Reviewerly/exHarmony huggingface dataset:
Swin Transformer-Based Edge Guidance Network for RGB-D Salient Object Detection

Salient object detection (SOD), which is used to identify the most distinctive object in a given scene, plays an important role in computer vision tasks. Most existing RGB-D SOD methods employ a CNN-based network as the backbone to extract features from RGB and depth images; however, the inherent locality of a CNN-based network limits the performance of CNN-based methods. To tackle this issue, we propose a novel Swin Transformer-based edge guidance network (SwinEGNet) for RGB-D SOD in which the Swin Transformer is employed as a powerful feature extractor to capture the global context. An edge-guided cross-modal interaction module is proposed to effectively enhance and fuse features. In particular, we employed the Swin Transformer as the backbone to extract features from RGB images and depth maps. Then, we introduced the edge extraction module (EEM) to extract edge features and the depth enhancement module (DEM) to enhance depth features. Additionally, a cross-modal interaction module (CIM) was used to integrate cross-modal features from global and local contexts. Finally, we employed a cascaded decoder to refine the prediction map in a coarse-to-fine manner. Extensive experiments demonstrated that our SwinEGNet achieved the best performance on the LFSD, NLPR, DES, and NJU2K datasets and achieved comparable performance on the STEREO dataset compared to 14 state-of-the-art methods. Our model achieved better performance compared to SwinNet, with 88.4% parameters and 77.2% FLOPs. Our code will be publicly available.
Development of PBL-Based Sound Wave Interactive Multimedia Using Lumi for Class XI High School Students

This study aims to describe the development, feasibility, and practicality of PBL-based sound wave interactive multimedia using Lumi. The research method used is research and development. The model used is an Instructional Design type ADDIE which consists of 5 stages namely Analyze, Design, Development, Implementation, and Evaluate. The results showed that PBL-based sound wave interactive multimedia using Lumi was declared feasible by the validator with an average Aiken validity index of 0.86 (very high). The teacher as a practitioner states that practical interactive multimedia is used in learning physics with an average value of 93.30 (very practical). Students also stated that practical interactive multimedia was used in learning physics with an average value of 90.46 (very practical). Based on the results of the study, it can be concluded that PBL-based interactive multimedia using Lumi is declared feasible and practical for use in the physics learning process, especially in sound wave material.
Postgraduate Student’s Attitudes towards Investment Decision-making: A Comparative Study between India and Nigeria

This comparative study aims to explore and analyze the attitudes of postgraduate students towards investment decision-making in the contexts of India and Nigeria. Through a survey conducted among 181 postgraduate students in both the countries, this research investigates their perceptions, preferences, and approaches when making investment decisions. By employing a structured questionnaire, the study assesses factors influencing investment choices, risk tolerance, information sources, and overall financial literacy levels. Drawing on the psychological, social, and economic distinctions between India and Nigeria, the research seeks to identify similarities and differences in investment attitudes among the two populations. The findings not only contribute to the literature on factors affecting investment behavior but also provide insights for financial educators, policymakers, and financial institutions to tailor strategies that enhance the investment decision-making skills of postgraduate students in both nations. Finally, this study shows the importance of educational background in shaping individual investment preferences and choices.
An Artificial Intelligence-Based Model for Knowledge Evaluation and Integration in Public Organizations

In the construction of knowledge bases, it is very important to evaluate the quality of the knowledge entered into them. This is exacerbated in public administrations, where knowledge should be oriented towards public services. In this study, an artificial intelligence-based method for the evaluation of knowledge is described. This method takes advantage of the structure and contents of the knowledge representation schemas (representing the knowledge of the corresponding experts) to carry out knowledge evaluation. More precisely, the method allows the various comparisons between the schemas to be integrated and the overall schema to evaluate the contribution of each schema.
Predicted resource assessment of Central Kazakhsta ore districts based on airborne geophysical methods

Purpose. The research is aimed at creating a high-quality geophysical basis for the additional geological study of Central Kazakhstan in determining the geological structure of the study area, identifying promising areas for further geological exploration, as well as analyzing the anomalous distribution of various minerals. Methodology. The research uses the methods of aeromagnetic, airborne gamma-ray spectrometry and gravimetric (ground) surveys. The obtained new airborne geophysical data are used for additional study of the geological structure of the district and the creation of a model of the geological structure of the study area. Gamma-ray spectrometry data analysis is conducted for detailed mapping of intrusive complexes and study of their lithological heterogeneity. Findings. The processing and interpretation of the materials of the conducted field studies in combination with the results of the geological-geophysical data analysis made it possible to refine the geological structure and to present a model of ore-prospecting complexes in the study area. Areas with an anomalous distribution of potassium, uranium, and thorium have been identified, which make it possible to assess the structural heterogeneity of hidden magmatic massifs and to reveal a connection with gold, copper-polymetallic and rare-metal mineralization. Recommendations for further detailed geological exploration, including prospecting-exploration drilling, are given. Originality. Research has shown the high efficiency of airborne geophysical methods, such as aeromagnetic and airborne gamma-ray spectrometry surveys to study the geological structure of ore areas, and in combination with detailed gravimetric exploration to identify lithological heterogeneity of igneous and sedimentary rocks in the study area. New scientific-methodological techniques and the geological structure model based on them gave an opportunity to assess the prospect of the study area for ore mineralization. Practical value. The identified promising areas for mining of minerals are of interest for further research and exploration. The integrated use of geological-geophysical data will make it possible to determine more precisely the nature of radioactive anomalies and to reveal their relationship with ore-prospecting horizons. In general, the research results contribute to increasing the efficiency and reducing the geological survey costs.
Multi-scale Fusion Residual Dense Dehazing Network

A multi-scale fusion residual dense dehazing network is proposed to address the problems of high model complexity and incomplete dehazing in most existing end-to-end dehazing algorithms. In the residual dense block (DSRB), the feature information is extracted by increasing the convolutional kernel perceptual field using smooth dilation convolution, and the continuous memory mechanism is formed by means of dense jump connections to improve the characterization ability of the network. DWC and PWC convolution are used to generate channel attention and pixel attention mechanisms to fuse shallow features and deep features at each scale and focus on target features adaptively. Finally, the method of this paper is tested on the SOTS test set of RESIDE, and the results show that the model complexity of this paper’s method is low, and the PSNR and SSIM metrics are improved, and the visual effect is good.
FLASH: Facial Landmark Detection Using Active Shape Model and Heatmap Regression

Detection of facial landmarks is a critical task for human face identification, emotion recognition in autopilot and real-time visual monitoring applications. This is really challenging due to the high number of discrete landmarks spreading over the face which is of different shapes and may be occluded or obscured. Many methods have been proposed over the years including ASMNet and AnchorFace. However, their performance is still limited in terms of both accuracy and efficiency. In this paper, we propose a novel method for facial landmark detection based on active shape model and heatmap called FLASH. The heatmap aims to highlight the important landmarks. Meanwhile, the shape model helps to conform the distribution of such landmarks. FLASH has been evaluated on two public datasets 300W-Challenging, WFLW and achieved a normalized mean square error (NME) of 6.67%, 7.34% correspondingly, which outperforms most existing methods. Specifically, this is much better than the recent ASMNet method with a NME of 8.20%, 10.77% on the two datasets, respectively. This is also comparable to the state of the art AnchorFace with a NME of 6.19%, 4.62%, correspondingly. The source code of FLASH is also publicly available.
Survey on Deep Learning Approaches for Detection of Email Security Threat

Emailing is among the cheapest and most easily accessible platforms, and covers every idea of the present century like banking, personal login database, academic information, invitation, marketing, advertisement, social engineering, model creation on cyber-based technologies, etc. The uncontrolled development and easy access to the internet are the reasons for the increased insecurity in email communication. Therefore, this review paper aims to investigate deep learning approaches for detecting the threats associated with e-mail security. This study compiles the literature related to the deep learning methodologies, which are applicable for providing safety in the field of cyber security of email in different organizations. Relevant data were extracted from different research depositories. The paper discusses various solutions for handling these threats. Different challenges and issues are also investigated for e-mail security threats including social engineering, malware, spam, and phishing in the existing solutions to identify the core current problem and set the road for future studies. The review analysis showed that communication media is the common platform for attackers to conduct fraudulent activities via spoofed e-mails and fake websites and this research has combined the merit and demerits of the deep learning approaches adaption in email security threat by the usage of models and technologies. The study highlighted the contrasts of deep learning approaches in detecting email security threats. This review study has set criteria to include studies that deal with at least one of the six machine models in cyber security.
Battery State-of-Health Aware Path Planning for a Mars Rover

A rover mission consists of visiting waypoints to gather scientific samples based on set requirements. However, rovers face operational uncertainties during the mission, affecting the performance of its electrical and mechanical components and overall mission success. Hence, it is critical to have a decision-making framework that is aware of the health state of the components when planning the path of the vehicle. In particular, battery degradation, and consequently the battery State of Health (SOH), can affect the optimality of decisions made by the autonomous system in the long term. This paper presents a decision-making system that incorporates information on the energy drawn from the battery (based on the velocity of the vehicle), terrain conditions, and model-based prognostic modules to assess impact on the battery state of charge (SoC). The decision-making system was formulated as a Markov Decision Process (MDP) to reach the goal destination by sending commands in a determined amount of time, while maintaining the battery SoC within the policy stated. The MDP problem was programmed using the open-source framework POMDPs.jl, which has a variety of online and offline solvers. To solve the MDP problem online, we used Monte Carlo Tree Search (MCTS). Results from simulations demonstrate the effect that battery degradation and charging plans have on decision-making.
Compression of PlenOctree Model Attributes Enabling Fast Communication and Rendering of Neural Radiance Fields

Neural Radiance Fields (NeRF) have led the advancement of techniques for 3D scene representations for synthesizing views through the use of a multilayer perceptron. Its inability to achieve real-time performance for photorealistic image rendering, however, has enabled the advent of methods that speed-up rendering time at the cost of rendering quality or model complexity. In this work, we focus on the existing Plenoctree method, which possesses high rendering speed but unfortunately needs a large space for storing and transmitting its model. We address this weakness by proposing improvements to the different blocks of its pipeline and adding an efficient compression stage, without modifying the underlying representation, while maintaining high rendering quality and speed. Results over a set of test camera poses - using our methods which were obtained with data from the training and validation datasets - show that we can reduce about eight times the bit rates of the encoded models and still obtain a higher quality of the synthesized images when comparing them to the original PlenOctree models or, alternatively, a reduction of about 50 times while presenting minimal degradation for novel view synthesis.
Subjective Evaluation of Dynamic Point Clouds: Impact of Compression and Exploration Behavior

The objective of this work was to explore the subjective quality assessment of dynamic point clouds with compression artifacts and to analyze the exploration behavior of the users while visualizing them with a Head-Mounted Display with 6 Degrees-of-Freedom. Thus, this paper presents a subjective study on dynamic point clouds using the Absolute Category Rating methodology and considering different compression rates using the MPEG standard Video-based Point Cloud Compression. Firstly, results on the impact of compression artifacts on the perceived Quality of Experience of the users are reported, showing the validity of Absolute Category Rating, although more than 20 observers may be needed to obtain robust conclusions. Results on users' exploration behavior show no significant differences when visualizing point clouds with different qualities, no changes in the behavior during the test session, and no correlation between exploration activity and quality assessments. Further research will be conducted to help identify appropriate methodologies for the subjective assessment of point clouds and for understanding users' exploration behavior.
Teachers' instructional design for e-learning of deaf and hard of hearing students

The purpose of this study was to examine teachers ’ e-learning instructional design and practices for deaf/hard of hearing students during the COVID-19 period. For this reason, this study is assumed to serve as a reference point for the development of accessible e-learning instruction for teachers of deaf/hard of hearing students. This study is a quantitative research in survey design. During the COVID-19 pandemic, education and communication were maintained thanks to e-learning, which has become an integral part of education. Since it was intended to establish the attitudes, thoughts, and behaviors of the sample over a specific time period, a cross-sectional design was used. The data collection tool was an online survey developed by the researcher. The participants of the study were 138 teachers. However, neither before nor throughout the COVID-19 period, 13 of the participating teachers had no experience with e-learning. Therefore, they did not answer the questions related to e-learning in the third part of the survey. Descriptive statistics were used to analyze the data. As a result, it was determined that teachers of deaf/hard of hearing students were unprepared for the e-learning process. They require assistance with the instructional design for e-learning.
Multi-Agent Collaborative Target Search Based on the Multi-Agent Deep Deterministic Policy Gradient with Emotional Intrinsic Motivation

Multi-agent collaborative target search is one of the main challenges in the multi-agent field, and deep reinforcement learning (DRL) is a good way to learn such a task. However, DRL always faces the problem of sparse reward, which to some extent reduces its efficiency in task learning. Introducing intrinsic motivation has proved to be a useful way to make the sparse reward in DRL. So, based on the multi-agent deep deterministic policy gradient (MADDPG) structure, a new MADDPG algorithm with the emotional intrinsic motivation name MADDPG-E is proposed in this paper for the multi-agent collaborative target search. In MADDPG-E, a new emotional intrinsic motivation module with three emotions, joy, sadness, and fear, is designed. The three emotions are defined by corresponding psychological knowledge to the multi-agent embodied situations in an environment. An emotional steady-state variable function H is then designed to help judge the goodness of the emotions. Based on H, an emotion-based intrinsic reward function is finally proposed. With the designed emotional intrinsic motivation module, the multi-agent system always tries to make itself joy, which means it always learns to search the target. To show the effectiveness of the proposed MADDPG-E algorithm, two kinds of simulation experiments with a determined initial position and random initial position, respectively, are carried out, and comparisons are performed with MADDPG as well as MADDPG-ICM (MADDPG with an intrinsic curiosity module). The results show that with the designed emotional intrinsic motivation module, MADDPG-E has a higher learning speed and better learning stability, and the advantage is more obvious when facing complex situations.
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The dynamic development of modern pedagogical science is characterized by the search for basic approaches to building the educational process in higher education.It is aimed at educating and training a professionally competent, proactive, creative specialist with highly developed personal and creative skills.Such a specialist should be endowed with a sense of duty and responsibility to society.Moreover, he or she must be able to quickly adapt to the modern world, characterized by an increased role of the individual, and intellectualization of his or her activities in the face of rapid changes in the development of science and information technology.The article aims to identify the patterns and clarify the main vectors of higher
Do institutional or subject referencing style choices create barriers for students with specific learning disability?

Models of information literacy (Coonan et al., 2018; SCONUL, 2011) are explicit: critical thinking and ethical information use are essential skills within higher education (HE). Referencing is key to this, demonstrating how students select and apply information to create knowledge (Buckley, 2015; Angelil-Carter, 1995). Within HE there has been an increased focus on inclusivity and accessibility (Equality Act, 2010; United Nations, 2015; Department for Education and Department for Health, 2015). Growing numbers of students are declaring a disability (Advance HE, 2019) and reports suggest they are increasingly dissatisfied with their courses (Office for Students, 2020). Proportionally, students with disabilities achieve lower grades than students without (Advance HE, 2019), suggesting needs and expectations of students with a disability are not being met within HE provisions. The presentation discussed an ethically approved small-scale mixed-methods study carried out as part of a MA in Special Educational Needs and Inclusion. The research investigated student perceptions of referencing, whether adherence to specific referencing styles is a barrier for students with dyslexia and began investigating the impact of referencing styles on reading comprehension. The largest disability declared within HE is Specific Learning Disability (SpLD) (Advance HE, 2019), which includes dyslexia (American Psychological Association, 2013). Students with SpLD report lower confidence with academic writing than non-SpLD students (Kinder and Elander, 2012). Academic literacy skills are arguably intertwined with a sense of legitimacy and belonging (Gourlay, 2009): it is therefore vital to consider ways of improving inclusion for all students (Office for Students, 2020). The presenter observed that students with SpLD spent more time and energy on referencing than their non-disabled peers. This perception is corroborated by others (Sanders, 2010) and when combined with slower reading speeds (Hendricks and Quinn, 2000; Sanders, 2010; Serry et al., 2018) reduces time students have for critical subject engagement (Wengelin, 2007). Attendees gained an understanding of how students view referencing and whether the choice of referencing system disadvantages students with dyslexia. Attendees also took part in a reading comprehension test giving a taster of the next steps for research which requires collaborative partners.
The Applying of Asking Stage Strategy to Teach Reading Comprehension

Abstract&#x0D; The main problem of this study was: is it significantly effective to teach reading comprehension by applying of asking stage strategy. Therefore, the main objective of this study was to find out whether or not it is significantly effective to teach reading comprehension by applying of asking stage strategy to the seventh grade students. Method used was pre-experimental method with one group pre-test post-test design. Population was all of the seventh grade students at SMP Negeri 5 Lubuklinggau which consisted of 8 classes with 261 students. Sample of this study was taken by using cluster random sampling, with number of sample 35 students. Data were collected through test consisting 20 items of multiple choices. The obtained data were analyzed through three techniques: 1) Individual Score, 2) conversion of score range, 3) the matched t-test. There were three major findings in this study, they are: (1) students’ mean score in the pre-test was (67.14), (2) students’ mean score in the post-test was (79.29), and (3) the result of matched t-test calculation should that t obtained was (6.75), which is higher than 1.697 as the critical value of the t table for degree of freedom 34 (35-1) with 95% significant level for one-tailed test. It can be concluded that the null-hypothesis (Ho) was rejected and the alternative hypothesis (Ha) was accepted. The fact shows that it was significantly effective to apply asking stage strategy for teaching reading comprehension for the seventh grade students at SMP Negeri 5 Lubuklinggau.