Reviewerly/exHarmony huggingface dataset:
Channel and Spatial Transformer for Underwater Image Enhancement

Underwater images always suffered from significant degradation, such as color deviation, low contrast and blurred details. Underwater image enhancement is an essential step to improve the image quality. However, the existing underwater image enhancement results always have the problem of colour bias, and do not consider the non-uniform attenuation in different color channels. In this paper, a channel and spatial (CS) transformer module is proposed for the underwater image enhancement, where multi-dimensional attention is extracted from the deep features. The channel-level multiscale feature fusion (CMFT) module focuses on the channel with severe color attenuation. The spatial-level attention module(SAT) focuses on the degraded regions in spatial domain. Furthermore, a new loss function that combines perceptual losses in RGB, LAB and LCH colour spaces is proposed to correct color distoration and improve image details. A large number of experiments on existing datasets have verified the excellent performance of the proposed networks.
Visualization of physical fields with augmented reality in teaching

Understanding physical effects occurring, for example, in the electromagnetic field, can be challenging. To ease the learning experience, it is beneficial to visualize and encourage interaction with the physical field. Augmented Reality (AR) can serve as a tool to visualize naturally invisible fields to help students understand physical effects. In this paper, we present a workflow to incorporate field results stemming from a FEM tool or simple analytical solutions into an augmented reality (AR) experience. We focus on providing a simple framework for educators to integrate this tool into school or university teaching. We present a workflow to process simulation results for AR and provide source material through a template and a guide so that educators can quickly translate their projects into (augmented) reality. The basis for this project is the game engine Unity, which can be used free of charge for educators. Combined with other free or open-source programs for visualization and preparation like openCFS and Paraview, this setup can be used freely by anybody for education.
Impact Of Market Orientation, Learning Orientation and Marketing Strategies on Organizational Performance with The Mediating Role of Innovation: An Empirical Evidence of SMEs of Peshawar and Charsadda

Purpose-This study aimed to investigate the influence of market orientation, learning orientation, and marketing strategies on organizational performance with innovation as a mediator.&#x0D; Design/methodology/Approach-The researchers used a quantitative survey approach and developed a multifactor questionnaire to gather data from 300 SMEs in Peshawar and Charsadda. The internal consistency of the instrument was assessed using Cronbach's alpha, which indicated that the instrument was reliable.&#x0D; Results-Regression analysis was used to examine the impact of market orientation, learning orientation, and marketing strategies on organizational performance. The findings revealed that there is a significant negative impact of market orientation and marketing strategies on organizational performance while the impact of learning orientation is negative but insignificant. However, when the mediating effect of innovation was introduced, a positive impact was observed. This suggests that market orientation, learning orientation, and marketing strategies have a positive impact on organizational performance when innovation is included as a mediator.&#x0D; Conclusion: This study provides valuable insights into the factors that influence organizational performance, highlighting the importance of market orientation, learning orientation, marketing strategies, and innovation. These findings can help SMEs to develop effective strategies that promote innovation and improve their overall performance. Further research could explore the impact of other variables on organizational performance and investigate the relationship between innovation and other performance indicators.
Tinjauan Digitalisasi Benda Bersejarah dari Budaya Lisan Menjadi Arsip Pemerintah

Archives are one of the sectors that have an important role in managing information, especially information about the history of an object from the royal era. Indonesia is a country with a history of quite a lot of kingdoms. The existence of archives that store historical information that can be accessed online will be useful for the z and alpha generations later. Various ways can be used to store archives digitally, one of which is by documenting oral history from the community into scientific articles that can be uploaded on the official website of the local regional government, especially in Ponorogo-Indonesia, such as the sub-district. The purpose of this article is to describe how government websites can act as archives for historical information around the area. This type of research uses a descriptive qualitative approach with the literature review method.
GIBS-Net: Unseen Video Background Subtraction with Global Information

Background subtraction (BGS) is a fundamental task in video processing and serves as the basis for many downstream applications in computer vision. While existing deep learning-based supervised algorithms have achieved remarkable results on certain datasets, their performance may deteriorate when applied to unseen videos. The current BGS algorithms for unseen video primarily focus on utilizing local information from video frames, resulting in limited access to global information. However, this limitation affects the overall performance of the model in accurately separating the foreground objects from the background. Consequently, we sought to address this limitation by incorporating global video information into the model verification process to enhance detection performance. In this study, we propose an improved model, GIBS-Net, by leveraging the Version Transformer (ViT) to enhance spatiotemporal connectivity of data. Our experiments demonstrate that GIBS-Net achieves superior testing performance on the CDNet-2014 dataset compared to state-of-the-art algorithms evaluated on unseen videos in terms of various metrics such as F-measure, recall, and precision. Furthermore, GIBS-Net exhibits excellent performance in processing unseen videos under abnormal weather conditions. In addition, we have conducted lightweight processing on the model and proposed the GIBS-Net-S model, which significantly reduces the number of parameters and floating point operations per second (FLOPs) while maintaining a high frames-per-second (FPS) rate.
Data Back-Up Algorithm Based on Blockchain with Network Erasure Codes

With the increase in data access on the cloud platform, traditional data back-up algorithms based on Raid5 can no longer meet the requirements of data storage and protection. Three copies and network erasure codes are currently the mainstream distributed data back-up methods. The triple copy technology has simple data synchronization but significant disk redundancy. The network erasure code technology has high fault tolerance and low redundancy, However, the computational power caused by data synchronization is high and there are strict requirements for the computing power of the central node. To address the above issues, we adopt the block chain to achieve distributed secure storage of data. We choose triple copy technology or network erasure code technology based on the frequency of disk usage, select the node with the highest computing power at the current stage as the central node to effectively solve the bottleneck problem of network erasure code and record the data protection method adopted by the disk array through block chain. Fully leveraging the advantages of triple copy technology and network erasure code correction technology, it improves the security and utilization of disk arrays.
Seed node selection algorithm based on augmented graph embedding

Identifying influential individuals in information dissemination is an important topic in social network research; however, most studies have difficulty in effectively identifying influential nodes to maximize specific information on the network when the graph embedding representation is not rich and fine enough due to the complex network structure; to address the above problem, a seed node selection is needed. is not rich and fine enough due to the complex network structure; to address the above problem, a seed node selection algorithm model based on enhanced graph embedding is proposed. First, to enrich the graph embedding representation data, an embedding model combining breadth and depth-first search is First, to enrich the graph embedding representation data, an embedding model combining breadth and depth-first search is used for vector representation of node features. Second, to obtain a more refined embedding representation, a feature fusion gate mechanism is proposed to adaptively fuse the node embedding components learned by the graph neural network using the gate mechanism. Experiments conducted on Wiki-2 and the large dataset Epinions show that the proposed method outperforms other existing state-of-the-art methods in terms of the expected impact range metrics.
Design of Section Limit Question Answering System Based on Knowledge Graph

The stable operation limit of power grid is an important boundary condition for the safe and reliable operation of power grid. When the operation mode of power grid changes, if the dispatcher can timely obtain the section limit, it will reduce the fault handling judgment time and improve the power grid control capacity. This paper proposes the design of section limit question answering system based on knowledge graph, which can parse the natural language questions, query the power grid operation rules knowledge graph to obtain section limit, and improve the efficiency of the dispatcher's control measures.
An Artificial Intelligence Model Recommendation Method for Power Dispatching Scenario Based on Knowledge Graph and Scene Label Matching

In the field of power dispatching, more and more tasks have adopted artificial intelligence solutions, and related research and literature are also showing an exponential explosive growth. In order to solve the problem of artificial intelligence information overload in power dispatching scenario, and to facilitate researchers with experience in power dispatching but lacking experience in artificial intelligence to use related algorithm models more effectively and conveniently in their work, this paper constructs a domain knowledge graph of artificial intelligence models for power dispatching scenario, and proposes an artificial intelligence model recommendation method based on knowledge graph and scene label matching. According to the cosine similarity of scene labels, the artificial intelligence model mapped on the knowledge graph is recommended. This method can be applied to the actual power dispatching scenario to provide personalized artificial intelligence model recommendation for different scientific research tasks, which greatly improves the retrieval efficiency of relevant researchers.
Modelling and Sliding Mode Control of a Stereo Vision Augmented 6 DoF Quadrotor System

This study introduces an innovative visual servoing approach for quadrotors equipped with stereo cameras. The approach utilizes stacked Jacobians without requiring inversion, effectively resolving issues related to singularities and reducing computational complexity. Additionally, it improves the robustness of the visual servoing process against image noise. The paper commences by describing the technique of appropriately stacking monocular Jacobians to achieve stereo vision. Subsequently, the acceleration image dynamics are derived from the stereo vision perspective. These dynamics are then utilized to establish a mapping with the quadrotor's 6 degrees of freedom (6 DoF) dynamical model. To control the developed system, a fast terminal sliding mode controller with a power rate proportional reaching law is designed. Numerical simulations are employed to demonstrate the effectiveness of the proposed approach, highlighting the advantages of this innovative method.
Deep Fusion Multi-View Hashing for Multimedia Retrieval

Multi-view hash representation learning is a crucial technique for multimedia retrieval. However, current methods have limitations in integrating the features of multiple views, leading to limited retrieval accuracy. Most studies use fusion methods such as weighted sum or concatenation, which fail to capture the complementarity and consistency between different views. We propose a novel Deep Fusion Multi-View Hashing (DFMVH) method to address the mentioned problems. For the first time, we propose multi-view hierarchical central learning, which effectively solves the complementarity and consistency issue of multi-view fusion. Additionally, we propose a multi-view feature fusion module based on Transformer and verify its superiority through experiments. Our results show that DFMVH outperforms state-of-the-art methods on benchmark datasets such as MIR-Flickr25K, NUS-WIDE, and MS COCO, achieving a significant mean average precision (mAP) increase of up to 12.15%.
Collision-Free Path Generation for Teleoperation of Unmanned Vehicles

Teleoperation, or remote driving, constitutes a crucial transitional phase toward the widespread adoption of fully autonomous vehicles. Nevertheless, to enable seamless real-time teleoperation, it is imperative to address the time delay between the driver and the vehicle. Collision-free path generation has emerged as a vital technique facilitating both teleoperation and autonomous driving, particularly in high-level path planning for vehicles. In the context of real-time teleoperation, a generated collision-free path serves as a valuable guide for the teleoperator, effectively mitigating the impact of time delay. In this research, we present a framework dubbed dual transformer network (DTNet), designed to cater to the needs of teleoperation by addressing road scene understanding. The proposed DTNet employs two transformer-based networks to effectively segment the road free space and detect road objects. Additionally, we introduce an innovative fusion mechanism that leverages the combined information from both networks to predict a collision-free path. The efficacy of the DTNet is extensively evaluated using a large-scale BDD100k dataset, substantiating its superior performance in road free space segmentation and road object detection tasks. Remarkably, DTNet achieves a mean intersection over union score of 83.89% for road free space segmentation and an impressive mean average precision score of 34.20% for road object detection. The experimental findings affirm the effectiveness of the DT-Net framework in addressing the challenges of road scene understanding, making it a promising solution to provide a robust and efficient approach for collision-free path generation, with broader implications for the advancement of autonomous driving technologies.
Beyond the Simplex: Hadamard-Infused Deep Sparse Representations for Enhanced Similarity Measures

Graphical representations are essential for compre-hending high-dimensional data across diverse fields, yet their construction often presents challenges due to the limitations of traditional methods. This paper introduces a novel methodology, Beyond Simplex Sparse Representation (BSSR), which addresses critical issues such as parameter dependencies, scale inconsistencies, and biased data interpretation in constructing similarity graphs. BSSR leverages the robustness of sparse representation to noise and outliers, while incorporating deep learning techniques to enhance scalability and accuracy. Furthermore, we tackle the optimization of the standard simplex, a pervasive problem, by introducing a transformative approach that converts the constraint into a smooth manifold using the Hadamard parametrization. Our proposed Tangent Perturbed Riemannian Gradient Descent (T-PRGD) algorithm provides an efficient and scalable solution for optimization problems with standard simplex or <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\ell_{1}$</tex> -norm sphere constraints. These contributions, including the BSSR methodology, robustness and scalability through deep representation, shift-invariant sparse representation, and optimization on the unit sphere, represent major advancements in the field. Our work offers novel perspectives on data representation challenges and sets the stage for more accurate analysis in the era of big data.
Real time status evaluation of submarine cables based on health index and cloud model

Submarine cables are an important component of various offshore engineering projects, but they are susceptible to the influence of marine environment and human factors. To reduce the risk of submarine cable faults, detect potential faults as early as possible, and conduct real-time evaluation of the operational status of submarine cables. Accurate evaluation can not only understand its operating status and development trend, but also provide certain suggestions and strategies for maintenance. Improve the safety and reliability of submarine cable operation.
Towards an Agile Information Systems Development Process Model

Process models are fundamental for structuring Information Systems Development (ISD) endeavors and contribute significantly to ISD projects’ efficiency and effectiveness. On the one hand, agile process models have been increasingly adopted in software development. In general, the agile approach emerged to overcome the limitations of the classical models, such as the late delivery of products or the lack of response to changes. On the other hand, regarding ISD, as far as we know, there are no specific proposals for agile processes. Considering the socio-technical nature of ISD, agile processes cannot be adopted in this context without being adequately tailored. This paper describes an ISD agile process model, underlying principles, and activities. It also discusses the model’s practical feasibility, benefits, and limitations.
Exploring Natural Language Processing and Sentence Embeddings for Sentiment Analysis of Online Restaurant Reviews

This paper explores the application of Natural Language Processing (NLP) methods in sentiment analysis of restaurant reviews available online, for a sample of restaurants in the Algarve region. The primary objective was to develop an automated method that could efficiently extract and categorize relevant sentiments relating to five key attributes of customer satisfaction, namely food quality, service, ambient, price and restaurant’s location. Using the F1 Score the proposed method was compared against human classification benchmarks. The results showed that Universal Sentence Encoding (USE) was a suitable method for implementation due to its acceptable F1 score performance, ease of accessibility and reduced cost. The use of semantic embeddings can provide valuable insights from online reviews that could benefit the restaurant management and in general the data-driven decision-making processes businesses in the gastronomic sector.
Reflection on African Citizen Identification Systems

A modern and well-structured citizen identification ecosystem contributes to accelerated development. Within the scope of ID4D or ID4Africa, actions are promoted for the development of robust identification and civil registration models. However, there are still many people in the world without any legal identification. This article analyses ecosystems in 11 African countries and highlights common challenges regarding inclusion; security, protection and privacy of citizen data; interoperability; governance; legal framework, among others, therefore, it is recommended the development of an integrated and interoperable conceptual model adjusted to the African reality.
Towards an Integrated Information System for a Higher Education Institution Volunteering Group

This paper describes the first few steps given towards the construction of an Integrated Information System for a Higher Education Institution Volunteering Group. With that purpose, the problem is characterized, requirements are gathered, and the prototype design discussed. Finally, a set of conclusions is presented and direction for future work pointed out.
The Role of Mobile Learning in Supporting Community Health Workers’ Continuous Learning: A Case of Village Health Teams in Patongo and Lokule Sub Counties-Uganda

The study assesses the role of Mobile learning in supporting Community Health Workers' continuous learning using a Smartphone application. The Community Health Workers are members of the community commonly known as Village Health Teams in Uganda. This group is presumed not be a priority for training. The aim is to objectify using Mobile learning affordances to support Community Health Workers' training, an activity presently considered to be irregular and underfunded. This is achieved through the implementation of a Mobile App prototype used as a learning tool for digitized training content on the subject of diseases, reproductive health, sanitation, and family wellbeing. The study involved 41 participants categorized as Community Health Workers, community health students, and Community Health Leaders who selectively participated in piloting the mobile application, responded of a survey questionnaire, took an interview and attended a feedback session. The study examined the availability of Smartphone devices, ease of Mobile App use, challenges of the existing approach to training and information delivery, Mobile Application accessibility issues, and anticipated technical challenges. Despite the conspicuous challenges of mobile technology in a rural setting, 97% of the participants indicated that Mobile learning is a favorable alternative to support the training of Community Health Workers, though, only 48% had access to a Smartphone. Attrition of volunteer workers was identified as the primary challenge to the training of Community Health Workers. The identified advantages relate to geographic convenience, cost of training, numerous mobile services, ease of access, update, storage, and sharing of content. However, imminent bottlenecks identified include the availability of Smartphone devices among Community Health Workers, lack of electricity, unreliable networks, and lack of internet data. Notable interventions from stakeholders aiming to mitigate the challenges are highlighted.
The Novel "Anak Rantau" by A. Fuadi as an Idea for Creating Illustrations

This article discusses the illustrations from the novel "Anak Rantau" by A. Fuadi, intended as a catalyst for solutions to address issues related to teenage reading interest (literacy) and moral crisis. The illustrations aim to motivate teenagers to engage in reading. By presenting illustrations from the novel "Anak Rantau," it is hoped that teenagers will be intrigued to understand the novel's content, resulting in moral outcomes aligned with the ethical values conveyed by the novel. The illustrations employed in this work are of the digital kind, reflecting the advancements in digitally based illustration techniques in this era of globalization. The technique involves a manual combination executed with the assistance of the Medibang application for digitization. Pens, watercolors, and oil pastels serve as the tools and materials used to sketch, color, and finalize the artwork, which is then transferred onto a digital canvas measuring 40 x 60 cm. From this discussion, it can be concluded that this work constitutes illustrations as an alternative solution, employing an approach that utilizes illustrations as catalysts to enhance reading quality, ultimately culminating in the moral improvement of teenagers