Reviewerly/exHarmony huggingface dataset:
Spaces and Places for Connection in the Postdigital University

Abstract This study focuses on the spaces and places for learning and teaching connections in higher education. Using a photovoice research method, we ask: what role do spaces and places play in offering opportunities for learning and teaching connection, and what do they tell us about the evolving practices of teachers in contemporary higher education? Whilst considerable attention has been paid to the learning spaces of students, we argue that less attention has been devoted to the spaces in which educators learn. Our findings are considered against a backdrop of the ongoing disruption of the Covid-19 pandemic, meaning that opportunities for interaction have assumed even greater significance, and the ways in which we use and understand teaching spaces are in flux. As such, our data highlights how the move to digital and hybrid learning is blurring the boundaries of spaces and places, reorienting what it means to teach and to learn in a postdigital higher education landscape. We engage sociomaterial and spatial concepts to examine how spaces entangle with university teachers’ experiences, and we explore the shifting nature of interaction and space in post-pandemic times.
Effects of Spatial Nonlocality versus Nonlocal Causality for Bound Electrons in External Fields

Using numerically exact solution of the time-dependent Schroedinger equation together with time-dependent quantum Monte Carlo (TDQMC) calculations we compare the effects of spatial nonlocality versus nonlocal causality for the ground state and for real-time evolution of two entangled electrons in parabolic potential in one spatial dimension. It was found that the spatial entanglement quantified by the linear quantum entropy is predicted with good accuracy using the spatial nonlocality, parameterized naturally within the TDQMC approach. At the same time, the nonlocal causality predicted by the exact solution leads to only small oscillations in the quantum trajectories which belong to the idler electron as the driven electron is subjected to a strong high frequency electric field, without interaction between the electrons.
An adaptive dimension differential evolution algorithm based on ranking scheme for global optimization

In recent years, evolutionary algorithms based on swarm intelligence have drawn much attention from researchers. This kind of artificial intelligent algorithms can be utilized for various applications, including the ones of big data information processing in nowadays modern world with heterogeneous sensor and IoT systems. Differential evolution (DE) algorithm is one of the important algorithms in the field of optimization because of its powerful and simple characteristics. The DE has excellent development performance and can approach global optimal solution quickly. At the same time, it is also easy to get into local optimal, so it could converge prematurely. In the view of these shortcomings, this article focuses on the improvement of the algorithm of DE and proposes an adaptive dimension differential evolution (ADDE) algorithm that can adapt to dimension updating properly and balance the search and the development better. In addition, this article uses the elitism to improve the location update strategy to improve the efficiency and accuracy of the search. In order to verify the performance of the new ADDE, this study carried out experiments with other famous algorithms on the CEC2014 test suite. The comparison results show that the ADDE is more competitive.
slr-kit: A semi-supervised machine learning framework for systematic literature reviews

Systematic Literature Review (SLR) is nowadays a challenging task due to the large number of papers that typically compose the scientific material of the topic to review. Recently, a lot of research effort has been devoted to automate, even partially, the stages of an SLR. This paper proposes the design and implementation of a workflow and a set of tools – called slr-kit – to support key tasks in an SLR. The proposed approach leverages a semi-supervised strategy, in which time-consuming processes are carried out using automatic tools, whereas manual tasks have been optimized by carefully designed support tools to reduce the overall required effort. Important parts of the workflow include the extraction of key terms directly from the abstracts of the papers to survey, and the subsequent topic modeling that allows for a thematic clustering of the corpus of papers. In the proposed workflow, the former task is carried out by exploiting a novel tool, called FAst WOrd Classifier (FAWOC). The latter, instead, is designed to be automatically carried out by leveraging an ad-hoc solution based on the application of the Latent Dirichlet Allocation (LDA) algorithm. The result of the process consists in a set of statistics regarding the relationship among papers, topics, and their trend of publication on journals and conference proceedings. The validity of the method is demonstrated with an application to a dataset related to the scientific field of NLP, while its accuracy is assessed by the manual examination of the results by domain experts.
Bus Factor in Practice

Bus factor is a metric that identifies how resilient is the project to the sudden engineer turnover.It states the minimal number of engineers that have to be hit by a bus for a project to be stalled.Even though the metric is often discussed in the community, few studies consider its general relevance.Moreover, the existing tools for bus factor estimation focus solely on the data from version control systems, even though there exists other channels for knowledge generation and distribution.With a survey of 269 engineers, we find that the bus factor is perceived as an important problem in collective development, and determine the highest impact channels of knowledge generation and distribution in software development teams.We also propose a multimodal bus factor estimation algorithm that uses data on code reviews and meetings together with the VCS data.We test the algorithm on 13 projects developed at JetBrains and compared its results to the results of the state-of-theart tool by Avelino et al. against the ground truth collected in a survey of the engineers working on these projects.Our algorithm is slightly better in terms of both predicting the bus factor as well as key developers compared to the results of Avelino et al.Finally, we use the interviews and the surveys to derive a set of best practices to address the bus factor issue and proposals for the possible bus factor assessment tool.
A Fully Automated Methodology for the Selection and Extraction of Energy-Relevant Features for the Energy Consumption of Battery Electric Vehicles

Abstract Compared to traditional vehicles, battery electric vehicles (BEVs) have a limited driving range. Therefore, accurately estimating the range of BEVs is an important requirement to eliminate range anxiety, which describes the driver’s fear of getting stranded. However, range estimators used in currently available BEVs are not accurate enough. To overcome this problem, more precise energy estimation techniques have been investigated. Modeling the energy consumption of BEVs is essential to obtaining an accurate estimation. For accurately estimating the energy consumption, many non-deterministic influencing factors such as weather and traffic conditions, driving style, and the travel route need to be considered. Thus, reducing the possible feature space to improve estimation is necessary. In consequence, we propose a fully automatic methodology to select and extract a subset of energy-relevant features. Utilizing existing real-world data to investigate all types of influencing factors. Taking into account different segmentation methods, data scalers, feature selection, and extraction techniques, our methodology uses the full range of combinations to identify the combination that yields the best subset of features.
Multi-task learning for video anomaly detection

We propose a multi-task learning framework for video anomaly detection based on a novel pipeline. Our model contains two crossing streams, one stream employs the backbone of Attention-R2U-net for future frame prediction, while the other is designed based on an encoder–decoder network to reconstruct the input optical flow maps. In addition, the latent layers of the two streams are merged together and assigned with a Deep SVDD-based loss at each location individually. Through the combination of these three tasks, the two-stream-crossing pipeline can be trained end-to-end to provide a comprehensive evaluation for the anomaly targets. Experimental results on several popular benchmark datasets show that our model outperforms the state-of-the-art competing models, which can be applied to different types of anomalous targets and meanwhile achieves remarkable precision.
Innovative Sensor Design Method of Industrial Ceramic Product Modeling Based on Immune Optimization Algorithm

A new method based on immune optimization algorithm was proposed for modeling and design of industrial ceramic products. Based on the joint coding of fuzzy rules and membership function parameters, an improved immune optimization algorithm was used to realize the synchronous optimization of industrial ceramic product modeling. A superheated steam temperature control system is taken as an example to carry out simulation tests. The results show that the ratio of the unit volume to the side length is between 0 and 1, which is called the mass of the grid element. Zero is the worst quality and one is the best. Element aspect ratio and distortion degree are also basic inspection indexes. The average mass of vacuum glass is 0.86, the aspect ratio is 1.71, and the element distortion degree is 0.13, indicating that the grid quality is well divided. It is proved that using the improved immune genetic algorithm to optimize fuzzy control can overcome the defects of the traditional genetic algorithm and ensure the faster and more stable search for the optimal rule base and membership function.
Object Detection in Remote Sensing Images by Combining Feature Enhancement and Hybrid Attention

The objects in remote sensing images have large-scale variations, arbitrary directions, and are usually densely arranged, and small objects are easily submerged by background noises. They all hinder accurate object detection. To address the above problems, this paper proposes an object detection method combining feature enhancement and hybrid attention. Firstly, a feature enhancement fusion network (FEFN) is designed, which carries out dilated convolution with different dilation rates acting on the multi-layer features, and thus fuses multi-scale, multi-receptive field feature maps to enhance the original features. FEFN obtains more robust and discriminative features, which adapt to various objects with different scales. Then, a hybrid attention mechanism (HAM) module composed of pixel attention and channel attention is proposed. Through context dependence and channel correlation, introduced by pixel attention and channel attention respectively, HAM can make the network focus on object features and suppress background noises. Finally, this paper uses box boundary-aware vectors to determine the locations of objects and detect the arbitrary direction objects accurately, even if they are densely arranged. Experiments on public dataset DOTA show that the proposed method achieves 75.02% mAP, showing an improvement of 2.7% mAP compared with BBAVectors.
Rapid nighttime haze removal with color-gray layer decomposition

Nighttime haze usually reduces the visible distance and visual contrast of the nighttime scene. To resolve this problem, a color-gray layer decomposition method is presented to decompose a real-world image into color-world and gray-world layers in the RGB color space. Based on the presented layer decomposition, a new nighttime dehazing framework is proposed to remove glowing color, enhance illumination, and remove haze. Specifically, to correct nighttime hazy images, an effective glow decomposition approach is present to remove glowing color in the color-world layer. For low-light nighttime hazy scenes, gamma correction is applied to enhance illumination in the gray-world layer. After nighttime hazy image correction, to further refine the coarse estimation of the transmission and the atmospheric light, a relative weighted guided image filter is devised with a new relative edge-aware weighting. Additionally, a 2D atmospheric scattering model is established to well remove haze with the decomposed transmission and the fused local atmospheric light. Therefore, the proposed method can rapidly recover high-quality nighttime dehazing results and preserve the intrinsic color of the scene while avoiding color artifacts. Quantitative comparisons verify that the proposed nighttime dehazing framework can achieve excellent performance in nighttime haze removal against previous state-of-the-art methods.
A Deep Learning-Based Semantic Segmentation Architecture for Autonomous Driving Applications

In recent years, the development of smart transportation has accelerated research on semantic segmentation as it is one of the most important problems in this area. A large receptive field has always been the center of focus when designing convolutional neural networks for semantic segmentation. A majority of recent techniques have used maxpooling to increase the receptive field of a network at an expense of decreasing its spatial resolution. Although this idea has shown improved results in object detection applications, however, when it comes to semantic segmentation, a high spatial resolution also needs to be considered. To address this issue, a new deep learning model, the M-Net is proposed in this paper which satisfies both high spatial resolution and a large enough receptive field while keeping the size of the model to a minimum. The proposed network is based on an encoder-decoder architecture. The encoder uses atrous convolution to encode the features at full resolution, and instead of using heavy transposed convolution, the decoder consists of a multipath feature extraction module that can extract multiscale context information from the encoded features. The experimental results reported in the paper demonstrate the viability of the proposed scheme.
Quadratic Voting in Blockchain Governance

Governance in blockchain platforms is an increasingly important topic. A particular concern related to voting procedures is the formation of dominant positions, which may discourage participation of minorities. A main feature of standard majority voting is that individuals can indicate their preferences but cannot express the intensity of their preferences. This could sometimes be a drawback for minorities who may not have the opportunity to obtain their most desirable outcomes, even when such outcomes are particularly important for them. For this reason a voting method, which in recent years gained visibility, is quadratic voting (QV), which allows voters to manifest both their preferences and the associated intensity. In voting rounds, where in each round users express their preference over binary alternatives, what characterizes QV is that the sum of the squares of the votes allocated by individuals to each round has to be equal to the total number, budget, of available votes. That is, the cost associated with a number of votes is given by the square of that number, hence it increases quadratically. In the paper, we discuss QV in proof-of-stake-based blockchain platforms, where a user’s monetary stake also represents the budget of votes available in a voting session. Considering the stake as given, the work focuses mostly on a game theoretic approach to determine the optimal allocation of votes across the rounds. We also investigate the possibility of the so-called Sybil attacks and discuss how simultaneous versus sequential staking can affect the voting outcomes with QV.
Information extraction from free text for aiding transdiagnostic psychiatry: constructing NLP pipelines tailored to clinicians’ needs

Developing predictive models for precision psychiatry is challenging because of unavailability of the necessary data: extracting useful information from existing electronic health record (EHR) data is not straightforward, and available clinical trial datasets are often not representative for heterogeneous patient groups. The aim of this study was constructing a natural language processing (NLP) pipeline that extracts variables for building predictive models from EHRs. We specifically tailor the pipeline for extracting information on outcomes of psychiatry treatment trajectories, applicable throughout the entire spectrum of mental health disorders ("transdiagnostic").A qualitative study into beliefs of clinical staff on measuring treatment outcomes was conducted to construct a candidate list of variables to extract from the EHR. To investigate if the proposed variables are suitable for measuring treatment effects, resulting themes were compared to transdiagnostic outcome measures currently used in psychiatry research and compared to the HDRS (as a gold standard) through systematic review, resulting in an ideal set of variables. To extract these from EHR data, a semi-rule based NLP pipeline was constructed and tailored to the candidate variables using Prodigy. Classification accuracy and F1-scores were calculated and pipeline output was compared to HDRS scores using clinical notes from patients admitted in 2019 and 2020.Analysis of 34 questionnaires answered by clinical staff resulted in four themes defining treatment outcomes: symptom reduction, general well-being, social functioning and personalization. Systematic review revealed 242 different transdiagnostic outcome measures, with the 36-item Short-Form Survey for quality of life (SF36) being used most consistently, showing substantial overlap with the themes from the qualitative study. Comparing SF36 to HDRS scores in 26 studies revealed moderate to good correlations (0.62-0.79) and good positive predictive values (0.75-0.88). The NLP pipeline developed with notes from 22,170 patients reached an accuracy of 95 to 99 percent (F1 scores: 0.38 - 0.86) on detecting these themes, evaluated on data from 361 patients.The NLP pipeline developed in this study extracts outcome measures from the EHR that cater specifically to the needs of clinical staff and align with outcome measures used to detect treatment effects in clinical trials.
Product Model Derivation from Feature Model and Formal Specification

Product derivation is the process of building a specific product from a software product line. Effective product derivation can improve software reuse productivity. Existing methods can only obtain abstract feature models, lacking detailed specifications of individual features. They are more about deriving code assets or class diagram templates without precise model descriptions for specific products. This article proposes a product derivation approach to obtain a formal specification of a specific product based on the feature model and formal specification. We use the integration ordering and behavior preserving integration techniques to integrate the formal specification for each feature pair. The method is divided into two steps. First, it determines the feature formal specification integration ordering based on the feature model. Then, the behavior-preserving integration will be conducted for pairs, including declaration integration, functional scenario path generation, and function integration based on path matching. Behavior preserving integration guarantees consistent behavior to ensure the quality of the formal specification after integration. Finally, we developed a support tool to conduct a case study. The tool first guides the user to perform feature functional scenario path matching, then performs functional integration based on the matching results and repeats the above steps to generate a product model. The result indicates that our method facilitates the derivation process and improves the quality of the generated models.
A Compact Parallel Pruning Scheme for Deep Learning Model and Its Mobile Instrument Deployment

In the single pruning algorithm, channel pruning or filter pruning is used to compress the deep convolution neural network, and there are still many redundant parameters in the compressed model. Directly pruning the filter will largely cause the loss of key information and affect the accuracy of model classification. To solve these problems, a parallel pruning algorithm combined with image enhancement is proposed. Firstly, in order to improve the generalization ability of the model, a data enhancement method of random erasure is introduced. Secondly, according to the trained batch normalization layer scaling factor, the channels with small contribution are cut off, the model is initially thinned, and then the filters are pruned. By calculating the geometric median of the filters, redundant filters similar to them are found and pruned, and their similarity is measured by calculating the distance between filters. Pruning was done using VGG19 and DenseNet40 on cifar10 and cifar100 data sets. The experimental results show that this algorithm can improve the accuracy of the model, and at the same time, it can compress the calculation and parameters of the model to a certain extent. Finally, this method is applied in practice, and combined with transfer learning, traffic objects are classified and detected on the mobile phone.
5G-BSS: 5G-Based Universal Blockchain Smart Sensors

A smart sensor is a sensor with information processing functions. It is the product of the combination of sensor integration and a microprocessor. It has the characteristics of intelligence, networking and high precision. It has been widely used in aerospace, aviation, intelligent transportation, industrial control and medical and health care. However, in some specific application scenarios with high data security requirements and low transmission delay, such as environmental detection, transportation, etc., smart sensors have three obvious shortcomings. First, the data transmission delay is high. Second, the confidentiality and integrity of the data transmission process cannot be effectively guaranteed. Third, centralized data storage is easily leaked and tampered with by malicious users and semi-trusted administrators. Therefore, a 5G-based blockchain smart sensor 5G-BSS was designed. 5G-BSS has three innovation points. First, the 5G communication module enables the smart sensor 5G-BSS. The 5G communication module is integrated into the smart sensor 5G-BSS to reduce the delay of data transmission and improve the speed and reliability of data transmission. Second, cryptographic algorithms enable the smart sensor 5G-BSS. The data encryption module of the smart sensor 5G-BSS improves the confidentiality and integrity of the data transmission process. Third, blockchain empowers the smart sensor 5G-BSS. The blockchain client is integrated into the smart sensor 5G-BSS to ensure the centralized storage of data and prevent data leakage and tampering by semi-trusted administrators. The operation process of the hardware and software architecture is described in detail and tested on the Fisco-Bcos. The experimental results show that 5G-BSS not only has fast data transmission speed but also can effectively guarantee the integrity, confidentiality and availability of data. 5G-BSS is suitable for application scenarios with high requirements for data security and data transmission, such as environmental monitoring, intelligent transportation, autonomous driving, etc.