Reviewerly/exHarmony huggingface dataset:
Tiny Machine Learning for Underwater Image Enhancement: Pruning and Quantization Approach

Many people have expressed an interest in underwater image processing in a variety of fields, including underwater vehicle control, archaeology, marine biological studies, etc. Underwater exploration is becoming an increasingly important element of our lives, with applications ranging from underwater marine and creature research to pipeline and communication logistics, touristic and entertainment use. Underwater images suffer from poor visibility, distortion, and poor quality for a variety of causes, including light propagation. The major issue arises when these images must be captured at depths greater than 500 feet and artificial lighting needs to be provided. Efficient algorithms and models were proposed to enhance the images taken underwater. However, these networks challenge efficient real-time deployment and needs significant computational resources and energy costs. In this paper these challenges will be tackled by using different network compression techniques such as pruning and quantization. These techniques will be applied on a model named WGH-net and the resulting in a tiny machine learning model is named tiny WGH-net model. Moreover, we compare the results of both models along with other models proposed by other researchers. Sustainable Development Goals (SDG) will also be discussed along with our contribution to some of the goals.
A Spatial-temporal Neural Network for Photovoltaic Power Prediction

The access of photovoltaic (PV) systems in the distribution networks has facilitated accurate PV power prediction for energy coordination and operations planning. Due to the power quality and photoelectric consumption of distribution system, the prediction task is still challenging. This paper aims to provide a spatial-temporal network to capture appropriate dependencies between temporal historical PV data and spatial effect factors for the multiple-step situational predictions. It involves a simplified version of convolution and pooling operation used to capture local features and model spatial correlations in the input data, and an attention based bidirectional long short-term memory (BiLSTM) used to enhance that association data prediction. Experimental results demonstrate that the proposed prediction network indicates enhanced accuracy across various time intervals and exhibits superior performance in prediction stability and robustness when compared to typical prediction methods based on the actual measurements from a photovoltaic micro plant. The method excels in capturing both temporal and spatial dependencies, making it a valuable tool for energy planning and operations in distribution networks.
Simulation of User Arrangement in Virtual Reality for Design Metaverse Network : Focus On Statistical Distribution

Several external factors, including COVID-19, have prompted a trend towards converting existing systems and services into non-contact alternatives. Furthermore, beyond non-face-to-face services for individuals and small groups, such as phone calls and small meetings, efforts are underway to transform non-face-to-face services for large groups, such as classes and concerts. In particular, current virtual reality platforms like metaverse are insufficient to provide non-face-to-face services to large groups, guaranteeing Quality of Service (QoS) for users. Calculating the expected number of users for such services is vital to prepare and design the network infrastructure and resources accordingly. This paper proposes a technique to generate and arrange virtual users who can simulate a real-world environment to design a metaverse network that can accommodate a large number of people. Various large-scale service arrangement techniques are outlined, utilising statistical distributions. Based on the arrangement of users in the XR service, we proposed ways to choose the appropriate servers and channels during the XDN design phase, along with the use of XDN's microservices.
Multi-objective optimization method of smart medical wireless sensor network coverage based on improved cuckoo algorithm

In order to improve the coverage level of smart medical wireless sensor network, a multi-objective optimization method of smart medical wireless sensor network coverage based on improved cuckoo algorithm is proposed. Construct the routing node deployment structure model of smart medical wireless sensor network, adopt the channel balanced scheduling method to realize the structured scheduling and spread spectrum processing of the transmission layer of smart medical wireless sensor network, adopt the multi-sensor fusion tracking identification method to realize the identification of the coverage parameters of smart medical wireless sensor network, adopt the improved cuckoo algorithm to establish the dynamic distribution function of wireless sensor network coverage multi-objective optimization, and randomly scatter several sensors in the area to be monitored. And set a certain number of anchor nodes, initialize the position parameters of the population, calculate the fitness value of the initial population and sort it from small to large. Through the joint parameter identification of the initial position set and the target position set of the mobile node, the multi-objective optimization of the coverage of the smart medical wireless sensor network is realized. The simulation test results show that this method can improve the coverage level of nodes and reduce the network energy cost and transmission error rate.
Animal Feed Optimization under Price Fluctuations using Evolutionary Algorithms

In the livestock industry feed cost impacts overall production cost, as the cost of feed amounts to over 60% of the production costs. This makes feed formulation of utmost concern for many breeders. Various challenges including ingredient short-age, and ingredient price fluctuations are encountered during the feed formulation process. In this work, using evolutionary algorithm, the feed formulation problem is modified to include feed cost variation that models feed ingredient price fluctuations, to minimize the feed cost per month. The objective function is modified by generating synthetic ingredient price from real-world price data. A 20% standard deviation is used to generate 12 different costs representing the cost for each month in the year. The proposed method incorporates possible price variations to search for optimal solutions in providing adequate feed materials that minimizes the cost for each month, and can select unique feed materials for each month that fits the animals growth stage and nutritional requirements.
Enhancing Autonomous Driving with Grounded-Segment Anything Model: Limitations and Mitigations

Recent advancements have been observed in the domains of object detection and semantic segmentation, notably with the introduction of the Grounding DINO and Segment Anything Model (SAM). In the realm of autonomous driving, an effective model is crucial to accurately perceive the road environment, thus warranting the exploration of these innovative models. The Grounded-SAM, known as Grounded Segment Anything Model, is an advanced machine learning model that has been designed for object detection and semantic segmentation. This feature makes it an invaluable tool in domains like autonomous driving, where accurate real-time object detection and understanding of the driving environment are critical for safe and efficient operation. In this paper, we first work on exploring limitations of Grounded-SAM in autonomous driving, where we identified its weakness in differentiating objects in fine-grained tasks. To mitigate this limitation, we leverage zero-shot and pre-trained classifiers to perform traffic signs and vehicles detection to enhance the precision of Grounded-SAM in autonomous driving.
Wordle Distribution Prediction Model Based on Random Forest

In order to get the distribution of the percentage of the accuracy of guesses, this paper uses Random Forest Regression to predict the percentages of (1, 2, 3, 4, 5, 6, X) in a future day. First, this paper adds several attributes into pro-processing data. Then, this paper takes the new data’s row as inputs and exports the output distribution. In addition, this paper constructs the Distribution Prediction Model Based on Random Forest that contains normalization part, random forest part and the weighted averaging output function. Moreover, this paper analysis Pearson’s correlation coefficient and draws the heatmap to show the final results. After many experiments and stimulations, this paper can get the EERIE on March 1, 2023, percentage is (0.23, 2.97, 15.23, 30.94, 29.66, 16.38, 3.38). And this paper also makes sensitivity analysis to test its reliability.
Gesture Classification Based on System’s Emotion and Strategy for Korean Dialogue Systems

During the conversation, the speaker’s gestures can give more information to the listener and make it easier to understand the speaker’s utterance. Although appropriate system gestures based on the system strategy and emotion are crucial in a conversation between a person and a dialogue system, no Korean conversation corpora have system utterances annotated with both system strategies and emotions. In this paper, we pseudo-annotate system gestures on system utterances in the extended version of the Korean empathetic conversation corpus. Based on the pseudo-annotation, we introduce a system gesture list classification model on the basis of Korean-English T5 (KE-T5). We achieve an accuracy of 90.8% on the gesture list classification model.
Multi-Task Learning-based Research on the Problem of Conversational Emotion Recognition

Emotion Recognition in Conversations (ERC) can determine the emotion state of a conversation by analyzing text or speech information in the conversations, which has led to its widespread interest. Many approaches also exist dedicated to contextual understanding through deep learning. The paper proposes the novel Multi-Task Learning Network for Emotion Recognition in Conversations (DialogueMTLN). Two networks are built separately, and the two tasks proceed in parallel using a multi-task learning approach. The respective loss functions are weighted and summed by an adaptive method and then back-propagated to train the network, establishing the critical process of emotion cue inference, achieving a complete understanding of the conversational context, and finally accomplishing an accurate classification of the emotion in the conversations. In extensive experiments, the proposed model is shown to be effective and superior on two public benchmark datasets.
A Study on Military Object Detection in Panoramic View Using Stable Diffusion

The bottleneck in the development of military object detectors based on deep learning is the costly and high-difficulty training data collection. Although various methods have been proposed to solve this problem, augmentation methods for sparse data, which is extremely difficult to collect, is still an area of high difficulty that requires continuous research. This paper proposes a method to generate training data of an object detector for military smart glasses with a panoramic view with extremely insufficient learning data by using outpainting with stable diffusion. Through experiments, it has been proved that the proposed method has far superior training performance than training data generated by simply cropping or concatenating existing training data in the form of a panoramic view. The proposed method can be used as one of the excellent data augmentation methods in a situation where an image of unusual size, such as a fish-eye view image or an ultra-wide-angle image, is required as training data.
Enhancing Prototypical Space for Interpretable Image Classification

In the Industry 4.0 era, machine learning and artificial intelligence have emerged as influential drivers in the domain of information and communication technology (ICT). This fusion of human interaction and technological progress emphasizes the necessity for artificial intelligence systems that provide transparent outcomes. The introduction of explainable artificial intelligence (XAI) plays a crucial role in establishing trust and facilitating the broader integration of AI. However, the earlier prototype-based XAI models encounter a constraint in acquiring a meaningful representation for all patches in the prototypical space. This limitation arises from its inherent update process, wherein the update gradient is only transmitted through the most active patch of the input image. To address this limitation, we introduce an innovative XAI model that introduces a novel reconstruction loss implemented by adopting a Variational autoencoder (VAE). This reconstruction loss offers insights into the rationale underlying the prototypical space and similarity mechanism within part prototypical-based XAI model, thereby enhancing comprehension of its fundamental principles. Extensive experimental results demonstrate the effectiveness of our method over existing approaches through evaluation on the widely used CUB-200-2011 dataset.
Towards a Lightweight Object Detection through Model Pruning Approaches

Object detection tasks represent one of the most prevalent areas of study in computer vision, leading to the introduction of numerous techniques. Among these, the You Only Look Once (YOLO) series of object detection models continued to evolve and progress. The latest iterations within the YOLO family exhibit enhanced performance and quicker inference times. However, the increased capacities and memory demands of these models present real-world challenges in terms of practical deployment. This underscores the importance of developing lightweight versions of the updated YOLO models to ensure their applicability in real-life scenarios. In this context, this study introduces YOLOv7 lightweight, building upon a prior channel pruning technique employed for YOLOv5. By adopting the foundational method to align with the YOLOv7 architecture, we effectively managed to reduce the model’s complexity. Furthermore, this research delves into identifying the appropriate pruning levels and model configurations tailored specifically for human detection tasks. In the course of our investigation, we evaluated the trade-off between performance degradation and reductions in parameters and computational complexity. This analysis led us to select a pruning protection ratio of 50% as the most optimal value. Moreover, this article presents the optimization of the lightweight YOLOv7 model for efficient human detection. In essence, our research not only suggests enhancements to existing methodologies for updated models but also emphasizes the practical application of such methods through a comprehensive grasp of the unique characteristics of updated models.
Research and Application of Convolutional Network Voice Based on Substation Inspection Scenario

With the rapid development of smart grids in China, more intelligent systems are put into the power grid, and new scene requirements are generated. Consequently, we would like to propose a speech recognition method, which is based on the improved convolutional network, and an order system of voice work is established for the substation inspection work order scenario, with three different speech characteristics and a hidden Markov model. A dataset of 4500 audio segments was established by different accents and environments in each region, then evaluating the feasibility of the system model. Finally, in the speech recognition experiment, the error rate of speech recognition words is controlled at 10%, and the sentence error rate is below 7%. Meanwhile, the speech input speed reaches 400 words per minute, and the response time of the recognition results is less than 200 ms. In voiceprint recognition, the recognition accuracy reaches 98% based on the voice characteristics of users, which verifies the rationality and feasibility of the proposed method compared with traditional CNN, and improves the work efficiency of inspection personnel.
Classification of Authentication Approaches to Stop the Next Breaking: Challenges, Benefits, Drawbacks, Awareness, and Recommendations

These days, the scope of a huge number of web, desktop, IoT, cloud and mobile applications being compromised is rapidly increasing. The attackers exploit weak authentication techniques to compromise government, educational, financial, and healthcare systems to access sensitive information. Authentication failure is very serious according to Open Web Application Security Project (OWASP) organization. This organization has ranked authentication failure among the top ten risks in the world in 2021 and 2023. In this study, we discuss the authentication technologies in terms of advantages, disadvantages, and drawbacks of some common authentication methods. We provide a comprehensive comparison table between several types of authentication techniques. Furthermore, we provide important recommendations for the future direction. Additionally, we provide a list of crucial factors for decision-makers to consider when selecting the appropriate authentication type. This research serves to raise awareness about the dangers of vulnerable authentication, benefiting users, companies, and researchers.
Revolutionizing Indonesian Farming Application through Knowledge-Sharing and Collaboration

Ensuring the stability and food security of a nation heavily relies on the crucial role played by the agricultural sector. Indonesia’s agricultural sector is confronted with the difficulty of coping with a significant influx of imported goods into the country. To address the issue of the decreasing number of farmers in Indonesia, it is necessary to generate the interest of the younger generation in pursuing a career in agriculture. An online discussion forum created specifically for Indonesian farmers to raise concerns and seek solutions related to the challenges they face while working in the agricultural sector. The platform offers features such as asking and answering questions, saving questions, and giving likes or dislikes. Through these features, users can connect and collaborate with each other, improving their productivity and livelihoods. This application was built using flask and bootstrap platform with Python and SQLite as the programming language and database connection. As system evaluation results, eight golden rules concept implemented to ensure the user interface is easy to use, effective and meets user needs. In the development of this website, black box testing is used to evaluate the functionality of each feature.
Transforming Ideas into Code: A Novel Approach to Design-to-Development Workflow

In the software industry, the design and development of digital applications involve various stakeholders, such as designers and developers, working in teams. Designers are responsible for delivering designs that meet specific user experience (UX) and user interface (UI) standards. Developers transform these designs into functional code, producing digital applications that meet requirements. A challenge in this process is the increased time and effort needed, as the transition is typically based on non-automatic processes. This paper presents the GUIRA methodology, aiming to streamline this transition, adopting automatic techniques accompanied by insights from a small-scale user study. The results showed that following GUIRA can save the development process time while maintaining a high code quality.
Unsupervised Anomaly Detection for Throughput Enhancement in Mobile Networks

Mobile Networks (MNs) are rapidly evolving, enabling seamless and faster communication around the world. However, the escalating complexity and management challenges of modern MNs, have become more pronounced, especially with the advent of 5G technology. User throughput is a pivotal determinant of Quality of Service (QoS) in MNs, influenced by many dynamic factors in such environments. Conventional monitoring tools often encounter limitations in comprehensively capturing the evolving nature of these networks, impeding the effective identification of some throughput constraints. To address this challenge, this work introduces a data-driven approach for unsupervised anomaly detection in MNs, specifically tailored for detecting anomalous throughput cells. This involves developing a scoring methodology leveraging Performance Management (PM) data. The proposed method integrates Machine Learning (ML), eXplainable Artificial Intelligence (XAI), and statistical analysis to evaluate the extent to which each cell’s user throughput aligns with the expected performance for its conditions, relative to comparable peers. This enables the identification and scoring of local anomalies where throughput falls relatively below expectations. Validation of this approach on a real-world dataset has demonstrated its effectiveness in identifying cells with the potential for throughput enhancement. This can help Mobile Network Operators (MNOs) improve operational efficiency and provide a better customer experience.
Review of Integrated STEM+C e-Learning Platforms to Support Underrepresented Students

Despite initiatives to promote broader participation within the workforce, the science, technology, engineering, mathematics, and computer science (STEM+C) fields still lack diversity. The underrepresentation of historically marginalized students in STEM+C fields may be caused by several factors. Such factors may include but are not limited to, a lack of exposure in their early childhood education, lack of confidence, and gender stereotypes that are perpetuated by society. While a systemic issue, the availability of e-learning platforms has the potential to provide low-stakes opportunities for historically marginalized students in STEM+C to explore subject areas and develop an interest. There is also the added benefit that such platforms are highly scalable. This paper offers a review and synthesis of the effectiveness of current e-learning platforms for promoting historically marginalized students’ participation in STEM+C focusing specifically on female students’ participation and e-learning platforms that teach STEM+C content. The benefits of such platforms, along with the limitations (e.g., cost, learner motivation, lack of social interaction and technical skills, etc.) are taken into consideration. However, based on the findings described in this review paper, such limitations ultimately do not undermine the benefits e-learning platforms may have in supporting students’ interest in pursuing STEM+C and promoting broader participation.
The Research on Server Signal Light Image Recognition Algorithm Based on SPD_YOLOV5

For the detection of server signal lights in data centers, challenges arise due to the small size of the lights and various factors such as visual quality, diversity, occlusion, and interference, which can affect the accuracy of signal light detection. Therefore, this paper proposes an improved approach for object detection based on YOLOv5 to enhance the accuracy of signal light detection. The proposed method includes the addition of a small object prediction layer on top of the original network and the utilization of SPDConv as a novel CNN building block, which effectively extracts features from low-resolution images and small objects. Furthermore, the loss function is optimized by replacing CIoU with SIoU, reducing degrees of freedom and improving the robustness of the network. Experimental results demonstrate significant performance improvements in the detection and recognition of server signal lights achieved by the improved YOLOv5 approach.