Reviewerly/exHarmony huggingface dataset:
GCN-based Reviewer Recommendation in Github based on the Network Expansion

Github is an open-source platform which focuses on collaboration based on full-request. As an important task of the Github collaboration platform, a code review is critically important, as it plays a key role in the collaboration ecosystem as well as the software quality improvement. Therefore, there have been several works to recommend proper reviewers to improve the Github collaboration ecosystem. Despite the fact, however, previous studies have some common limitations. What is at the core in previous works is to focus on the recommendation of the existing network only, not considering the chances of extending the network in order to consider more proper candidates. For this purpose, this study suggests a GCN-based link prediction based on the network extension, which expands the boundaries of potential reviewer candidates in the network. As a result, the model based on the network extension shows a good performance compared to the existing network. In addition, we conducted a comparison for the existing reviewers and new reviewers, and identified the new and potential reviewers as having a positive impact compared to the existing reviewers.
Reducing the device complexity for 3D human pose estimation: A deep learning approach using monocular camera and IMUs

Camera and inertial measurement unit (IMU) based three-dimensional (3D) human pose estimation flourished in the last decade. However, existing methods are difficult to apply to clinical environments because of the many required devices. In this study, we attempt to reduce the devices and maintain their robustness. Occ-Corrector, a semantic convolution-based neural network, was proposed to estimate 3D human poses robustly in occlusion cases involving a single camera with the help of a few IMUs. It includes a simple Sensor-Reshape, which helps to fuse IMU information with the camera more effectively, and a new strategy of alternating the loss function, allowing the model to improve its accuracy in predicting challenging poses. By conducting an inverse analysis of the weight matrix, the importance of each IMU was investigated for device simplification purposes. The proposed method was verified by using the Total Capture dataset with two hypothetical occlusion conditions, and the result showed that only five IMUs can make the accuracy stable in different occlusion situations.
An Enhancement in Single-Image Dehazing Employing Contrastive Attention over Variational Auto-Encoder (CA-VAE) Method

Hazy images and videos have low contrast and poor visibility. Fog, ice fog, steam fog, smoke, volcanic ash, dust, and snow are all terrible conditions for capturing images and worsening color and contrast. Computer vision applications often fail due to image degradation. Hazy images and videos with skewed color contrasts and low visibility affect photometric analysis, object identification, and target tracking. Computer programs can classify and comprehend images using image haze reduction algorithms. Image dehazing now uses deep learning approaches. The observed negative correlation between depth and the difference between the hazy image’s maximum and lowest color channels inspired the suggested study. Using a contrasting attention mechanism spanning sub-pixels and blocks, we offer a unique attention method to create high-quality, haze-free pictures. The L*a*b* color model has been proposed as an effective color space for dehazing images. A variational auto-encoder-based dehazing network may also be utilized for training since it compresses and attempts to reconstruct input images. Estimating hundreds of image-impacting characteristics may be necessary. In a variational auto-encoder, fuzzy input images are directly given a Gaussian probability distribution, and the variational auto-encoder estimates the distribution parameters. A quantitative and qualitative study of the RESIDE dataset will show the suggested method's accuracy and resilience. RESIDE’s subsets of synthetic and real-world single-image dehazing examples are utilized for training and assessment. Enhance the structural similarity index measure (SSIM) and peak signal-to-noise ratio metrics (PSNR).
Encrypted chat application using RSA Algorithm

In some ways, the potency and effectiveness of the knowledge systems rely upon its design and the way the knowledge area unit is transmitted among totally different parties. Similarly, a crucial side of computer code development is the security of the knowledge that flows through open communication channels. One of the foremost widespread designs is User/server design which creates the centralization of knowledge storage and process modification, and supply flexibility for applying authentication strategies and coding algorithms inside info systems. whereas the number of users increases, it needs to increase the authentication and coding levels as high as possible. users/servers could be a technology that enables the opening of an associate degree interactive session between the user's browser and also the server. during this study, we tend to use users/server design to accomplish secure messaging/chat between users while not the server having the ability to decode the message. During this manner, a Server Cryptography- based mostly Secure electronic messaging System mistreatment RSA (Rivest- Shamir Adelman), it is a widely used public-key cryptography and authentication system for encryption of digital electronic messaging transactions like email over the computer network, extranet, and net, to write in code and decipher messages is developed using Java Web Application.
MLSFF: Multi-level structural features fusion for multi-modal knowledge graph completion

With the rise of multi-modal methods, multi-modal knowledge graphs have become a better choice for storing human knowledge. However, knowledge graphs often suffer from the problem of incompleteness due to the infinite and constantly updating nature of knowledge, and thus the task of knowledge graph completion has been proposed. Existing multi-modal knowledge graph completion methods mostly rely on either embedding-based representations or graph neural networks, and there is still room for improvement in terms of interpretability and the ability to handle multi-hop tasks. Therefore, we propose a new method for multi-modal knowledge graph completion. Our method aims to learn multi-level graph structural features to fully explore hidden relationships within the knowledge graph and to improve reasoning accuracy. Specifically, we first use a Transformer architecture to separately learn about data representations for both the image and text modalities. Then, with the help of multimodal gating units, we filter out irrelevant information and perform feature fusion to obtain a unified encoding of knowledge representations. Furthermore, we extract multi-level path features using a width-adjustable sliding window and learn about structural feature information in the knowledge graph using graph convolutional operations. Finally, we use a scoring function to evaluate the probability of the truthfulness of encoded triplets and to complete the prediction task. To demonstrate the effectiveness of the model, we conduct experiments on two publicly available datasets, FB15K-237-IMG and WN18-IMG, and achieve improvements of 1.8 and 0.7%, respectively, in the Hits@1 metric.
A SURVEY ON PERSPECTIVES ON THE GAP BETWEEN THE SOFTWARE INDUSTRY AND THE SOFTWARE ENGINEERING EDUCATION

In 1989, the gap between the software industry and software engineering education was first mentioned. Its existence has been regularly reported on since then, and solutions to close it have been proposed. However, after thirty years, all efforts to close the gap have failed. In this study, we argue that the gap between industry and academia exists for a variety of interconnected reasons. To take a broader look at the problem from the standpoint of all related entities, we provide a detailed overview of the profession and identify the entities, extract the causes that stem from these entities, and discuss what each entity should do, report and analyse the results of a questionnaire that was administered to students and recent graduates, highlight the highlights of interviews with students, recent graduates, and academics, We also contribute to finding solutions by considering all entities involved, which allows us to reach out to all of them and find out what they can do to acknowledge and close the gap. Our research concludes that the gap requires constant attention and hard work from all parties involved, and that everyone should be on the lookout for new technologies, learn to embrace changes, and adapt to them in order to keep the gap to a minimum.
Korean Scene Text Recognition Using Semi-Supervised Learning with Character-Level Consistency Regularization

Scene text recognition is a task that recognizes characters in scene images. Existing studies have been actively conducted based on English but little has been done on Korean. Because Korean does not have enough labeled data and has a large number of characters compared to English, it is more difficult to train Korean scene text recognition model. In addition, most of the previous studies trained the model using synthetic images rather than real images because of insufficient labeled data. However, using synthetic images can reduce generalization performance because of domain gap between real and synthetic images. In this study, we propose a Korean scene text recognition model using semi-supervised learning that overcomes the insufficient labeled data and domain gap. By using text alignment and consistency regularization specialized for Korean scene text recognition, we can obtain better performance than the existing supervised and semi-supervised scene text recognition models for three evaluation datasets. To the best of our knowledge, this is the first study that attempts semi-supervised learning for Korean scene text recognition.
Centrifugal and Aggregation Module Based on Shallow Features

Conventional image classification and detection tasks often rely on stacking multiple convolutional layers to increase the receptive field and obtain better features for the next step. Even with the addition of attention mechanisms, the more convolutional layers there are, the less positional information is contained in the resulting feature maps, leaving only semantic information in the end. Since the application of transformers in computer vision (e.g., Vision Transformer), many researchers have started to pay attention to the importance of positional information. Thus, we propose a centrifugal aggregation module, which aims to extract useful information from shallow features, transforming the image to the frequency domain through 2D Fourier transform, calculating its spectrum and phase maps, and then processing them through various methods before transforming back to the spatial domain. The experimental results show that adding our module to the shallow layers of most convolutional neural networks can improve the model’s performance.
MEMORIALIZATION THROUGH METAVERSE: NEW TECHNOLOGIES FOR HERITAGE EDUCATION

Abstract. The prolonged COVID-19 pandemic has restricted civic engagement with heritage sites and on-site learning. Although active participation is a prerequisite for managing cultural properties, most communities have had to cope with related activities – particularly memorialization at cemeteries – during the pandemic. This study explores the dynamism of innovative technologies that radically change pedagogical approaches through the United Nations (UN)-designated graveyard, the UN Memorial Cemetery in Korea (UNMCK). It focuses on the metaverse learning program of an international memorial ceremony named “Turn toward Busan,” which is honored on Remembrance Day, 11 November, to commemorate the fallen UN veterans who died in the Korean War (1950–1953). The online activity provides students with the opportunity to engage in a silent tribute to UN veterans and to participate in the virtual UNMCK implemented on the metaverse platform called “Gather. Town.” Qualitative analysis of a literature review, interviews with related stakeholders, and video participation reveals that this program of incorporating the remembrance of war veterans into the framework of the metaverse has a high educational impact in the era of Industry 4.0. The proposed solution demonstrates how virtual learning courses integrate multi-dimensional methods to encourage participants’ proactive heritage involvement and awareness of peacebuilding. Given that 2023 marks the 70th anniversary of the Korean War Armistice, this study is expected to provide a stepping stone for further research investigating the correlation between the humanities and digital technologies to foster a more reconciliatory world.
The use of technical aid resources in the history of education: sharing anecdotic and historical events

The history of education includes the educational aids and resources that the teachers have used to transmit the knowledge best through the years. The objective of this article is to review some of the devices used to aid education from an anecdotal and personal point of view. I used it in my academic life, many of this equipment. The initial teaching experiences in the history of the world were probably developed from one instructor to a unique person and, after a time, included more people until to reach the concept of the actual group of learners in a classroom.
Geometry of phase-covariant qubit channels

Abstract We analyze the geometry on the space of non-unital phase-covariant qubit maps. Using the corresponding Choi-Jamiołkowski states, we derive the Hilbert-Schmidt line and volume elements using the channel eigenvalues together with the parameter that characterizes non-unitality. We find the shapes and analytically compute the volumes of phase-covariant channels, in particular entanglement breaking and obtainable with time-local generators.
Learning latent functions for causal discovery

Abstract Causal discovery from observational data offers unique opportunities in many scientific disciplines: reconstructing causal drivers, testing causal hypotheses, and comparing and evaluating models for optimizing targeted interventions. Recent causal discovery methods focused on estimating the latent space of the data to get around a lack of causal sufficiency or additivity constraints. However, estimating the latent space significantly increases model complexity, compromising causal identifiability and making it hard to compare models that correspond to different causal hypotheses. We propose a kernel, non-parametric latent-space modelling approach and deal with the difficulty of comparing causal directions by measuring and controlling for the level of causal assumption fulfilment. We introduce a latent noise causal inference framework to estimate latent factors associated with the hypothesized causal direction by optimizing a loss function with kernel independence criteria. We extend the framework to work with time series using an additional time-dependent kernel regularizer. We discuss the additivity assumption and model complexity and give empirical evidence of performance in a wide range of synthetic and real causal discovery problems.
A Hybrid Method for Objective Quality Assessment of Binary Images

In the paper, a novel hybrid method for an automatic quality assessment of binary images is proposed that may be useful, e.g., for computationally limited embedded systems or Optical Character Recognition applications. Since the quality of binary images used as the input for further image analysis strongly influences the obtained results, a reliable evaluation of their quality is a crucial element for the validation of such systems. Assuming the availability of several video frames, an objective quality assessment of individual video frames may also be helpful for the choice of a binarized image leading to the “best” final results. Nevertheless, most of the image quality assessment methods concern the analysis of grayscale or color images, utilizing the available image datasets containing numerous images subject to various distortions together with the corresponding subjective quality scores. Therefore, a reliable quality assessment of binary images is troublesome due to a small number of datasets and methods dedicated to binary images. The approach presented in the paper is based on the combination of one of such metrics, known as Border Distance, with some other methods, utilizing the nonlinear model based on the weighted sum and product of individual metrics. The experimental verification of the proposed hybrid metric conducted for the publicly available Bilevel Image Similarity Ground Truth Archive dataset leads to a significantly higher correlation with subjective quality scores in comparison to the other methods.
An Experimental Comparison of Anomaly Detection Methods for Collaborative Robot Manipulators

A large number of methods for anomaly detection in robotic manipulation have been proposed, but their applicability and performance in real-world scenarios are often not established. In this paper, we therefore perform an experimental comparison of a broad range of practically applicable methods to detect exogenous anomalies in pick-and-place tasks.We first collect a dataset, which has been made freely available, on a state-of-the-art collaborative robot. The extensive experimental campaign comprises 600 runs under normal operation, and 80 runs where exogenous perturbations are present, and data is collected on the robot’s joints, force-torque readings, and the time spent in each program node. Using the data collected during normal operation, we train a set of anomaly detection methods whose computational complexity is low enough to run on resource-constrained robot hardware. We then evaluate the trained methods on the data collected in the perturbed runs and find that several methods can achieve a high anomaly detection performance. We show that exploiting knowledge of the robot’s program tree can increase the performance for some types of anomalies.We also observe that performance, in general, deteriorates when the application includes: (i) rarely-visited program branches, (ii) physical contact with the environment, and (iii) stochastic trajectories.
Research on key technologies for autonomous vehicles

Abstract With the advancement of science in the world, Artificial Intelligence is also developing rapidly. Currently, the traditional automobile industry is making significant efforts to develop autonomous driving technology based on the development of AI. The research focuses on key technologies for autonomous vehicles, systematically analyzing environmental perception, behavioral decision-making, path planning, and vehicle lateral or longitudinal motion control systems.
Pursuing Benefits or Avoiding Threats: Realizing Regional Multi-Target Electronic Reconnaissance With Deep Reinforcement Learning

Unmanned combat aerial vehicles (UCAVs) are preferred for regional electronic reconnaissance due to their versatility and stealth. This paper proposes a deep reinforcement learning (DRL) method to enable UCAVs to complete regional multi-target electronic reconnaissance (MER) tasks with continuous autonomous maneuvers. Distinguishing from traditional heuristic search algorithms, we first derive the objective function of MER and elucidate sufficient conditions to improve the success rate of reconnaissance recognition. Then, using the original cognitive electronic warfare framework, a three-dimensional MER simulator named Scouer-N is created to satisfy the requirements of dynamic environment training for DRL-based agents. To enable the processing of sequential situation awareness, a generative network is constructed by introducing a partially observable Markov decision process (POMDP) model, which assists the UCAV in filtering the observations from the sensor and predicting the actual states. Finally, we propose a priority-driven state reward shaping method that provides normalized state representation and dense rewards to the agent during training to improve the agent’s behavioral knowledge for MER. The experimental results demonstrate a considerable improvement in the task success rate of the trained UCAV relative to the benchmark, proving the efficacy of our approach in helping agents learn the optimal reconnaissance strategy from the potential state space.
Adoption of AI-Based Technologies in the Food Supplement Industry: An Italian Start-Up Case Study

Although many industries have already implemented technologies based on artificial intelligence (AI) in their business, the effects of new digital solutions on customer satisfaction are not yet fully known. This study aimed to evaluate the AI-based advice implemented by an Italian start-up operating in food supplements to support customer choices. The evaluation utilized the Delphi method and a questionnaire survey. This research aimed to provide companies wishing to use AI with a preliminary evaluation criterion for improving customer satisfaction through digital approaches. Research findings indicate that AI-based advice can improve customer perspectives, such as customer satisfaction and loyalty, by providing a value-added business service, diversified for each product category. However, some mistakes have emerged, which may still be a limitation in the use of AI-based advice. Therefore, this study presents an innovative approach to evaluate the performance of digital advice in traditional sectors such as the food industry.
In-depth Teacher-student Interaction Research in Polytechnic Smart Classroom based on the Perspective of “San Jiao” Reform

Classroom reform is an important step in the reform of “San Jiao”. The polytechnic classroom urgently needs to be deeply restructured and upgraded. Smart classroom enabling deep interaction embodies the teaching structure elements, the deep learning in the environment of smart classroom, and the deep integration of information technology.
Beyond the Screen: Exploring Primary English Teachers’ Attitudes Towards Digital Learning Management Systems

This study aims to investigate primary English teachers' experiences with integrating digital platforms into teaching in a primary school in Sidoarjo.The study employed a qualitative research design and a case study approach, and data was collected through interviews, video recordings, and artifacts gleaned from lesson plans.Thematic analysis was used to analyze the data, which revealed that most teachers have a positive opinion about using digital platforms in the classroom, but lack pedagogical practices for blending specific approaches with technology.The study also found that having access to technological facilities does not guarantee effective incorporation of technology into lessons.The results suggest the need for teacher training on digital storytelling and pedagogical practices for integrating technology in primary classrooms.
Research on Rural Comprehensive Energy Service Operation Based on Data-driven of Electric Power Internet of Things

In order to promote the revolution of energy production and consumption, and build a clean, low-carbon, safe and efficient energy system, the research on rural comprehensive energy service operation based on the data drive of electric power Internet of Things was put forward. In this paper, the innovation of big data technology application concept drives the integrated energy service operation management mode, and improves the service quality and operation management efficiency. Focusing on the collaborative optimization of production and consumption, the construction and management of rural comprehensive energy services will make use of technological advantages to accurately locate customers’ needs, improve service efficiency, and benefit the concept of energy green ecological construction in rural comprehensive energy development planning to build a smart energy ecosystem.