Reviewerly/exHarmony huggingface dataset:
Multi-Level and Multi-Scale Feature Aggregation Network for Semantic Segmentation in Vehicle-Mounted Scenes

The main challenges of semantic segmentation in vehicle-mounted scenes are object scale variation and trading off model accuracy and efficiency. Lightweight backbone networks for semantic segmentation usually extract single-scale features layer-by-layer only by using a fixed receptive field. Most modern real-time semantic segmentation networks heavily compromise spatial details when encoding semantics, and sacrifice accuracy for speed. Many improving strategies adopt dilated convolution and add a sub-network, in which either intensive computation or redundant parameters are brought. We propose a multi-level and multi-scale feature aggregation network (MMFANet). A spatial pyramid module is designed by cascading dilated convolutions with different receptive fields to extract multi-scale features layer-by-layer. Subseqently, a lightweight backbone network is built by reducing the feature channel capacity of the module. To improve the accuracy of our network, we design two additional modules to separately capture spatial details and high-level semantics from the backbone network without significantly increasing the computation cost. Comprehensive experimental results show that our model achieves 79.3% MIoU on the Cityscapes test dataset at a speed of 58.5 FPS, and it is more accurate than SwiftNet (75.5% MIoU). Furthermore, the number of parameters of our model is at least 53.38% less than that of other state-of-the-art models.
Investigating factors that affect the adoption of COVID-19 contact-tracing apps: A privacy calculus perspective.

On June 16, 2020, the German government issued a coronavirus disease 19 (COVID-19) contact-tracing app whose privacy settings were fiercely debated in advance.Contrary, potential benefits regarding the acceleration of contact tracing were advertised.This study investigated the relative importance of privacy concerns and perceived benefits in relation to the decision to use the app adopting a privacy calculus perspective extended by trust in app privacy and technological knowledge about the app.Results of a representative German sample (N = 952) revealed that privacy concerns were negatively related to app usage, and perceived benefits and knowledge were positively related to actual app adoption.Additionally, perceiving app benefits was the most important factor for (continued) usage intention, while privacy concerns did not play a role anymore.Trust was strongly negatively associated with privacy concerns and strongly positively with perceived benefits.Results are discussed concerning consequences for communication strategies when launching technology whose adoption should help in mitigating dangers of pandemics.
An Effective and Comprehensive Image Super Resolution Algorithm Combined With a Novel Convolutional Neural Network and Wavelet Transform

In order to further improve the reconstruction effect of the image super resolution algorithm, this paper proposes an image super resolution algorithm combining deep learning and wavelet transform (ISRDW). In terms of network design, it is not only simple in structure, but also more effective in capturing image details compared with other neural network structures. At the same time, cross-connection and residual learning methods are used to reduce the difficulty of the training model. In terms of loss function, this paper uses the loss generated in the original image space domain and the wavelet domain to strengthen the constraint of network training. Experimental results show that the algorithm proposed in this paper achieves better results under different data sets and different evaluation indexes.
On the Dominant Factors of Civilian-Use Drones: A Thorough Study and Analysis of Cross-Group Opinions Using a Triple Helix Model (THM) with the Analytic Hierarchy Process (AHP)

This study explores the experts’ opinions during the consultation stage before law-making for civilian drones. A thorough literature study is first undertaken to have the set of influencing factors that should be suitable for the investigation from the perspective of designing and selecting civilian drones. Several rounds of surveys using the Delphi method, followed by an analytic hierarchy process (AHP), are performed to conform to the organized tree structure of constructs and factors and to obtain the knowledge about the opinions of the expert groups, with the expert sample being intentionally partitioned into three opinion groups at the beginning: academia (A), industry (I), and research institutes (R). Doing so facilitates a “mind-mining” process using the triple helix model (THM), while the opinions across the groups can also be visualized and compared. This exploits a new set of knowledge for the design and selection of civilian drones on a scientific yet empirical basis, and the observed differences and similarities among the groups may benefit their future negotiations to propose the drafts for regulating the design, manufacturing, and uses of civilian drones. As several significant implications and insights are also drawn and gained from the abovementioned results eventually, some possible research directions are worthwhile. The proposed hybrid methodological flow is another novelty.
The Implementation of Machine Learning in Lithofacies Classification using Multi Well Logs Data

Lithofacies classification is a process to identify rock lithology by indirect measurements. Usually, the classification is processed manually by an experienced geoscientist. This research presents an automated lithofacies classification using a machine learning method to increase computational power in shortening the lithofacies classification process's time consumption. The support vector machine (SVM) algorithm has been applied successfully to the Damar field, Indonesia. The machine learning input is various well-log data sets, e.g., gamma-ray, density, resistivity, neutron porosity, and effective porosity. Machine learning can classify seven lithofacies and depositional environments, including channel, bar sand, beach sand, carbonate, volcanic, and shale. The classification accuracy in the verification phase with trained lithofacies class data reached more than 90%, while the accuracy in the validation phase with beyond trained data reached 65%. The classified lithofacies then can be used as the input for describing lateral and vertical rock distribution patterns.
Agile Practices and Impacts on Project Success

The creation of Agile frameworks signified the development of practices specifically aimed at delivering projects -in an unpredictable world -on time, within budget and to the required quality.The purpose of this study is to present the potential effects of common Agile practices on conditions for project success.Two literature reviews are conducted.The first literature review identifies commonly reported Agile practices, while the second literature review focuses on these Agile practices' reported effects.The two literature reviews identify twelve commonly reported Agile practices and their reported effects on six conditions for project success.Some Agile practices are shown to be more common than others.An abundance of identified terms used for Agile practices complicates the review of the existing empirical studies and the establishment of a common research agenda.Furthermore, the research shows that most of the examined Agile practices have both positive and negative effects on the conditions for project success.Only a few of the commonly reported Agile practices are shown to have a solely positive effect.The study identifies variations in the amount of available research on the effects of the various Agile practices.For research, this study identifies Agile practices and effects that need further attention.For practice, this study shows that Agile practices should be implemented with the desired effects, and the organizational and contextual conditions, in mind.
Space Debris Tracking with the Poisson Labeled Multi-Bernoulli Filter

This paper presents a Bayesian filter based solution to the Space Object (SO) tracking problem using simulated optical telescopic observations. The presented solution utilizes the Probabilistic Admissible Region (PAR) approach, which is an orbital admissible region that adheres to the assumption of independence between newborn targets and surviving SOs. These SOs obey physical energy constraints in terms of orbital semi-major axis length and eccentricity within a range of orbits of interest. In this article, Low Earth Orbit (LEO) SOs are considered. The solution also adopts the Partially Uniform Birth (PUB) intensity, which generates uniformly distributed births in the sensor field of view. The measurement update then generates a particle SO distribution. In this work, a Poisson Labeled Multi-Bernoulli (PLMB) multi-target tracking filter is proposed, using the PUB intensity model for the multi-target birth density, and a PAR for the spatial density to determine the initial orbits of SOs. Experiments are demonstrated using simulated SO trajectories created from real Two-Line Element data, with simulated measurements from twelve telescopes located in observatories, which form part of the Falcon telescope network. Optimal Sub-Pattern Assignment (OSPA) and CLEAR MOT metrics demonstrate encouraging multi-SO tracking results even under very low numbers of observations per SO pass.
ENHANCED MINERAL QUANTIFICATION AND UNCERTAINTY ANALYSIS FROM DOWNHOLE SPECTROSCOPY LOGS USING VARIATIONAL AUTOENCODERS

This paper describes an innovative machine learning application, based on variational autoencoder frameworks, to quantify the concentrations and associated uncertainties of common minerals in sedimentary formations using the measurement of atomic element concentrations from geochemical spectroscopy logs as inputs. The algorithm comprises an input(s), encoder, decoder, output(s), and a novel cost function to optimize the model coefficients during training. The input to the algorithm is a set of dry-weight concentrations of atomic elements with their associated uncertainty. The first output is a set of dry-weight fractions of fourteen minerals, and the second output is a set of reconstructed dry-weight concentrations of the original elements. Both sets of outputs include estimates of uncertainty on their predictions. The encoder and decoder are multilayer feed-forward artificial neural networks (ANN), with their coefficients (weights) optimized during calibration (training). The cost function simultaneously minimizes error (the accuracy metric) and variance (the precision or robustness metric) on the mineral and reconstructed elemental outputs. Training of the weights is done using a set of several-thousand core samples with independent, high-fidelity elemental and mineral (quartz, potassium-feldspar, plagioclase-feldspar, illite, smectite, kaolinite, chlorite, mica, calcite, dolomite, ankerite, siderite, pyrite, and anhydrite) data. The algorithm provides notable advantages over existing methods to estimate formation lithology or mineralogy relying on simple linear, empirical, or nearest-neighbor functions. The ANN numerically capture the multi-dimensional and nonlinear geochemical relationship (mapping) between elements and minerals that is insufficiently described by prior methods. Training is iterative via backpropagation and samples from Gaussian distributions on each of the elemental inputs, rather than single values, for every sample at each iteration (epoch). These Gaussian distributions are chosen to specifically represent the unique statistical uncertainty of the dry-weight elements in the logging measurements. Sampling from Gaussian distributions during training reduces the potential for overfitting, provides robustness for log interpretations, and further enables a calibrated estimate of uncertainty on the mineral and reconstructed elemental outputs, all of which are lacking in prior methods. The framework of the algorithm is purposefully generalizable that it can be adapted across geochemical spectroscopy tools. The algorithm reasonably approximates a ‘global-average’ model that requires neither different calibrations nor expert parameterization or intervention for interpreting common oilfield sedimentary formations, although the framework is again purposefully generalizable so it can be optimized for local environments where desirable. The paper showcases field application of the method for estimating mineral type and abundance in oilfield formations from wellbore logging measurements.
Federated Reinforcement Learning Acceleration Method for Precise Control of Multiple Devices

Nowadays, Reinforcement Learning (RL) is applied to various real-world tasks and attracts much attention in the fields of games, robotics, and autonomous driving.It is very challenging and devices overwhelming to directly apply RL to real-world environments.Due to the reality gap simulated environment does not match perfectly to the real-world scenario and additional learning cannot be performed.Therefore, an efficient approach is required for RL to find an optimal control policy and get better learning efficacy.In this paper, we propose federated reinforcement learning based on multi agent environment which applying a new federation policy.The new federation policy allows multi agents to perform learning and share their learning experiences with each other e.g., gradient and model parameters to increase their learning level.The Actor-Critic PPO algorithm is used with four types of RL simulation environments, OpenAI Gym's CartPole, MoutainCar, Acrobot, and Pendulum.In addition, we did real experiments with multiple Rotary Inverted Pendulum (RIP) to evaluate and compare the learning efficiency of the proposed scheme with both environments.
The Impact of Social Networks on the Development of Students’ Social Competence

The article analyzes the possibilities which virtual social networks provide for the development of youth’s social competence. The authors analyze the results of the survey on the students’ use of virtual social networks for communication. To achieve the purpose of the research and to determine the essence and significance of social competence we used methods of analysis, synthesis and generalization of research works; the method of observation was used to identify the peculiarities of communication between young people in social networks; the method of questionnaire was used to collect information about the students’ preferences regarding the choice of specific virtual social networks for communication. The results of observations and questionnaires confirmed the suggestion that the time young people spend on online communication and surfing the Internet is increasing. The survey results of 107 students studying in Year 1-4 at the pedagogical university (Ukraine) let us make a conclusion that they often stay on virtual social networks browsing them from 2 to 4 times a day (51,4%). Almost a third of students are constantly on social networks. The most popular ones appeared to be Instagram (83,1%) and Facebook (28%). With the help of social networks students actively communicate with group mates (80,4%), friends and family (78,5%). Further research is planned to consider the ways how to avoid the negative effects of the Internet on students’ personality.
PrivacyGuard

The Internet of Things (IoT) devices have been increasingly deployed in smart homes and smart buildings to monitor and control their environments. The Internet traffic data produced by these IoT devices are collected by Internet Service Providers (ISPs) and IoT device manufacturers, and often shared with third-parties to maintain and enhance user services. Unfortunately, extensive recent research has shown that on-path adversaries can infer and fingerprint users' sensitive privacy information such as occupancy and user in-home activities by analyzing IoT network traffic traces. Most recent approaches that aim at defending against these malicious IoT traffic analytics can not sufficiently protect user privacy with reasonable traffic overhead. In particular, many approaches did not consider practical limitations, e.g., network bandwidth, maximum package injection rate or actual user in-home behavior in their design.
Prediction of Flight Status of Logistics UAVs Based on an Information Entropy Radial Basis Function Neural Network

Aiming at addressing the problems of short battery life, low payload and unmeasured load ratio of logistics Unmanned Aerial Vehicles (UAVs), the Radial Basis Function (RBF) neural network was trained with the flight data of logistics UAV from the Internet of Things to predict the flight status of logistics UAVs. Under the condition that there are few available input samples and the convergence of RBF neural network is not accurate, a dynamic adjustment method of RBF neural network structure based on information entropy is proposed. This method calculates the information entropy of hidden layer neurons and output layer neurons, and quantifies the output information of hidden layer neurons and the interaction information between hidden layer neurons and output layer neurons. The structural design and optimization of RBF neural network were solved by increasing the hidden layer neurons or disconnecting unnecessary connections, according to the connection strength between neurons. The steepest descent learning algorithm was used to correct the parameters of the network structure to ensure the convergence accuracy of the RBF neural network. By predicting the regression values of the flight status of logistics UAVs, it is demonstrated that the information entropy-based RBF neural network proposed in this paper has good approximation ability for the prediction of nonlinear systems.
Learning to Organize a Bag of Words into Sentences with Neural Networks: An Empirical Study

Sequential information, a.k.a., orders, is assumed to be essential for processing a sequence with recurrent neural network or convolutional neural network based encoders.However, is it possible to encode natural languages without orders?Given a bag of words from a disordered sentence, humans may still be able to understand what those words mean by reordering or reconstructing them.Inspired by such an intuition, in this paper, we perform a study to investigate how "order" information takes effects in natural language learning.By running comprehensive comparisons, we quantitatively compare the ability of several representative neural models to organize sentences from a bag of words under three typical scenarios, and summarize some empirical findings and challenges, which can shed light on future research on this line of work.
Machine Learning for the Prediction of Returned Checks Closing Status

Data mining is one of the most valuable tools for several applications, especially in banking sector. It can be used in risk analysis of loans, detecting fraud in real time and classifying customers’ interests for retention and services purposes. The main objective of this research is to develop a predictive model that can expect the closure status of returned cases in Qatar. This would be of valuable help for the banking sector as well as The Ministry of Justice. The banking sector aims at anticipating when the check will be paid and the Ministry of Justice holds the burden to assign more judges to look into cases of this kind. To produce the predictive model, four-year worth of data (2014-2017) on returned checks as registered at the Police Criminal System in The State of Qatar were obtained. The data was first cleaned from improper input and the attributes collected were reduced from 16 to 10 eliminating those with the lowest to no correlation. Seven classifiers were used in Python code to generate the prediction model: Random Forest (RF), Gaussian Naive Bayes (NB), kNearest Neighbor (KNN), Logistic Regression (LR), Decision Tree Classifier (CART), Linear Discriminant (LD) and Support Vector Machine (SVM). The evaluation of the classifiers was based on three main criteria: accuracy, running time and memory usage. The results show that SVM was not able to converge to any output while RF was the most accurate with 84.4%. Moderate performance in all three criteria was obtained by KNN and LR classifiers, while CART had the second best accuracy with moderate running time and memory usage. It was also found that the size of dataset and the correlation between attributes hugely affect the performance of each algorithm. Moreover, a logarithmic correlation between the accuracy and running time of the classifiers was obtained with R2 of 0.94. To this end, several recommendations for future work have been proposed to facilitate the use of such predictive models with high confidence in Qatar
MEAN: Multi-head Entity Aware Attention Networkfor Political Perspective Detection in News Media

The way information is generated and disseminated has changed dramatically over the last decade.Identifying the political perspective shaping the way events are discussed in the media becomes more important due to the sharp increase in the number of news outlets and articles.Previous approaches usually only leverage linguistic information.However, news articles attempt to maintain credibility and seem impartial.Therefore, bias is introduced in subtle ways, usually by emphasizing different aspects of the story.In this paper, we propose a novel framework that considers entities mentioned in news articles and external knowledge about them, capturing the bias with respect to those entities.We explore different ways to inject entity information into the text model.Experiments show that our proposed framework achieves significant improvements over the standard text models, and is capable of identifying the difference in news narratives with different perspectives.
A Case Study of ESL Students’ Remote Speaking Class Learning Experiences in a Canada University During the COVID-19 Pandemic

The COVID-19 pandemic forced a transfer from face-to-face (F2F) learning to remote online learning in universities worldwide. A university in southern Ontario delivered English courses to language learners living globally. This study, adopting a photo-production visual method, explored four English as a second language (ESL) students’ perceptions of this online learning compared to typical F2F learning and investigated activities enabling speaking opportunities and students’ expectations for online learning. The results showed that students perceived minor differences between online learning and F2F learning, including a non-academic English environment, a sense of community, and instant communications. The learning process involved interactive and collaborative discussions and presentations that allow students’ speaking opportunities. The online discussions contributed to students’ confidence but lacked adequate feedback towards students’ speaking skills. Students expected more types of learning activities that contribute to collaborations among peers, a sense of belonging to the online community, and examination orientated English skills.
Predicting Discourse Trees from Transformer-based Neural Summarizers

Previous work indicates that discourse information benefits summarization.In this paper, we explore whether this synergy between discourse and summarization is bidirectional, by inferring document-level discourse trees from pre-trained neural summarizers.In particular, we generate unlabeled RST-style discourse trees from the self-attention matrices of the transformer model.Experiments across models and datasets reveal that the summarizer learns both, dependency-and constituencystyle discourse information, which is typically encoded in a single head, covering long-and short-distance discourse dependencies.Overall, the experimental results suggest that the learned discourse information is general and transferable inter-domain 1 .