Reviewerly/exHarmony huggingface dataset:
Multi-View Stereo Network Based on Attention Mechanism and Neural Volume Rendering

Due to the presence of regions with weak textures or non-Lambertian surfaces, feature matching in learning-based Multi-View Stereo (MVS) algorithms often leads to incorrect matches, resulting in the construction of the flawed cost volume and incomplete scene reconstruction. In response to this limitation, this paper introduces the MVS network based on attention mechanism and neural volume rendering. Firstly, we employ a multi-scale feature extraction module based on dilated convolution and attention mechanism. This module enables the network to accurately model inter-pixel dependencies, focusing on crucial information for robust feature matching. Secondly, to mitigate the impact of the flawed cost volume, we establish a neural volume rendering network based on multi-view semantic features and neural encoding volume. By introducing the rendering reference view loss, we infer 3D geometric scenes, enabling the network to learn scene geometry information beyond the cost volume representation. Additionally, we apply the depth consistency loss to maintain geometric consistency across networks. The experimental results indicate that on the DTU dataset, compared to the CasMVSNet method, the completeness of reconstructions improved by 23.1%, and the Overall increased by 7.3%. On the intermediate subset of the Tanks and Temples dataset, the average F-score for reconstructions is 58.00, which outperforms other networks, demonstrating superior reconstruction performance and strong generalization capability.
Multimodal Global Trajectory Planner for Autonomous Underwater Vehicles

The underwater environment introduces many limitations that must be faced when designing an autonomous underwater vehicle (AUV). One of the most important issues is developing an effective vehicle movement control and mission planning system. This article presents a global trajectory planning system based on a multimodal approach. The trajectory of the vehicle’s movement has been divided into segments between introduced waypoints and calculated in parallel by advanced path planning methods: modified A* method, artificial potential field (APF), genetic algorithm (GA), particle swarm optimisation (PSO), and rapidly-exploring random tree (RRT). The shortest paths in each planned segment are selected and combined to give the resulting trajectory. A comparison of the results obtained by the proposed approach with the path calculated by each method individually confirms the increase in the system’s effectiveness by ensuring a shorter trajectory and improving the system’s reliability. Expressing the final trajectory in the form of geographical coordinates with a specific arrival time allows the implementation of calculation results in mission planning for autonomous underwater vehicles used commercially and in the military, as well as for autonomous surface vehicles (ASVs) equipped with trajectory tracking control systems.
Blockchain Technology for Access and Authorization Management in the Internet of Things

The Internet of Things (IoT) continues to suffer from security issues, even after 20 years of technological evolution and continuing efforts. While the decentralization of the IoT seems to be a solution for improved resource management and scalability, most of the services remain centralized, exposing IoT systems to malicious attacks. As a result, this leads to functionality failures and endangers user and data integrity. Identity and Access Management (IAM) has the ability to provide defense against a great number of security threats. Additionally, blockchain is a technology which can natively support decentralization, as well as access and authorization management techniques, using the corresponding programmable logic and leveraging cryptographic mechanisms for privacy and security. Using standardized frameworks (e.g., Decentralized Identifiers and Verifiable Credentials), a blockchain-based access and authorization solution can present the basis for a uniform decentralized IAM framework for the IoT. To this end, this paper presents a proof-of-concept design and implementation of an IAM solution based on Solidity smart contracts, targeting two areas: firstly, supporting the fact that blockchain can seamlessly provide the basis for a decentralized IAM framework, while secondly (and most importantly) exploring the challenge of integrating within existing IoT systems, avoiding redesigning and redeveloping on behalf of IoT manufacturers.
RCA-GAN: An Improved Image Denoising Algorithm Based on Generative Adversarial Networks

Image denoising, as an essential component of image pre-processing, effectively reduces noise interference to enhance image quality, a factor of considerable research importance. Traditional denoising methods often lead to the blurring of image details and a lack of realism at the image edges. To deal with these issues, we propose an image denoising algorithm named Residual structure and Cooperative Attention mechanism based on Generative Adversarial Networks (RCA-GAN). This algorithm proficiently reduces noise while focusing on preserving image texture details. To maximize feature extraction, this model first employs residual learning within a portion of the generator’s backbone, conducting extensive multi-dimensional feature extraction to preserve a greater amount of image details. Secondly, it introduces a simple yet efficient cooperative attention module to enhance the representation capacity of edge and texture features, further enhancing the preservation of intricate image details. Finally, this paper constructs a novel loss function—the Multimodal Loss Function—for the network training process. The experimental results were evaluated using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) as evaluation metrics. The experimental results demonstrate that the proposed RCA-GAN image denoising algorithm has increased the average PSNR from 24.71 dB to 33.76 dB, achieving a 36.6% improvement. Additionally, the average SSIM value has risen from 0.8451 to 0.9503, indicating a 12.4% enhancement. It achieves superior visual outcomes, showcasing the ability to preserve image texture details to a greater extent and excel in edge preservation and noise suppression.
Delving Deep into Reverse Engineering of UEFI Firmwares via Human Interface Infrastructure

The Unified Extensible Firmware Interface (UEFI) provides a specification of the software interface between an OS and its underlying platform firmware. UEFI UI is an interactive interface that allows users to configure and manage UEFI settings, which is closely related to HII (Human Interface Infrastructure). In practice, HII provides a mechanism that allows developers to create UI elements with HII-related protocols. In this paper, we provide a comprehensive analysis of the UEFI combined with a case study. We proposed a protocol-centered static analysis method to obtain UEFI’s password policy, using HII-related protocols to find password implementation. Existing static analyses are ineffective in detecting such password policy in stripped UEFI firmware images. By reverse-engineering the IFR (Internal Forms Representation) in HII, we located where much sensitive information is stored. Lastly, we studied hardware port configurations, using Secure Boot as a case in point. We analyzed how UEFI uses the HII protocol to set relevant information in the UEFI UI. This paper is the first to offer a reverse-engineering systematic analysis of exploring UEFI via HII, providing valuable insights into its structure and potential enhancements for firmware security.
IFE-Net: An Integrated Feature Extraction Network for Single-Image Dehazing

In recent years, numerous single-image dehazing algorithms have made significant progress; however, dehazing still presents a challenge, particularly in complex real-world scenarios. In fact, single-image dehazing is an inherently ill-posed problem, as scene transmission relies on unknown and nonhomogeneous depth information. This study proposes a novel end-to-end single-image dehazing method called the Integrated Feature Extraction Network (IFE-Net). Instead of estimating the transmission matrix and atmospheric light separately, IFE-Net directly generates the clean image using a lightweight CNN. During the dehazing process, texture details are often lost. To address this issue, an attention mechanism module is introduced in IFE-Net to handle different information impartially. Additionally, a new nonlinear activation function is proposed in IFE-Net, known as a bilateral constrained rectifier linear unit (BCReLU). Extensive experiments were conducted to evaluate the performance of IFE-Net. The results demonstrate that IFE-Net outperforms other single-image haze removal algorithms in terms of both PSNR and SSIM. In the SOTS dataset, IFE-Net achieves a PSNR value of 24.63 and an SSIM value of 0.905. In the ITS dataset, the PSNR value is 25.62, and the SSIM value reaches 0.925. The quantitative results of the synthesized images are either superior to or comparable with those obtained via other advanced algorithms. Moreover, IFE-Net also exhibits significant subjective visual quality advantages.
Predictive Mapping of Prospectivity for Gold in the Central Portion of the Tapajós Mineral Province, Brazil

This work aims to model mineral prospectivity for intrusion–related gold deposits in the central portion of the Tapajós Mineral Province (TMP), southwestern Pará state. The scope includes experimentation and evaluation of knowledge and data-driven methods applied to multisource data to predict potential targets for gold mineralization. The radiometric data processing allowed to identify a hydrothermal alteration footprint of known gold deposits, providing information in regions with little or no field data available. The aeromagnetic data analysis prompted the identification of high magnetic zones, which are probably related to hydrothermal fluid transport. Linear features extracted from digital elevation data revealed an NNW–SSE general trend, which is consistent with the main structural control of deposits. The data were integrated through three modeling techniques—fuzzy logic (knowledge-driven), weights of evidence (WofE, data-driven), and a machine learning algorithm (SVM, data-driven)—resulting in three prospective models. In all models, the majority of indicated prospective regions coincide with the known deposits. The results obtained in the models were combined to generate an agreement map, which mapped the overlapping of their highest prospective scores, indicating new areas of prospective interest in the central portion of the TMP.
Robust Optimization over Time Problems—Characterization and Literature Review

Robust optimization over time (ROOT) is a relatively recent topic in the field of dynamic evolutionary optimization (EDO). The goal of ROOT problems is to find the optimal solution for several environments at the same time. Although significant contributions to ROOT have been published in the past, it is not clear to what extent progress has been made in terms of the type of problem addressed. In particular, we believe that there is confusion regarding what it actually means to solve a ROOT problem. To overcome these limitations, the objective of this paper is twofold. On the one hand, to provide a characterization framework of ROOT problems in terms of their most relevant features, and on the other hand, to organize existing contributions according to it. As a result, from an initial set of 186 studies, the characterization framework was applied to 35 of them, allowing to identification of some important gaps and proposing new research opportunities. We have also experimentally addressed the effect of available information on ROOT problems, concluding that there is indeed a significant impact on the performance of the algorithm and that the proposed classification is appropriate to characterize the complexity of ROOT problems. To help identify further research opportunities, we have implemented an interactive dashboard with the results of the review conducted, which is available online.
Assessing Interactive Web-Based Systems Using Behavioral Measurement Techniques

Nowadays, e-commerce websites have become part of people’s daily lives; therefore, it has become necessary to seek help in assessing and improving the usability of the services of e-commerce websites. Essentially, usability studies offer significant information about users’ assessment and perceptions of satisfaction, effectiveness, and efficiency of online services. This research investigated the usability of two e-commerce web-sites in Saudi Arabia and compared the effectiveness of different behavioral measurement techniques, such as heuristic evaluation, usability testing, and eye-tracking. In particular, this research selected the Extra and Jarir e-commerce websites in Saudi Arabia based on a combined approach of criteria and ranking. This research followed an experimental approach in which both qualitative and quantitative approaches were employed to collect and analyze the data. Each of the behavioral measurement techniques identified usability issues ranging from cosmetic to catastrophic issues. It is worth mentioning that the heuristic evaluation by experts provided both the majority of the issues and identified the most severe usability issues compared to the number of issues identified by both usability testing and eye-tracking combined. Usability testing provided fewer problems, most of which had already been identified by the experts. Eye-tracking provided critical information regarding the page design and element placements and revealed certain user behavior patterns that indicated certain usability problems. Overall, the research findings appeared useful to user experience (UX) and user interface (UI) designers to consider the provided recommendations to enhance the usability of e-commerce websites.
BRAIN: Blockchain-Based Record and Interoperability Network

In today’s highly competitive landscape, effective information sharing is crucial for distinguishing successful entities from the rest. However, achieving seamless collaboration in this digital era remains challenging due to distrust among entities and the absence of a secure data exchange medium. These limitations hinder interactions, reducing competitiveness, especially for resource-constrained entities. To address these challenges, we introduce the Blockchain-based Record and Interoperability Network (BRAIN), a modular framework utilizing blockchain technology. BRAIN offers an innovative solution to foster collaboration, trust, and competitiveness. By leveraging blockchain’s cryptographic techniques, it ensures secure and transparent data exchange, eliminating the need for intermediaries and thereby enhancing productivity and collaboration. BRAIN serves as a configurable foundation for domain-specific applications. BRAIN’s key features include flexibility, efficiency, security, robustness, and scalability. It adapts to diverse applications, integrates seamlessly with existing systems, and ensures secure information sharing. In conclusion, BRAIN provides a foundational platform for entities to assert data ownership and grant access permission. Built on blockchain technology, it offers access traceability and data security and eliminates the need for trusted third parties. Its flexibility and scalability make it a valuable tool for enhancing data sharing. Future enhancements aim to address weaknesses and expand its use cases, promising a robust and versatile solution for secure and efficient data exchange.
Postdigital Academic Writing

This entry focuses on postdigital academic writing, providing a socio-historical exploration of writing in higher education and its meaning for advancing collective intelligences. It shows that postdigital writing cannot be divorced from its context and hence it should be seen as a tool for learning in a human and more-than-human collaboration. Postdigital academic writing, if taken seriously, has the potential to reframe education and research as multimodal, inspirational, authentic, and meaningful.
How Does The Bilingualism Improve the Student English Ability: a Discourse Case Study on Higher Education

In this 4.0 era, the use of English as a mean of communications playing a vital role to work in many parts such as technology, education, communication and many others. It is true that not all people can communicate in English fluently and clearly. Luckily, some people or students especially, use bilingualism to study or learn English. Many of them think by using bilingualism they still can interact or communicate by using English as a mean of interaction. Focus of this research was to find out how the bilingualism improve the English student ability. This research used a descriptive qualitative research. Data were collected by using questionnaire and interview guideline. Data were analyzed by using the content analysis and the theory used was proposed by Hoffman 1991. Population of the research was all of students of English for mathematics consisting 35 students. This research used total sampling for instrument using questionnaire and used purposive sampling for the semi-structure interview. The research shows that bilingualism occur in the classroom interaction such as talking about particular topic, quoting somebody else, being emphatic about something, interjection, repetition used for clarification, expressing a group of identity and intention of clarifying the speech content for interlocutor. From the analysis, it is seen that mostly bilingualism used by the students to study about particular topics such as term, theme and content. The least used by student is on the intention of clarifying the speech content for interlocutor because the students always think that what the people say are true and they don’t know how to express the clarification in English. However, for the English learning, bilingualism has already helped both the lecturers and students to interact and communicate about various topics. It is a good start to introduce English as the foreign language In Indonesian Higher Education where English is still considered as the foreign language.
Test case generation and history data analysis during optimization in regression testing: An NLP study

The generation of test cases to verify and validate the actions of software or an application, as per the customers' requirements, is an indispensable activity in software industries. A tester could construct test cases to suffice various objectives, which could be random or task-oriented at times. Most of the time, test cases are generated based on clients' specifications or requirements. These requirements are structured in natural language, and manual derivation of test cases from such client-stated requirements could be a cumbersome and time-absorbing activity for testers. Until recently, many practitioners have proposed a natural language processing (NLP)-oriented solution to automate or semi-automate the manual process of generating test cases from requirements; nevertheless, such studies imposed a restriction on how the clients should document or represent their requirements. This study, on the contrary, suggested an NLP solution that considers free-format user requirements and applies text pre-processing, a combination of dependency parser and RAKE process, along with a statistical similarity measure and template-based natural language generation (NLG) to translate them into detailed test cases. Apart from test case generation, with the aid of NLP tactics, this study has also proposed a solution for encoding the historical data of test cases into numerical values. Such numerical scores serve as valuable data and create the proper insight for testers during test case optimization.
Pengaruh Teknik Kekaguman Berbantuan Media Audio Visual dalam Pembelajaran Menulis Teks Puisi

This study aims to determine the effect of the awe technique assisted by audio-visual media on students learning to write poetry texts in class VIII SMP Negeri 27 Makassar. The data for this research is quantitative data from students' pretest and posttest results. The type of research used by researchers in this study is quantitative using a pre-experimental design, namely a one-group pretest and posttest design. The population in this study was class VIII students of SMP Negeri 27 Makassar, with a total of 358 students divided into 11 classes. The sampling technique used in this study was purposive sampling. The sample taken in this study was from class VIII 9 which totaled 35 students. The data analysis technique used in this research is quantitative descriptive data analysis. The results showed that the average value of students' short story writing in the pretest was 60.9 while the average value in the pretest was 83. If the result of one sample test shows the value of tcount &amp;#62; ttable, which is 8.398 &amp;#62; 2.032 then the hypothesis H0 is rejected and H1 is accepted. This study shows that the use of awe techniques assisted by audiovisual media has an effect on learning to write poetry texts.
Governance, tourism and inclusive growth in Africa

Abstract The use of Gross Domestic Product (GDP) per capita as a measure of economic growth has been recently subject to wide criticism by many economists. It has been observed that although some countries have experienced economic growth in recent years, income inequality and poverty continue to be exacerbated. Recently, sustainable development goal 8 has been geared to promote inclusive growth. Considering this fact, this study expands the literature by examining the relationship between tourism development and inclusive growth for a panel of 44 African countries over the period 2000–2020. Further, the study also adds to the literature by examining the moderation of governance quality on the tourism‐inclusive growth nexus. The investigation is made using the generalized method of moments as an estimation technique. The result indicates that tourism and governance quality appear to promote inclusive growth. Moreover, the results indicate that good quality of institutions or governance could complement tourism development to promote inclusive growth, as positive synergies are apparent from the role of governance in moderating the incidence of tourism on inclusive growth. Policy implications are discussed.
A Cost-Effective System for Indoor Three-Dimensional Occupant Positioning and Trajectory Reconstruction

Accurate indoor occupancy information extraction plays a crucial role in building energy conservation. Vision-based methods are popularly used for occupancy information extraction because of their high accuracy. However, previous vision-based methods either only provide 2D occupancy information or require expensive equipment. In this paper, we propose a cost-effective indoor occupancy information extraction system that estimates occupant positions and trajectories in 3D using a single RGB camera. The proposed system provides an inverse proportional model to estimate the distance between a human head and the camera according to pixel-heights of human heads, eliminating the dependence on expensive depth sensors. The 3D position coordinates of human heads are calculated based on the above model. The proposed system also associates the 3D position coordinates of human heads with human tracking results by assigning the 3D coordinates of human heads to the corresponding human IDs from a tracking module, obtaining the 3D trajectory of each person. Experimental results demonstrate that the proposed system successfully calculates accurate 3D positions and trajectories of indoor occupants with only one surveillance camera. In conclusion, the proposed system is a low-cost and high-accuracy indoor occupancy information extraction system that has high potential in reducing building energy consumption.