Reviewerly/exHarmony huggingface dataset:
Web 3.0 Resources in the Development of Autonomous Work of Higher Education Students in Times of Covid-19 Pandemic

This research on web 3.0 tools and autonomous work analyzes new and interactive possibilities for the generation of educational content in web environments. The research aims to determine the use of web 3.0 tools and the autonomous work of higher education students in times of pandemic. The research methodology was of an experimental type through a quantitative approach, with a documentary bibliographic modality for the understanding of the variables and field where direct contact was maintained with the study population. For the collection of information, the survey technique was used based on a questionnaire on a Likert scale. The study population was 68 students of the Tourism major, a population to which the experimentation was applied based on the ADDIE methodology for the development of digital tools and the application of the TAM model survey. The statistic used to test the hypothesis is Kolmogorov-Smirnov with a value less than 0.05. The results of this research were that the students favorably accepted the technology, that is, the web 3.0 tools in autonomous work since they contribute to generating self-learning skills, motivation, and commitment to the construction of knowledge in a playful way.
Prediction of Positive and Negative Sentiments for Twitter Data Using Machine Learning

Analysis of sentiment is one of the important field in Natural Language Processing (NLP) used to find the emotions using the text. It is an approach to identify the mentality, perspective or feelings of the individual towards anything, administration, film and so on by dissecting the feelings and surveys shared via online media. Different online media networks such as Facebook, Twitter, etc., permit individuals to share their perspectives with others. Twitter become the most well-known online media network that permits clients to share data via the short messages called tweets on a continuous premise. A huge number of individuals associate with one another simultaneously, and a tremendous measure of information is created in seconds. To utilize this information, we build up a Twitter visualization and sentiment analysis system. The dataset used for sentiment analysis is collected from the Twitter website. The pre-processing steps are applied for cleaning the collected dataset by removing hashtags like watchwords. Application Programming Interfaces (APIs) are used to perform sentiment analysis after cleaning the data. This will dissect the estimations as positive and negative for a specific item and administration that helps associations, ideological groups and average folks to comprehend the viability of their endeavours and better dynamic. Our results show that it can handle information progressively, and get envision data consistently.
Joy to the Work: How Does Software Team Formation Strategy Influence Job Satisfaction?

Background - The main assets of software organizations are their development teams. Several factors impact job satisfaction, such as salary, workload, and autonomy; still, the software team relationship could impact one's job satisfaction. Objective - We aim to understand how software team formation strategy impacts job satisfaction. Method - We analyzed two software team formation strategies: (i) team members selected by a leader, and (ii) team members self-selection. We administered a survey to 66 employees of an R&D organization, comparing the team formation strategies. Results - Our results indicated that, in this context, the self-selection team formation strategy positively impacted job satisfaction and influenced good team cohesion and performance. Qualitative results showed that this formation strategy assembled teams with a good relationship among their members in which conflicts arose but were harmonically solved. Conclusion - This suggests that autonomy in the team formation stage would be good practice and could be more tested in the industry. These results may bring new evidence to practitioners about the benefits and drawbacks that self-selected teams may offer to the organization.
High-Performance Architecture Aware Sparse Convolutional Neural Networks for GPUs

Convolutional Neural Networks (CNN) are used to analyze data with spatial/temporal structure. In recent years, CNN's popularity has increased exponentially by virtue of its accuracy and applicability. Due to its massive deployment scale, especially in the automotive industry, image analytics, and portable devices, even fractional improvement in performance and power consumption can lead to enormous savings. In this work, we focus on exploiting the sparsity of feature maps and reducing the required number of computations and data movement, leading to improved performance. Compared to kernel sparsity, where the sparsity structure is known apriori, the feature map sparsity is only known during runtime, making this a challenging optimization problem, especially for GPUs. In this paper, we develop a GPU-friendly Sparse CNN framework capable of handling feature map sparsity. The efficacy of our approach is demonstrated by comparing the performance of our implementation with the state-of-the-art implementations. Our approach can also be extended to support upcoming techniques such as feature map pruning and submanifold sparse convolutional Networks.
Multi-exposure image fusion of dynamic scenes using consistency detection and weighted least squares filtering

The high dynamic range image obtained by multi-exposure image fusion (MEF) contains more detailed information, which has broad application prospects and crucial practical significance in many fields. However, due to the introduction of the ghosting artifacts caused by object movement and camera shake, MEF in the dynamic scenes has always been a challenge. To address this problem, we designed a deghosting method for MEF. The over- and under-exposed images are corrected by intensity mapping and consistency detection to obtain the aligned latent images. Then the high- and low-frequency components of the latent images are generated using weighted least squares filtering. The blending weights are calculated based on the image luminance and the exposedness function. These frequency components are integrated into the final deghosted image with more texture details and vivid color. A comprehensive evaluation experiment is carried out, proving that the proposed method has a better visual effect and stable performance than the state-of-the-art deghosting MEF methods.
Analyzing Technical Debt by Mapping Production Logs with Source Code

Poor coding practices, bad design decisions, and expedited software delivery can introduce technical debt. As software grows, manual detection and management of technical debt become increasingly difficult. To address these problems, recent research offers a variety of approaches for automating the process of recognizing and managing technical debt. Unfortunately, current strategies for measuring technical debt depend on static standards that fail to acknowledge software usage patterns in production. In this paper, we utilized existing tools to identify technical debt using static code analysis and then employed production log analysis to rank these debts dynamically.
Correction to: On How to Not Prove Faulty Controllers Safe in Differential Dynamic Logic

The original version of this paper contains errors in many tables and equations mistakes and has been submitted by mistake. This has been corrected.
Code Summarization Through Learning Linearized AST Paths with Transformer

The lack of code comments is common in software projects. This work proposes TFSum, which generates from a function’s source code a readable summary to describe its functionality, with a Transformer based model trained on sequences linearized from the function’s abstract syntax tree (AST). To ensure the quality of the generated summaries, TFSum firstly parses from the function’s source code an AST of semantic richness, as the raw representation of the function; but linearizes and pre-processes it into a set of normalized token sequences, for efficient and effective following semantic representation learning. On this basis, an encoder-decoder based generative model that adopts the Transformer architecture is designed and trained to automatically generate code comments. The experimental evaluations conducted on a public dataset show the superiority of TFSum over state-of-the-art neural code summarization methods, with the BLEU score reaching 46.84.
An Investigation into Digital Literacy and Autonomous Learning of High School Students

The rapid development of digital technologies and the internet has redefined access to language resources and approaches to language learning. High school students are experiencing more digitalized learning activities, which influences their independent learning mode. In Autonomous Learning and Digital Literacy, rare research has been done to examine the relationship between the two. Drawn from responses of 224 high school students to the questionnaires, this study investigated their digital literacy level from five dimensions, such as knowledge acquisition, and their autonomous learning situation from two perspectives. The results showed that the participants have a certain degree of digital literacy, have a high desire to learn, and their self-management capacity is at a medium level. Yet there is no correlation between students&amp;rsquo; levels of digital literacy and their levels of autonomy as learners. The findings may prompt educators to prioritize encouraging digital literacy and student agency in the classroom.
An Empirical Evaluation of Predicted Outcomes as Explanations in Human-AI Decision-Making

In this work, we empirically examine human-AI decision-making in the presence of explanations based on predicted outcomes. This type of explanation provides a human decision-maker with expected consequences for each decision alternative at inference time—where the predicted outcomes are typically measured in a problem-specific unit (e.g., profit in U.S. dollars). We conducted a pilot study in the context of peer-to-peer lending to assess the effects of providing predicted outcomes as explanations to lay study participants. Our preliminary findings suggest that people’s reliance on AI recommendations increases compared to cases where no explanation or feature-based explanations are provided, especially when the AI recommendations are incorrect. This results in a hampered ability to distinguish correct from incorrect AI recommendations, which can ultimately affect decision quality in a negative way.
Innovation Performance Prediction of University Student Teams Based on Bayesian Networks

Many studies have been conducted on the impact of dualistic learning, knowledge sharing, member heterogeneity, and their influencing factors on team performance in enterprises. However, research on the substantial differences between university student teams and enterprise teams is scarce. To address this void, this empirical study explores how the mechanism of dualistic learning affects university student teams’ learning performance facing rapid changes in higher education. Using the questionnaire, two modules of dualistic learning were identified through reliability and validity tests, and the research data set was formed. After preprocessing the data set, two team innovation performance prediction models were established based on the Bayesian network (BN). According to the characteristics of BN, the probability reasoning of the model was calculated and the posterior probability table was obtained under different dualistic learning levels. The results show that dualistic learning has significant impacts on innovation performance, and the improvement of dualistic learning can stimulate team innovation performance. This research can provide important theoretical guidance for teams to improve their ability, gain competitive advantages, and stimulate the creative enthusiasm of college students. Hopefully, this research will enrich the existing theoretical connotation to a certain extent and promote the development of relevant empirical research.
Crisis Effect: Methodological Proposal for the Analysis of E-assessment Model in Teaching and Learning in New Normal Phase

Digital education needs to rise together with the emergence of the fourth education revolution in this era. Consequently, higher education institutions are responsible in dealing with the technological advancements to prepare future graduates for work. However, the recent crisis has given another new challenge to the digital education. The challenge is not limited to the tool, device, or technological and application revolutions, but also in using the virtual platform for the assessment of teaching and learning, or known as an e-assessment. Therefore, it is important to address the methodology to analyse the factors of e-assessment model at it is still best to be carried out in the new model while it is valid for most prior models with crisis robustness adaptation in respect with the new normal phase. Therefore, the creation of the new model’s focal criteria includes its aim, methodology, and conceptual pedagogical components.
The Effectifeness of Joesin Translation Channel on Students' Vocabulary Mastery

This research presented The Effectiveness of Joesin Translation YouTube Channel on Students' Vocabulary Mastery at SMP Negeri 6 Central Buton (An Experimental Study at the VII Grade Students Outcomes at SMP Negeri 6 Central Buton). This research question is: Are there any effects of YouTube on students' vocabulary mastery at the VII grade students' outcomes of SMP Negeri 6 Central Buton? This study aims to determine YouTube on students' vocabulary mastery among the VII grade students of SMP Negeri 6 Central Buton. The design of this study was a pre-experiment using the One Group Pre-test-Post-test. This research variable consists of two variables: the effect of the Joesin Translation Channel as a free variable and the students' vocabulary mastery as a bound variable. The sample was 26 students. This research instrument was vocabulary tests (pretest and posttest). The data collection technique in this study was to provide pretests, treatments, and posttests. Data on students' English learning outcomes were obtained from the results of the Pretest and Posttest in the form of multiple choice. The data showed an average increase in students' math learning outcomes before applying (39.99%) and (73.71%) after applying to Joesin Translation Channel on YouTube media. Furthermore, the results of testing based on hypothesis testing were carried out statistically one-sample t-test rejected and accepted. With its acceptance, It can be concluded that Joesin Translation Channel is effective in Students' Vocabulary Mastery in improving students' English learning outcomes.
Angular Error Reduction of a Machine Vision System using a Trapezoidal Trajectory Velocity Profile

Nowadays laser scanners play an important role in technology and research, when it comes to measure 3D coordinates physically and in real time of any object under observation. Laser scanners have to measure the 3D coordinates of these objects in shortest time and with the smallest measuring error. A novel laser scanner called Technical Vision System (TVS), which uses Dynamic Triangulation and DC motors, was developed and proven to represent a fast and reliable scanning system. Previous research used the step response of the TVS mechanical actuators (DC motors), which can overshoot if certain controller parameters of the closed-loop control are selected. If the overshoot is undesirable, a trade-off must be made between speed and overshoot of the step response. Thereby, present paper describes a new approach to control the actual angular position of the TVS DC motors using a trapezoidal profile for the DC Motor velocity. The DC motors step response is replaced by a trajectory response, which allows the reduction of the relative angular error of the DC motors shaft to a value less than or equal to 0.1 percent. Also, the design of the control system, to implement the trapezoidal trajectory profile for the DC motor velocity in practice, is described in detail.
A Comparative Analysis of End-To-End Trainable Motion Compensation based Video Super-Resolution Techniques

Video Super-Resolution (VSR) is gaining massive attention in the digital age across various application domains such as video surveillance and ultra-high-definition displays. Deep learning (DL) has recently made significant progress in research and industry domains. Existing researchers used numerous VSR methods based on deep learning to improve the resolution. VSR is based on inter-frame information utilization. Video frames provide the leverage information of data that modalities record sequentially, i.e.; the object's temporal changes cause inconsistent measurements. Consequently, suitable models and algorithms must be developed to provide artifact-free images. This paper goes through two reconstruction methods incorporating various motion data sources. Initially, it describes an iterative approach that implicitly treats dynamic behavior as uncertainty in the forward model. Further direct reconstruction techniques can be derived for an explicit motion map. This paper compares both explicit and implicit motion compensation-based methods. Convolutional neural networks (CNN) and recurrent, residual network (RRN) based methods from a deep learning environment are analyzed to reconstruct the High-Resolution (HR) frames. This paper shows numerical findings for both methods using simulated data. Observations show that RRN can boost the super-resolution (SR) performance and achieve considerable visual quality after reconstruction; however, performance is limited to small datasets due to shallow network parameters. Extensive experiments show that RRN significantly impacts SR results due to its substantial computational efficiency.
Lightweight Neural Networks for Pedestrian Detection in Intelligent Vehicles

Most actual intelligent vehicles (IV) are powered by a variety of sensors and cameras. Vision-based applications for IV mainly require visual information. In this paper, the authors introduce a pedestrian detection application used for pedestrian safety. The authors proposed a deep fully convolutional neural network (DFCNN) for pedestrian detection. The proposed model is suitable for mobile implementation. To do this, the authors propose to build lightweight blocks using convolution layers, and replace pooling layers and fully connected layers with convolution layers. Training and testing of the proposed DFCNN model for pedestrian detection were performed using the Caltech dataset. The proposed DFCNN has achieved 85% of average precision and an inference speed of 30 FPS. The reported results have demonstrated the robustness of the proposed DFCNN for pedestrian detection. The achieved performance was low computation complexity and high performance.
Nighttime Object Detection

These days, it's becoming harder to feel safer when we go out at night. So, to tackle this security problem, the authors propose a night patrolling mechanism to detect objects in low light conditions. Images taken during the nighttime have difficulties with less contrast, brightness, and noise owing to inadequate light or insufficient exposure. Deep learning-based methods accomplish end-to-end, unsupervised object recognition using convolutional neural networks, which abolishes the requirement to describe and draw out attributes separately. Despite the fact that deep learning has led to the invention of many successful object detection algorithms; many state-of-the-art object detectors, like Faster-RCNN and others, can't carry out at their best under low-light situations. Even with an extra light source, it is hard to detect the features of an item due to the uneven division of brightness. This chapter proposes a deep learning algorithm called single shot detector, with Mobilenet v2 as the backbone to tackle the issues of object detection under low-light situations.
A Comparison of Bidirectional GRU and LSTM for Hand Gesture Recognition Using Leap Motion

Hand Gestures are the most natural and reliable way of communication with computers. This paper proposes a recognition approach for human hand patterns utilizing a touchless sensor, Leap Motion Controller (LMC). Hand gestures of the proposed work are the set of feature vectors that keep changing with time, referred to as dynamic hand gestures. Firstly, to enhance the recognition rate of dynamic hand gestures, we are presenting two variants of Recurrent Neural Network (RNN) known as Bidirectional Gated Recurrent Unit (Bi-GRU) and Bidirectional Long Short Term Memory (Bi-LSTM). Secondly, we compare these two models on the ASL dataset. Results show that the average accuracy score obtained using Bi-GRU and Bi-LSTM is 96.97% and 97.28%, respectively. The accuracy of both proposed models outperforms the competing work on the ASL dataset. We find that LSTM is superior in terms of accuracy, whereas GRU is a simple model that takes less time to train.
Painted PIXOR: A Real-Time Multi-Sensor Fusion Object Detection Pipeline for Autonomous Vehicles

Multiple sensors such as cameras and LiDARs play an essential role in perceiving the surrounding environment for autonomous vehicles (AVs) by providing complementary information. Fusing multi-sensor information and detecting traffic objects in an accurate and real-time manner remains a challenge. In this work, we propose a new fusion pipeline: Painted PIXOR, based on the improved Point Painting and PIXOR detector. We first improve the semantic correctness of images by fusing multi-view images in Point Painting through an efficient PCC fusion method. We then project the LiDAR points into the fused image semantic map and append traffic object class scores to the corresponding original point. The appended point is used as the feature encoder input to extract the suitable input for the PIXOR detector. Experiments tested on KITTI detection show that our proposed method performs better than the original PIXOR and other methods in terms of average precision (AP).
Implementation of Business Modeling Canvas (BMC) and Unified Modelling Language (UML) for Android-based Application

The development of digital in indonesia is shown from the increasing demand of computers use, and mobile Application. The development technology has an impact on changes in many human habits , especially in human's lifestyles.In this era People’s can get everything efficiently and effectively except in needs of foods. People can use VeggieFun for fulfillment fruits and vegetables needs. VeggieFun provides convenience people’s to order and buy fruits dan vegetables without go to market. As an application for comercial used, VeegieFun use bussines model and UML methode for development and implementation VeegieFun aplication. With a Bussines With Bussines Model Canvas (BMC) we can more get analiysis from bussines model and using UML In development of veggiefun applicationt for simplify the stages of implementation and development an application . Keyword : Busines Modeling Canvas, Unified Modelling Language , Application