Reviewerly/exHarmony huggingface dataset:
A Code Reviewer Recommendation Approach Based on Attentive Neighbor Embedding Propagation

Code review as an effective software quality assurance practice has been widely applied in many open-source software communities. However, finding a suitable reviewer for certain codes can be very challenging in open-source communities due to the difficulty of learning the characteristics of reviewers and the code-reviewer interaction sparsity in open-source software communities. To tackle this problem, most previous approaches focus on learning developers’ capabilities and experiences and recommending suitable developers based on their historical interactions. However, such approaches usually suffer from data-sparsity and noise problems, which may reduce the recommendation accuracy. In this paper, we propose an attentive neighbor embedding propagation enhanced code reviewer recommendation framework (termed ANEP). In ANEP, we first construct the reviewer–code interaction graph and learn the semantic representations of the reviewer and code based on the transformer model. Then, we explicitly explore the attentive high-order embedding propagation of reviewers and code and refine the representations along their neighbors. Finally, to evaluate the effectiveness of ANEP, we conduct extensive experiments on four real-world datasets. The experimental results show that ANEP outperforms other state-of-the-art approaches significantly.
Online Education About Covid 19 Using Zoom and Android App

The use of online learning methods has increased since the Large-Scale Social Restrictions (PSBB) were set by the government.The current condition is a challenge for the world of nursing education to immediately respond by conducting online learning using e-learning developed by related institutions or using learning support resources that are already available in cyberspace.The need for improvisation in the learning method becomes a rationale in the development of methods and infrastructure in the world of education.One system that can be used is an Android-based open source that can be modified and used by the majority of students.The purpose of this study was to determine the effectiveness of the combination of online classical learning in combination with android-based applications.This study uses a quasi-experimental design.The material presented was updates about covid 19, prevention of the spread of covid 19 in the perspective of IPCN, nursing care for Covid 19 patients, mental health and psychosocial approaches to Covid 19 patients.The results show that the combination of learning using online classics and combinations with android-based applications is effective for increasing student knowledge.
QMRNet: Quality Metric Regression for EO Image Quality Assessment and Super-Resolution

The latest advances in super-resolution have been tested with general-purpose images such as faces, landscapes and objects, but mainly unused for the task of super-resolving earth observation images. In this research paper, we benchmark state-of-the-art SR algorithms for distinct EO datasets using both full-reference and no-reference image quality assessment metrics. We also propose a novel Quality Metric Regression Network (QMRNet) that is able to predict the quality (as a no-reference metric) by training on any property of the image (e.g., its resolution, its distortions, etc.) and also able to optimize SR algorithms for a specific metric objective. This work is part of the implementation of the framework IQUAFLOW, which has been developed for the evaluation of image quality and the detection and classification of objects as well as image compression in EO use cases. We integrated our experimentation and tested our QMRNet algorithm on predicting features such as blur, sharpness, snr, rer and ground sampling distance and obtained validation medRs below 1.0 (out of N = 50) and recall rates above 95%. The overall benchmark shows promising results for LIIF, CAR and MSRN and also the potential use of QMRNet as a loss for optimizing SR predictions. Due to its simplicity, QMRNet could also be used for other use cases and image domains, as its architecture and data processing is fully scalable.
Reducing Medication Math Anxiety of Prelicensure Nursing Students Using Social Media Tutorials

First-semester prelicensure nursing students commonly report experiencing math anxiety that negatively affects academic performance and triggers fear of causing patient harm. Students often turn to social media platforms to supplement their learning experience.To explore the feasibility of using Instagram tutorials to reduce math anxiety of first-semester prelicensure nursing students.A quasi-experimental descriptive survey design was used to evaluate the utility of posting supplemental math tutorials on Instagram and examined the effect of tutorials on math anxiety levels. A convenience sample of 20 students used the tutorials. Math anxiety levels were evaluated using the Single-Item Math Anxiety Scale.A statistically significant inverse relationship was observed between math anxiety levels and tutorial use. The Instagram platform was familiar to students and simple to access. Brief tutorials appealed to audio-visual learners.Using Instagram-based tutorials is feasible and appears effective in reducing medication math anxiety.
Predictive Uncertainty Estimation for Camouflaged Object Detection

Uncertainty is inherent in machine learning methods, especially those for camouflaged object detection aiming to finely segment the objects concealed in background. The strong enquote center bias of the training dataset leads to models of poor generalization ability as the models learn to find camouflaged objects around image center, which we define as enquote model bias. Further, due to the similar appearance of camouflaged object and its surroundings, it is difficult to label the accurate scope of the camouflaged object, especially along object boundaries, which we term as enquote data bias. To effectively model the two types of biases, we resort to uncertainty estimation and introduce predictive uncertainty estimation technique, which is the sum of model uncertainty and data uncertainty, to estimate the two types of biases simultaneously. Specifically, we present a predictive uncertainty estimation network (PUENet) that consists of a Bayesian conditional variational auto-encoder (BCVAE) to achieve predictive uncertainty estimation, and a predictive uncertainty approximation (PUA) module to avoid the expensive sampling process at test-time. Experimental results show that our PUENet achieves both highly accurate prediction, and reliable uncertainty estimation representing the biases within both model parameters and the datasets.
Dirichlet process mixture models for the analysis of repeated attempt designs

In longitudinal studies, it is not uncommon to make multiple attempts to collect a measurement after baseline. Recording whether these attempts are successful provides useful information for the purposes of assessing missing data assumptions. This is because measurements from subjects who provide the data after numerous failed attempts may differ from those who provide the measurement after fewer attempts. Previous models for these designs were parametric and/or did not allow sensitivity analysis. For the former, there are always concerns about model misspecification and for the latter, sensitivity analysis is essential when conducting inference in the presence of missing data. Here, we propose a new approach which minimizes issues with model misspecification by using Bayesian nonparametrics for the observed data distribution. We also introduce a novel approach for identification and sensitivity analysis. We re-analyze the repeated attempts data from a clinical trial involving patients with severe mental illness and conduct simulations to better understand the properties of our approach.
Channel polarization of two-dimensional-input quantum symmetric channels

Being attracted by the property of classical polar code, researchers are trying to find its analogue in quantum fields, which is called quantum polar code. The first step and the key to design quantum polar code is to find out for the quantity which can measure the quality of quantum channels, whether there is a polarization phenomenon which is similar to classical channel polarization. Coherent information is believed to be the quantum analogue of classical mutual information and the quantity to measure the capacity of quantum channel. In this paper, we define a class of quantum channels called quantum symmetric channels and prove that for quantum symmetric channels, under the similar channel combining and splitting process as in the classical channel polarization, the maximum single-letter coherent information of the coordinate channels will polarize. That is to say, there is a channel polarization phenomenon in quantum symmetric channels.
Performing Accents of Stage Speech Expression in the Ukrainian Opera (from the Work Experience of Ivan Patorzhynsky)

The author considered the relationship between words, music and stage action in Ukrainian classical opera. The stage language of the artist is characterized as the most powerful means of influencing the listener, thanks to which the ideological content and inner world of the character (singer-actor) is conveyed to the audience. The process of the singer-actor's work on creating a complete vocal and stage image with the use of spoken dialogues is analyzed. The peculiarities of the implementation of musical and theatrical works by means of stage broadcasting in an opera on national themes, taking into account the mentality of the Ukrainian people (peculiarities of life, culture, customs, rites of various social strata of the population, etc.) have been revealed. The specific methods of emotional and meaningful accents of the expressiveness of stage speech in Ukrainian opera are determined, using the example of Ivan Patorzhynskyi's performance. An organic combination of character-comedic features and pathos was revealed in Ivan Patorzhynskyi's creation of a complex and multifaceted image of Karas in S. Gulak-Artemovsky's opera "Zaporozhets za Danube" (Zaporozhian Beyond the Danube). The personal intonation and emotional techniques of the outstanding artist in the embodiment of the bright and unique image of Vyborny Makogonenko in M. Lysenko's opera "Natalka Poltavka" were clarified. The resolute, heroic intonations of Ivan Patorzhynskyi in the image of Taras as a brave, wise commander and his deep drama and tragic experiences as a father in the scene of the murder of his traitorous son in M. Lysenko's opera "Taras Bulba" are outlined. The professional approach and thorough search work of the master in creating socio-psychological portraits of the characters embodied by the actor in the specified operas are proven. Further scientific research is predicted to identify new synthesized means of expression in the combination of music, speech and stage action in today's realities.
An interpretable method for automated classification of spoken transcripts and written text

We investigate the differences between spoken language (in the form of radio show transcripts) and written language (Wikipedia articles) in the context of text classification. We present a novel, interpretable method for text classification, involving a linear classifier using a large set of n-gram features, and apply it to a newly generated data set with sentences originating either from spoken transcripts or written text. Our classifier reaches an accuracy less than 0.02 below that of a commonly used classifier (DistilBERT) based on deep neural networks (DNNs). Moreover, our classifier has an integrated measure of confidence, for assessing the reliability of a given classification. An online tool is provided for demonstrating our classifier, particularly its interpretable nature, which is a crucial feature in classification tasks involving high-stakes decision-making. We also study the capability of DistilBERT to carry out fill-in-the-blank tasks in either spoken or written text, and find it to perform similarly in both cases. Our main conclusion is that, with careful improvements, the performance gap between classical methods and DNN-based methods may be reduced significantly, such that the choice of classification method comes down to the need (if any) for interpretability.
Graph attentive matrix factorization for social recommendation

Abstract Recommender systems commonly encounter with the problems of data sparsity and cold start. Recently, social recommendation has emerged with the rapid expansion of social platforms, offering an opportunity to alleviate such two obstacles. Nevertheless, there are still two key limitations in existing studies. From the perspective of model design, previous social recommenders only consider the influence of a user's direct friends or uniformly treat the influences from different friends. From the perspective of model learning, most of them apply a sampling‐based optimization strategy, which requires high‐quality positive and negative samples. In light of the aforementioned limitations, we propose a new probabilistic method, named Graph Attentive Matrix Factorization (GAMF). Our method not only explicitly captures high‐order social relationships, but also adopts an attention mechanism to automatically pick up different influences between friends. Moreover, we develop an efficient optimization algorithm to learn model parameters in a non‐sampling manner. Extensive experiments on four large‐scale datasets show the superiority of GAMF over state‐of‐the‐art recommenders, especially under the cold start scenario.
Spectrogram Filtering and Ridge Graph Fitting Based Time Frequency Analysis

Since a frequency modulation signal can be approximated by polynomial chirplet in a local time window, polynomial chirplet transform has been applied to acoustic signal processing, radar Doppler analysis and gravity wave analysis. However, the direct implementation of a polynomial chirplet transform has extremely high computational cost due to its high dimensional polynomial chirplet parameter space. In this paper, we propose a spectrogram time-frequency filtering and ridge graph polynomial fitting approach to estimate polynomial chirplet parameters for the time-frequency analysis. In the proposed method, a low dimensional spectrogram ridge graph fitting is developed to extract high dimensional polynomial chirplet parameters for the computational cost reduction. Furthermore, the spectrogram filtering in the time-frequency space is proposed to improve the reliability of spectrogram ridge extraction, and a ridge interpolation technique is recommended to improve the accuracy of ridge extraction. Test results show that the proposed method has a low computational cost, high reliability and accuracy for extracting polynomial chirplet parameters.
Belief Mining in Persian Texts Based on Deep Learning and Users' Opinions (revised December 2022)

Belief mining and the study of public opinion provide valuable information. Analyzing the feelings and belief mining of social media data leads to understanding users' opinions and has wide applications in decision making and policymaking. This article applies a new method based on deep learning to solve the problems of belief mining for Persian comments on the Twitter. In this method, first, the data is preprocessed with a deep neural network and then classified into political, cultural, economic, and sports classes, and the sentimental polarity is obtained. SentiPers is applied on four different datasets from Persian Twitter, Digikala store, Google translator, and synonym for evaluation. Then the results are compared with other machine learning and deep learning methods such as neural network, support vector machine, DNN, CNN, and LSTM. Python software has been used to implement this method. The accuracy of the proposed word embedding method for LSTM, CNN, DNN on the SentiPres dataset is 0.931, 0.923, 0.916 respectively. For the TF-IDF method, it is 0.837, 0.863, 0.883 respectively. that the accuracy of LSTM-WSD, CNN-WSD model has increased by 8% and 6% compared to TF-IDF. The results show that the LSTM and Word embedding methods work best.
FileDAG: A Multi-Version Decentralized Storage Network Built on DAG-Based Blockchain

Decentralized Storage Networks (DSNs) can gather storage resources from mutually untrusted providers and form worldwide decentralized file systems. Compared to traditional storage networks, DSNs are built on top of blockchains, which can incentivize service providers and ensure strong security. However, existing DSNs face two major challenges. First, deduplication can only be achieved at the directory-level. Missing file-level deduplication leads to unavoidable extra storage and bandwidth cost. Second, current DSNs realize file indexing by storing extra metadata while blockchain ledgers are not fully exploited. To overcome these problems, we propose FileDAG, a DSN built on DAG-based blockchain to support file-level deduplication in storing multi-versioned files. When updating files, we adopt an increment generation method to calculate and store only the increments instead of the entire updated files. Besides, we introduce a two-layer DAG-based blockchain ledger, by which FileDAG can provide flexible and storage-saving file indexing by directly using the blockchain database without incurring extra storage overhead. We implement FileDAG and evaluate its performance with extensive experiments. The results demonstrate that FileDAG outperforms the state-of-the-art industrial DSNs considering storage cost and latency.
BELIEVE: Privacy-Aware Secure Multi-Party Computation for Real-Time Connected and Autonomous Vehicles and Micro-Mobility Data Validation Using Blockchain—A Study on New York City Data

Connected and autonomous vehicles, as well as micro-mobility devices (e-scooters, e-bikes, and smartphones), constantly generate tremendous amounts of mobility data, comprising spatio-temporal trajectories and other sensory data, the sharing of which can assist in different safety and intelligent transportation system applications. However, constant sharing of such data for real-time mobility decisions could violate the privacy of respective users. At the same time, malicious users could generate and share false data, which would be potentially harmful for stakeholders relying on this data for their safety and mobility decisions. In this paper, we propose a new framework: Blockchain-Enabled Location Identification and Efficient Validation with Encryption” (BELIEVE)—a blockchain-based platform with multi-party computation (MPC)—to securely validate data from a mobility source without violating the user’s privacy. The smart contractin BELIEVE first enables a source to initiate the mobility data validation process, then enables mobile peers in the vicinity to reach a consensus on the data validation using MPC without requiring the source to share raw data, thereby preserving the user’s privacy. The spatio-temporal validation is then updated on a distributed ledger (Inter Planetary File System) as an immutable transaction. Real-time data validation is challenging, considering constant mobility alongside the reduction of resource consumption at the devices and, therefore, to address this, we employ an adaptive approach to the problem where the validation frequency as well as storage on the blockchain are dynamically decided by the nodes, based on the network conditions. We evaluate BELIEVE using a simulation model developed for a portion of New York City, U.S., transportation network and it achieves lower delays and overhead.
An Edge Autonomous Lamp Control with Camera Feedback

Recently IoT edge devices have become more diverse and lower cost. In addition, small low-power single-board computers' computing performance has significantly increased. These conditions make it possible to process locally without communicating to the cloud. Since the advantages of in-edge processing are security and privacy, we applied in-edge IoT to smart homes with rich private information to be secured. In in-edge processing, conventional cloud-managed abnormality monitoring and system maintenance cannot be involved. We developed a lamp control system with in-edge processing. It detects failures using camera image processing and recovers from the failure. The abnormalities of the image processing are detected by monitoring cyclic outdoor brightness change observed on windows captured with the same camera. We have developed a prototype system with Python with OpenCV and FastAPI, etc., over PHP-based lamp timer control while keeping source code size small and considering validation easiness. The camera detectors work at 10 FPS on Python with as small as 1607 total source code lines (three times of code lines against the original lamp control timer).
Mixture regression modelling based on the shape mixtures of skew Laplace normal distribution

Modelling skewness and heavy-tailedness in heterogeneous data sets is a compelling problem, particularly in regression analysis. The main goal of this study is to propose a mixture regression model based on the shape mixtures of skew Laplace normal (SMSLN) distribution for modelling skewness and heavy-tailedness simultaneously. The SMSLN distribution has been introduced by Doğru and Arslan [Finite mixtures of skew Laplace normal distributions with random skewness. 11th International Statistics Congress (ISC2019); Bodrum/Turkey; Finite mixtures of skew Laplace normal distributions with random skewness. Comput Stat. 2021;36(1):423–447] as a flexible extension of the skew Laplace normal (SLN) distribution and includes an extra shape parameter that controls skewness and kurtosis. The maximum likelihood estimators for the parameters of interest with the help of the expectation-maximization (EM) algorithm are obtained. The performance of the proposed mixture model is demonstrated via a simulation study and a real data example.
Tangent-Based Path Planning for UAV in a 3-D Low Altitude Urban Environment

Unmanned aerial vehicles (UAVs) have emerged as promising platforms for fast, energy-efficient, and cost-effective package delivery. Path planning in 3-D urban environments is critical to drone delivery. The paper proposes a novel tangent-based (3D-TG) method for UAV path planning in 3-D urban environments. When a drone encounters an obstacle, a tangent graph is constructed to generate three sub-paths from both sides and above to bypass an obstacle, one of which is selected according to sophistically designed heuristic rules. The selected sub-path would be constantly adjusted its direction via tangent graph to avoid obstacles until the path can extend to the goal without obstacle collision. To avoid moving obstacles, velocity obstacle is incorporated in the 3D-TG. The experimental results on synthetic and realistic scenarios illustrate that 3D-TG performs well under static, unknown and dynamic environments. More significantly, 3D-TG can also generate a collision-free path for a drone to navigate through simple mazes efficiently, within a reasonable time.