain system might encounter. To guarantee performance while assuring satisfaction of safety constraints across variety of circumstances, an assured autonomous control framework is presented in this article by empowering RL algorithms with metacognitive learning capabilities. More specifically, adapting the reward function parameters of the RL agent is performed in a metacognitive decision‐making layer to assure the feasibility of RL agent. That is, to assure that the learned policy by the RL agent satisfies safety constraints specified by signal temporal logic while achieving as much performance as possible. The metacognitive layer monitors any possible future safety violation under the actions of the RL agent and employs a higher‐layer Bayesian RL algorithm to proactively adapt the reward function for the lower‐layer RL agent. To minimize the higher‐layer Bayesian RL intervention, a fitness function is leveraged by the metacognitive layer as a metric to evaluate success of the lower‐layer RL agent in satisfaction of safety and liveness specifications, and the higher‐layer Bayesian RL intervenes only if there is a risk of lower‐layer RL failure. Finally, a simulation example is provided to validate the effectiveness of the proposed approach.
Generative Adversarial Networks with Bi-directional Normalization for Semantic Image Synthesis

Semantic image synthesis aims at translating semantic label maps to photo-realistic images. However, most of previous methods easily generate blurred regions and artifacts, and the quality of these images is far from realistic. There are two unresolved problems existing: first, these methods directly feed the semantic label as input to the deep network, through convolution operation to produce the normalization parameters γ and β, we find that the semantic labels are different from real scene images, they are not able to provide detailed structural information, making it difficult to synthesize local details and structures; second, there are no bi-directional information flow between the semantic labels and the real scene images, this leads to inefficiently utilize the semantic information and maintain semantic constrains to preserve the semantic information in the process of semantic image synthesis. We propose Bi-directional Normalization (BDN) in our generative adversarial networks to solve these problems, which allows semantic label information and real scene image feature representation to be effectively utilized by a bi-directional way for generating high quality images. Extensive experiments on several challenging datasets demonstrate significantly better than that results of existing methods in both visual fidelity and quantitative metrics.
A Statistical Photovoltaic Power Forecast Model (SPF) based on Historical Power and Weather Data

In this paper, we present an empirical statistical model, photovoltaic (PV) power forecast model (SPF) based on the historical power and weather type data. Firstly, the historical power data is grouped into 4 levels, according to the influence of different weather types on the penetration rate of solar radiation. Secondly, K-means and the parabolic least square fitting are used to model the PV power of each level. Thirdly, the variations to the initial fitted power are further investigated by analyzing the probability distribution of the residuals. The final power forecast is validated by a ten-day forecast for three stations, and the mean absolute percentage error (MAPE) value are 12.85%, 11.92%, 16.69%, respectively. Superior to the PV power forecast from the numerical weather data, this approach is computationally fast and needs less meteorological inputs, which can apply to the power forecast and energy yield estimation for the widely distributed PV plants.
Question-Guided Erasing-Based Spatiotemporal Attention Learning for Video Question Answering

Spatiotemporal attention learning for video question answering (VideoQA) has always been a challenging task, where existing approaches treat the attention parts and the nonattention parts in isolation. In this work, we propose to enforce the correlation between the attention parts and the nonattention parts as a distance constraint for discriminative spatiotemporal attention learning. Specifically, we first introduce a novel attention-guided erasing mechanism in the traditional spatiotemporal attention to obtain multiple aggregated attention features and nonattention features and then learn to separate the attention and the nonattention features with an appropriate distance. The distance constraint is enforced by a metric learning loss, without increasing the inference complexity. In this way, the model can learn to produce more discriminative spatiotemporal attention distribution on videos, thus enabling more accurate question answering. In order to incorporate the multiscale spatiotemporal information that is beneficial for video understanding, we additionally develop a pyramid variant on basis of the proposed approach. Comprehensive ablation experiments are conducted to validate the effectiveness of our approach, and state-of-the-art performance is achieved on several widely used datasets for VideoQA.
English Speech Recognition Method Based on HMM Technology

In order to solve the problem of insufficient feedback and intelligent speech recognition in English pronunciation learning, this paper designs and implements an English speech learning system based on speech recognition technology. Matlab is used to complete the sample speech data collection and preprocessing. Combined with the needs of Chinese speaking English learners, the speech recognition technology based on HMM model is used to decode the speech by Viterbi, and the recognition score is obtained by posterior probability. The experimental results show that this scheme greatly reduces the search space and effectively shortens the algorithm time without affecting the recognition accuracy. The function of each module of the system has been well implemented. The system can be used by users normally and is stable in function and performance.
Grassmannian codes from paired difference sets

An equiangular tight frame (ETF) is a sequence of vectors in a Hilbert space that achieves equality in the Welch bound and so has minimal coherence. More generally, an equichordal tight fusion frame (ECTFF) is a sequence of equi-dimensional subspaces of a Hilbert space that achieves equality in Conway, Hardin and Sloane's simplex bound. Every ECTFF is a type of optimal Grassmannian code, that is, an optimal packing of equi-dimensional subspaces of a Hilbert space. We construct ECTFFs by exploiting new relationships between known ETFs. Harmonic ETFs equate to difference sets for finite abelian groups. We say that a difference set for such a group is "paired" with a difference set for its Pontryagin dual when the corresponding subsequence of its harmonic ETF happens to be an ETF for its span. We show that every such pair yields an ECTFF. We moreover construct an infinite family of paired difference sets using quadratic forms over the field of two elements. Together this yields two infinite families of real ECTFFs.
A multi-sender decoupling theorem and simultaneous decoding for the quantum MAC

In this work, we prove a novel one-shot ‘multi-sender’ decoupling theorem generalising Dupuis' seminal single sender decoupling theorem. We start off with a multipartite quantum state, say on <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A_{1}A_{2}R$</tex> , where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A_{1}, A_{2}$</tex> are treated as the two ‘sender’ systems and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R$</tex> is the reference system. We apply independent Haar random unitaries in tensor product on <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A_{1}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A_{2}$</tex> and then send the resulting systems through a quantum channel. We want the channel output <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$B$</tex> to be almost in tensor with the untouched reference <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R$</tex> . Our main result shows that this is indeed the case if suitable entropic conditions are met. An immediate application of our main result is to obtain a one-shot simultaneous decoder for sending quantum information over a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -sender entanglement unassisted quantum multiple access channel (QMAC). The rate region achieved by this decoder is the natural one-shot quantum analogue of the pentagonal classical rate region. Assuming a simultaneous smoothing conjecture, this one-shot rate region approaches the optimal rate region of Yard et al. [20] in the asymptotic iid limit. Our work is the first one to obtain a non-trivial simultaneous decoder for the QMAC with limited entanglement assistance in both one-shot and asymptotic iid settings; previous works used unlimited entanglement assistance.
Network Traffic Classification Method Supporting Unknown Protocol Detection

At present, private protocols are widely used on the Internet. As a result, traditional traffic classification methods including port-based and DPI methods have become restricted. Existing machine learning-based methods depend on feature engineering, which makes feature design difficult. In addition, classification models can only classify data as predefined categories, which restricts the models when they are used to detect unknown protocol traffic. To address the above problems, we propose a two-stage traffic classification method combining a CNN model and a density-based clustering algorithm, which can classify known protocol traffic and detect arbitrary kinds of unknown protocol traffic simultaneously. We conducted sufficient experiments on the Information Security Centre of Excellence (ISCX) VPN-nonVPN and Defense Advanced Research Projects Agency (DARPA) 1998 datasets, and the accuracies on the test sets containing known and unknown protocol traffic achieved 97.03% and 98.50%, respectively, which are superior to other studies.
GridNet‐3D: A Novel Real‐Time 3D Object Detection Algorithm Based on Point Cloud

3D object detection based on point cloud has an important application prospect in automatic driving technology. Aiming at the low precision of 3D object detection based on point cloud and the poor real-time performance caused by large numbers of 3D convolutions, a novel end-to-end real-time object detection algorithm named GridNet-3D is proposed. In the work, 2D gridmapping is used to preprocess the original point clouds. Then a novel structure grid encoding layer is adopted to encode point cloud features and is gotten grid feature maps in bird's eye view which is connected to region proposal network module to generate detections. Despite only using point clouds, the results on the KITTI 3D detection benchmark show that our algorithm has higher detection precision and better real-time performance on the detection of cars, pedestrians and cyclists, which has high practical value.
Knowledge Sharing in Agile Settings: State of the Practice of Organizational Training

One of the central tasks of process improvement is to deal with changing environmental conditions, and organizational training is crucial for this purpose. The software industry is one such dynamic and ever-changing environment. However, knowledge dissemination and ongoing learning is an open challenge in this industry, especially in agile organizations where continuous learning is essential. This paper has two aims: 1) investigate and report the state of organizational training practice in the agile software development setting; 2) identify relevant issues to be considered by agile organizations addressing the organizational training process. To do this, we conducted a systematic mapping of the literature to provide an overview of how agile organizations plan, perform, and assess their training activities as part of their continuous learning process. Compared with other agile software development domains, the literature on this topic is scant, but we were able to derive relevant results related, for example, to the most popular training techniques or training coordination mechanisms. We also identified some evidence gaps concerning how organizations plan training, how they use training records, and how they assess and improve training processes. The paper concludes by presenting eight important issues that cover the different organizational training activities and that provide valuable insights to effectively share knowledge in agile settings.
Visually Similar Handwritten Chinese Character Recognition with Convolutional Neural Network

Computer vision has penetrated many domains, for instance, security, sports, health and medicine, agriculture, transportation, manufacturing, retail, and so like. One of the computer vision tasks is character recognition. In this work, a visually similar handwritten Chinese character dataset is collected. Subsequently, an enhanced convolutional neural network is proposed for the recognition of visually similar handwritten Chinese characters. The convolutional neural network is enhanced by the dropout regularization and early stopping mechanism to reduce the overfitting problem. The Adam optimizer is also leveraged to accelerate and optimize the training process of the convolutional neural network. The empirical results demonstrate that the enhanced convolutional neural network achieves a 97% accuracy, thus corroborate it has better discriminating power in visually similar handwritten Chinese character recognition.
Copy complexity of Horn formulas with respect to unit read-once resolution

In this paper, we discuss the copy complexity of Horn formulas with respect to unit resolution. A Horn formula is a boolean formula in conjunctive normal form (CNF) with at most one positive literal per clause. Horn formulas find applications in a number of domains, such as program verification (abstract interpretation) and logic programming (answer set programming). Quantified Horn clauses are used extensively in temporal verification of universal properties. Resolution is one of the oldest proof systems (refutation systems) for the boolean satisfiability problem (SAT), when the input is presented in conjunctive normal form (CNF). It is both sound and complete, although inefficient, when compared to other stronger proof systems for boolean formulas. Despite its inefficiency, the simple nature of resolution makes it an integral part of several theorem provers. Unit resolution is a restricted form of resolution in which each resolution step needs to use a clause with only one literal (unit literal clause). While not complete for general CNF formulas, unit resolution is complete for Horn formulas. Read-once resolution is a form of resolution in which each clause (input or derived) may be used in at most one resolution step. As with unit resolution, read-once resolution is incomplete in general and complete for Horn clauses. This paper focuses on a combination of unit resolution and read-once resolution called unit read-once resolution. Unit read-once resolution is incomplete for Horn clauses. In this paper, we study the copy complexity problem in Horn formulas with respect to unit read-once resolution. Briefly, the copy complexity of a formula with respect to unit read-once resolution, is the smallest number k, such that replicating each clause k times guarantees the existence of a unit read-once resolution refutation (UROR). This paper focuses on two problems related to the copy complexity of Horn formulas with respect to unit read-once resolution. We first relate the copy complexity of Horn formulas with respect to unit read-once resolution to the copy complexity of the corresponding Horn constraint system with respect to the addition rule. We also examine a form of copy complexity in which we permit replication of derived clauses, in addition to the input clauses. Finally, we provide a polynomial time algorithm for the problem of checking if a 2-CNF formula has a UROR.