Reviewerly/exHarmony huggingface dataset:
Identifying refactoring opportunities for large packages by analyzing maintainability characteristics in Java OSS

The source code of a Java-based software system is often structured into packages. When packages are large, they often carry maintainability quality issues. In the literature, there is a lack of empirical evidence on the specific maintainability issues that occur when packages become too large. Our study fills this gap by performing relationship analysis of package size with respect to internal maintainability characteristics (coupling, cohesion, and complexity) using package-level metrics collected from 111 open-source Java projects provided in Qualitas Corpus. Our results show significantly higher maintainability issues in large packages as indicated by the maintainability metrics. We also report strong relationships of package size with cohesion (represented by the number of connected components in a package) and complexity (measured by the number of internal relationships in a package). Based on these strong associations with package size, we show that these cohesion and complexity metrics can be used to identify large package refactoring opportunities. Furthermore, we also discuss why some maintainability metrics (e.g., coupling metrics) may not be useful for refactoring large packages. Editor's note: Open Science material was validated by the Journal of Systems and Software Open Science Board.
Depth error correction for plenoptic cameras based on an innovative AR system combining geometrical waveguide and micro-lens array

Plenoptic cameras have been increasingly applied in three-dimensional (3D) optical imaging and measurements. Micro-lens array is the key component to capture the images at different depths simultaneously to realize the 3D reconstruction of the scenery. However, only a part of the captured information will be used eventually when focusing on specific objects, which implies a waste of time and resources that are incompetent for fast-response tasks like gesture recognition. Furthermore, it is still quite challenging to accurately calibrate the depths under complex circumstances, even sometimes losing a part of the target information due to the low signal-to-noise ratio. In this paper, a new hybrid optical system is developed combining a geometrical waveguide and micro-lens array to project a virtual scale network for quantitative depth calibration and fast-tracking of targets. Such a methodology based on the augmented reality (AR) mechanism helps to rapidly scope the targeted objects/features without reconstructing the full range model, significantly saving the processing time for a low-latency response. By establishing a geometrical model to quantitatively correlate the images in the auxiliary coordinate system and the virtual scale coordinate system, the depth error caused by the plenoptic camera can be calibrated and corrected. The coefficient of determination R2is used to evaluate the depth accuracy of 3D images and acts as the threshold to control the depth correction iterations. The closer the value of R2 is to 1, the more accurate the depth information is. Experiments proved that even under complex backgrounds or insufficient light, in virtue of the virtual coordinate networks, backgrounds could be rapidly filtered, and targets were effectively identified with accurate depth information. The algorithm flow chart for the correction of depth error is given. In such a way, the system can achieve faster and more accurate capturing of 3D objects in a real-time manner than conventional plenoptic cameras.
Virtual Tour with Voice Assistant using Extended Reality

We are all aware that virtual reality is a new emerging technology in the modern era. Virtual reality has enormous potential across many industries like Education, Hospitality, Gaming, Army, Tourism, etc. It is the medium of creating our own world, our own customized reality. In this paper, we discussed the extended reality concepts to make the virtual tour of the college campus with real voice assistant service that based on IoT concept. It is the key to experiencing, feeling and touching the college premises, using VR Technology. With this project any of guests want to visit the college premises, He/She can do virtually with our assistant service. In hectic schedules, colleges are facing difficulties during visiting the college to the parents because all faculty members have their other work to do. With this problem, the virtual tour is a solution. So we make the virtual tour with unique voice assistant facility on the concept of IoT technology which will provide the information regarding the place where the visitor will enter for visit by the help of VR Headset.
Portable Hotel Management System Using Virtual Reality

In todays era, people often go to restaurants. So, good hotel management is necessary to increase the number of customers. To enhance it, a VR model is being developed. The main use of this VR model is to reduce the customer’s interaction with the servers and also increase customer satisfaction. VR not only increases satisfaction but also enhances user-friendliness. The existing system has a pen-based technology that is used to select the menu. But, in the updated system a touch screen system has been enabled to increase the accuracy. Customers can order or place the menu through a VR device preinstalled in their tables to reduce the manpower.
BidChain: A Blockchain-Based Decentralized Application for Transparent and Secure Competitive Tendering in Public Construction Projects

Deficiencies of tendering systems in public projects can leave governments with subsequent issues in projects and waste taxpayers’ money. Electronic tendering (e-tendering) systems have been found beneficial for awarding public projects; however, current practices still have deficiencies. Legal and security issues and the lack of transparency have been identified as the main shortcomings in current tendering practices. This paper argues that the underlying issue with e-tendering systems is due to their centralized nature. Therefore, this research presents a critical assessment of the blockchain technology in developing distributed e-tendering systems and proposes a novel framework for decentralizing e-tendering systems. The proposed framework is created by integrating three technologies: blockchain, public key infrastructure (PKI), and interplanetary file system (IPFS). Using the proposed framework, a decentralized application (DApp) is developed to demonstrate the concept and evaluate the potentials of blockchain in mitigating the security and legal challenges and improving transparency and trust. The result shows that the reliance on centralized authorities for managing the tendering process is reduced in a blockchain-based e-tendering system, and a transparent and tamper-proof record of the tendering history is distributed among parties.
Interactive attention and improved GCN for continuous sign language recognition

RGB sign videos are easily influenced by light, perspective, and clothing, and skeleton data might be an effective complement. The challenge of multi-modal sign language recognition lies in the fusion of different modalities and the feature extraction of each modality. Therefore, we propose a continuous sign language recognition method based on interactive attention and improved graph convolutional networks (GCN). For the skeleton stream, a hand shift decoupling GCN is proposed to improve the graph modeling capabilities by capturing the interaction between the two hand nodes through the shift of hand features and decoupling operation. Then a cascaded attention module progressively in space, time, and channel dimensions effectively extracts high-level semantic features. To make the best of the two streams’ features, an interactive attention mechanism is designed which takes the semantic features of the skeletons as queries and RGB features as keys and values, enhancing the spatial–temporal correlation mining ability and recognition performance of the network. The proposed method achieves competitive results on two public sign language datasets CSL and RWTH-PHOENIX-Weather-2014.
A one-dimension convolutional neural network based interference classification method

Interference is a common problem in wireless communication, navigation and radar systems. A wide variety of interferences are used to degrade the communication quality especially in electronic warfare environment. In modern military communication systems, interference classification is an important module for its ability to obtain prior interference information before adopting related anti-interference method. This paper proposes a deep learning based interference classification method, which applies one-dimension convolutional neural networks to automatically extract interference features for classification. Computer simulations show better classification performance and lower computational complexity. Meanwhile, this proposed method is implied on software defined radios (SDR) hardware, more than 99% correct classification probability can be achieved with limited samples of the received signal, which verifies the robustness of this proposed method.
Impact of positive and negative emotions on protest for institutional reform: An analysis of #EndSars Twitter posts

A protest is a collective action undertaken by a group of people to achieve a common goal. Participation in protests is motivated by appraisals of a social condition. The dual pathway model of collective action has been extended to consider both negative and positive emotional paths to protest. This study further shows the prevalence of positive and negative emotions as they differ systematically between the different phases of protest. This study divides the protest period into four phases: before the trigger event, during the trigger event, during the protest, and reform announcement. A sentimental analysis of how positive and negative emotions vary across the four phases of protest was conducted using the Twitter posts of the #EndSARS protest in Nigeria. Findings revealed that both positive and negative emotions co-occur alongside the protest phases. Trust was dominant before the trigger event, fear was dominant during the trigger event, anger was dominant during the protest and joy was dominant following the reform announcement. Findings from this study indicate that fear and anger instigate protest. Anticipation motivates protest and joy is the feeling of satisfaction experienced when the goal of a protest is achieved.
Object Detection in UAV Images Based on Improved YOLOv5

Aiming at the problems of small targets, many instances, and complex backgrounds in UAV aerial images, in this paper, an improved YOLOv5-based algorithm for detecting objects in UAV images is presented. This paper enhances the robustness of an algorithm for recognizing aerial images by incorporating a spatial pyramid pooling network with a probability pooling method, introducing an upsampling network structure based on deconvolution and convolution attention mechanism. As a result, the issue of invalid features negatively impacting recognition accuracy is resolved, and a higher accuracy in recognizing aerial images is achieved. By conducting experiments on the VisDrone public dataset, it was found that the enhanced algorithm achieved an average accuracy of 34.9%, which is 3.21% higher than the average accuracy achieved by the original, unimproved algorithm.
Navigating the Current “New World” of Teaching with Technology: A Glimpse into Our Teachers’ Minds

The COVID-19 pandemic helped spark a surge in innovative usages of technology in education, from robot-based remote graduation ceremonies to immersive learning through extended reality, meetings in fantastical game worlds, automatic examination methods, and flexible learning options such as hybrid classes. It’s been said that we can’t go back to “normal” because this is normal now–but what exactly is today’s “new normal”? The current paper reports on the results of an anonymous online survey conducted with 42 teachers in business, IT, nursing, and education at our university in October 2021, to gain insight into where some teachers on the “front lines” currently stand on the use of technology in education. Some insights included that: More teachers than we had expected were using robotics and extended reality (XR), suggesting that silo effects can exist in education, even at small universities; furthermore, the rates of teachers who had seen such usage seemed close to the rates of teachers who had tried using them, suggesting the usefulness of raising awareness to promote professional digital competence (PDC). Rates for using games and exam tools were lower than expected, despite the availability of game platforms and a growing need to consider the threat of how technology can be misused to cheat in exams, possibly due to teachers’ limited time for pedagogical development. Also, teachers appeared to have strong and differing opinions about learning formats, although a general preference was observed for physical classes and exams, and hybrid teacher meetings. Our aim is that these results will be used by our university’s pedagogical center to support our teachers’ PDC and uses of edtech in the near future.
Augmented Reality and Artificial Intelligence in Education: Toward Immersive Intelligent Tutoring Systems

The integration of Artificial Intelligence (AI) and Augmented Reality (AR) in education as a means to address the new educational needs and to provide high quality education is gaining ground. AR provides engaging, interactive and motivating mixed reality environments while AI offers personalized experiences. The aim of this chapter is to explore how the convergence of AI and AR can impact the educational domain and lead to the development of more interactive and immersive Intelligent Tutoring Systems (ITS). This chapter presents the technologies of AI, AR and ITS and examines their use in educational settings. Then, it showcases the findings of recent related studies, discusses the potentials and merits that their combination engenders in educational contexts and provides future research directions. Based on the findings, it can be said that the convergence of AI and AR can result in the development of interactive, immersive, engaging and personalized learning experiences. These experiences can be applied in all educational levels and have the potential to take into account learners’ psychological, cognitive and motivational states as well as their unique characteristics, knowledge, preferences, interests and performance. These experiences can help to meet the educational demand, positively impact teaching and learning activities and increase learning outcomes and motivation.
Evolutionary Stitching of Plot Units with Character Threads

Ideation of novel narrative plots is often achieved by recombination of elements from prior plots. This approach presents two challenges: what elements to select from prior narratives, and how to find a new combination that makes sense without replicating any of the originals. The present paper relies on prior results for representing elementary narrative units for plot representation that both respect connectivity across a plot and allow a high degree of abstraction from previous instances of plot. An evolutionary procedure is applied to search the space of possible combinations, driven by metrics on coherent connectivity across plot units.
Correcting OCR Recognition of the Historical Sources Texts Using Fuzzy Sets (on the Example of an Early 20th Century Newspaper)

Our article is presenting an attempt to apply NLP methods to optimize the process of text recognition (in case of historical sources). Any researcher who decides to use scanned text recognition tools will face a number of limitations of the pipeline (sequence of recognition operations) accuracy. Even the most qualitatively trained models can give a significant error due to the unsatisfactory state of the source that has come down to us: cuts, bends, blots, erased letters - all these interfere with high-quality recognition. Our assumption is to use a predetermined set of words marking the presence of a study topic with Fuzzy sets module from the SpaCy to restore words that were recognized with mistakes. To check the quality of the text recovery procedure on a sample of 50 issues of the newspaper, we calculated estimates of the number of words that would not be included in the semantic analysis due to incorrect recognition. All metrics were also calculated using fuzzy set patterns. It turned out that approximately 119.6 words (mean for 50 issues) contain misprints associated with incorrect recognition. Using fuzzy set algorithms, we managed to restore these words and include them in semantic analysis.
Two-Stage Model for Copy-Move Forgery Detection

Digital images can be tempered or modified very easily with many tools available on the internet. Nowadays some software not only enhance and retouch but also copy-move objects of the image in such a way that is hard to differentiate. Image forgery detection deals with the recognition of tempering like splicing, copy-move, and other modifications done on the image. One of the most common ways of passive forgery detection is the block-based copy-move technique. In this paper, forgery detection is achieved in two parts. In the first part, Copy-Move forgery is classified by support vector machine (SVM) and features are extracted by local binary pattern (LBP) and discrete cosine transform (DCT). However, the region of forgery is calculated in the second part with the help of the Gray-level Co-occurrence Matrix (GLCM) through overlapping blocks. The whole experiment and evaluations are performed on the CoMoFoD image data set. The performance is measured in terms of accuracy and sensitivity. The promising result indicates the effectiveness of the model with enhanced accuracy of 97.82%.
Harvesting Metadata for XR Digital Learning Objects

Abstract The current work is a proposal for Moodle administrators who aim to provide content creators and teachers with capabilities to describe in a semi-automatic way their learning resources with LOM-based metadata and make these metadata available to search service providers so that other stakeholders can easily find and retrieve them. It was composed within ARETE project to support reusability and discoverability of 3D/AR and other types of educational resources included in the project’s Moodle digital repository. Aiming on utilizing previous work on this domain, the code of two existing plugins was modified and enriched to serve the project’s needs. This paper aims to demonstrate in detail two plugins that will be utilized in ARETE’s Moodle digital repository to support the discoverability of learning resources by creating and exposing their metadata to make them available for harvesting. The content in the ARETE repository is particularly relevant to 3D/AR learning activities created through an XR authoring toolkit. Nevertheless, educational content in other formats continues to be supported by the aforementioned plugins. The integration of IEEE-LOM and OAI-PMH standards to a Moodle repository seems to be a feasible way to enhance the development of learning content by utilizing relevant already existing resources that can be easily found and retrieved. However, the difficulty of finding service providers that could support the collection of learning resource metadata and be willing to build search engines on top of them suggests the need to consider different approaches.
Graphical decomposition model to estimate hourly global solar radiation considering weather stochasticity

Hourly solar radiation data are of great concern for solar PV, solar thermal and building energy assessment. Unfortunately, due to economic as well as technical limitations, most studies of solar radiation as well as actual measurements are daily values. Hence, daily solar radiation decomposition model is quite significant. Furthermore, graphical representation is a simple and direct way to analyze and interpret meteorological data. Consequently, a graphical solar radiation decomposition model based on the solar radiation daily pattern is proposed. It is achieved by clustering, regression, classification and identification of meteorological data (including solar radiation). The developed model is nested into the prediction system and allows reliable prediction of hourly global solar radiation. The proposed model improves 8.3% in accuracy compared to the commonly used model with an R2 of 0.91. Meanwhile, in existing studies, solar radiation daily patterns are mostly discriminated by clearness index. This is an average state but cannot describe the temporal variation of solar radiation. In our work, it is found that weather, cloud cover, and sunshine duration have a good temporal consistency on the solar radiation daily patterns. The biased distribution of solar radiation can be explained by the time series of these characterizing factors. Based on these new advances, sixteen different types of solar radiation have been identified, and the frequency of occurrence of each type has been established and presented.
A dual attention-guided 3D convolution network for automatic segmentation of prostate and tumor

In middle-aged and older men, prostate cancer (PCa) is a common tumor disease with a mortality rate second only to lung cancer. The automatic and accurate segmentation of the prostate and tumor in magnetic resonance imaging (MRI) images can help doctors diagnose malignancies more efficiently. T2 weighted imaging (T2W) is now used in the majority of studies on prostate MRI image segmentation; however, diffusion-weighted imaging (DWI) is more valuable in the diagnosis of PCa. The morphological differences between the prostate and tumor regions are minimal, the tumor size is uncertain, the border between the tumor and surrounding tissue is hazy, and the categories separating normal regions from tumors are uneven. Consequently, it is challenging to segment prostate and tumor on DWI images. For the segmentation of prostate and tumor regions on DWI images, this study offers a dual attention-guided 3D convolutional neural network (3D DAG-Net). A visual attention method is built into the encoder step to obtain the features of various receptive fields and deliver more detailed contextual information. A multiscale attention technique is proposed at the decoder stage to fuse multiscale features to acquire finer global and local details. To resolve the class discrepancies between the prostate, tumor, and background regions in segmentation tasks, we propose a hybrid loss function for handling class imbalance. We tested the algorithm on DWI images of PCa obtained from a nearby hospital, demonstrating the uniqueness and effectiveness of the method. Dice similarity coefficient (DSC) values for prostate and tumor DWI segmentation were 92.28% and 88.73%, respectively. We present a unique dual-attention mechanism 3D segmentation network architecture for quantitative assessment of prostate and tumor volumes on DWI. The automatic segmentation results produced by our technology were highly correlated and consistent with expert manual segmentation findings.