Reviewerly/exHarmony huggingface dataset:
Maritime low-light image enhancement study based on improved ResUnet

Recent years, both the domestic and international frontiers made headway in the realm of intelligent navigation for ships, so vision-based ship monitoring systems and smart ships had a great development prospect. However, in the night-time environment at sea, the images usually have low contrast, low visibility and generous noise splash. Deep learning algorithms are affected by these problems, which not only limit the detecting and recognizing capability but also bring the unstable elements in the aspect of safety. To solve these problems, this study constructs paired Maritime-Low-Light datasets and build the MLL-ResUnet (Maritime low-light-ResUnet) model based on the ResUnet model to enhance nighttime images at sea. The CurveNLU module is designed, which adjusts the image brightness curve to make an adaptive adjustment of the image brightness for achieving the image enhancement tasks. Lastly, to consider the image output effect and model light-weighting, we introduce the ORSnet (Original Resolution Subnetwork) module and the GhostConv module. In the experimental part, the MML dataset is applied in the several other state-of-the-art image enhancement algorithms and the proposed algorithm. Based on the compared results, we find that the model proposed achieves impressive results. Specifically, it scores 26.865 and 0.84 in the PSNR and SSMI quality evaluation indexes, surpassing the performance of other algorithms. There is strong evidence to support that the proposed model not only ensures the authenticity of the output image, but also has a certain guarantee for the speed of calculation. Overall, the MLL-ResUnet model meets the requirements of the ship monitoring system and enables real-time monitoring of ship movement at night at sea.
Modulation recognition technology based on ResNet50

Modulation recognition technology belongs to one of the cognitive radio direction technologies in the field of communication, which is widely used in military and civilian use. This paper proposes a ResNet50-based modulation recognition algorithm. Compared with the matrix of most direct input signals, this paper innovatively transforms the signal through STFT pre-processing into time-frequency map [11] and then input it. The last three layers of the ResNet50 network are modified, changing the 1000 signal categories of the original model to only 9 categories, while the other modules remain unchanged. At the same time, transfer learning is introduced, so that the network is only trained on the last three layers, and good recognition performance can be obtained in small-scale data.
Research on path planning for UAV based on improved ant colony algorithm

Abstract: The field of unmanned aerial vehicle (UAV) research places significant emphasis on path planning, especially intelligent optimization method. In order to address issues with classical ant colony optimization (ACO) algorithm, such as its tendency to become stuck in local optima and limited search capabilities, this paper proposes a novel algorithm, IACO, which improves upon the typical ACO algorithm. This algorithm is designed for static hilly environments and incorporates a comprehensive heuristic function along with self-updating rules. Referred to A* algorithm, IACO utilizes a heuristic function that considers the position of initial and target nodes respectively, using Manhattan distance to simplify calculation compared with Euclidean distance. On the process of global updating of pheromone, IACO improves the accumulation by considering the average length of path and the number of iterations, while also taking into account the worst path during pheromone volatilization. Additionally, the decay parameter is replaced with the normal distribution that is related to the number of iterations. The elitist strategy is also used to prevent local optima and accelerate iteration rate. Simulation experiments show that the entire path is above the hill, enabling the UAV to fly at a safe distance. IACO significantly reduces path length by 24.69% and iteration time by 20.32%, which has been demonstrated its superiority over ACO in terms of optimization effectiveness and search efficiency.
Human posture estimation at low resolution

Human pose estimation is an important research direction in the field of computer vision, which is widely used in human-computer interaction, behavior analysis, and intelligent surveillance. Although existing human pose estimation algorithms possess high accuracy for high-resolution images, they perform poorly for low-resolution images that are prevalent in practical applications, and thus are difficult to be widely used in people's daily lives. In this paper, we propose a new end-to-end network framework for accurate human pose estimation of low-resolution images by combining super-resolution assistance and quantization error optimization. In addition, a composite loss function is designed to jointly train the super-resolution network to generate high-resolution images that contribute to human pose estimation instead of simple pre-processing. The experimental results show that the mAP of our method reaches 68.1% and 61.4% on the COCO datasets downsampled to 128×96 and 64×48.
Research on Cable Tunnel Image Defogging Based on Optimized Contrast Enhancement

With the progress of economy and science and technology in recent years, overhead lines in transmission lines have gradually been replaced by high-voltage cables. Because the cable tunnel is an underground closed equipment, the staff can not intuitively observe the situation in the cable tunnel in real time, and can only rely on the cable tunnel image transmitted by the monitoring system in real time for online monitoring, and the environment in the cable tunnel is complex, the air is not circulating, and it is very easy to produce fog, which is extremely unfavorable to the image transmitted by the monitoring system in real time. In this paper, the contrast is optimized by the method of image enhancement to achieve the purpose of dehazing the cable tunnel image and make the image clearer.
Adversarial Training Enhanced Multi-Channel Graph Convolutional Network for Aspect Sentiment Triplet Extraction

The aim of this article is to propose an improved method for the ASTE task through the use of EMC-GCN and adversarial training. The authors suggest that by improving the robustness and accuracy of the EMC-GCN model, the overall performance on the task can be optimized. The article presents a series of experiments that validate the effectiveness of the proposed method. Specifically, the authors demonstrate that the use of adversarial training can optimize the loss function of the model, leading to significant improvements on the recall rate. Ultimately, this results in a more optimized and effective model that can achieve better performance on the comprehensive index F1 across multiple datasets. Overall, the proposed method provides a promising approach to improving the ASTE task through the use of EMC-GCN and adversarial training. By optimizing the performance of the model, this approach has the potential to facilitate more effective sentiment analysis and enhance our ability to understand and interpret complex linguistic data.
Accurate Stereo Matching Based on 3D Labels in Driving Scenes

We present a novel global stereo matching approach aimed at achieving accurate and smooth 3D label disparity estimation based on LocalExp. This innovative scheme can effectively assign an appropriate 3D label to each pixel from an infinite label space and generate a continuous disparity map with subpixel accuracy. To address the persistent challenge of weak texture, our method adopts a novel approach by utilizing calculated initial labels rather than random labels, which yields enhanced disparity maps. The process involves employing grid cells of the same size as the original image pair, allowing the reduced image pair to capture more detailed information in local regions. After enlarging the disparity map of reduced image pair and replacing the random initial labels with the refined ones, we achieve superior results in our method. Experimental results demonstrate the high matching accuracy of our method in driving scenes, and its generalizability is verified.
Improved YOLO-Pose Crowd Pose Estimation

Aiming at the problem of low detection and recognition rate caused by crowded occlusion or indistinct difference between front and back backgrounds when performing human pose estimation in crowded scenes, We proposed an improved YOLO-Pose algorithm that combines convolutional block attention modules. By Integrate CBAM to Bottleneck module to dig deep into the feature information of the two levels of channel and space, and enhance the feature extraction of the target object in the image. In order to improve the convergence ability of the model, improve the CIOU loss function, use EIOU instead of the original regression function of YOLOv8, optimize the regression accuracy of anchor points, reduce the difficulty of network training, and improve the detection rate of occlusion. Compared with the general YOLOv8 algorithm paper, the improved algorithm proposed in this paper has a higher accuracy rate when performing pose estimation in crowded scenes.
Information Support for Postwar Economy Recovery of Ukraine

This research focuses on information support for Postwar Economy Recovery of Ukraine. The article analyzes the dynamics of the balance of payments of Ukraine and describes push factor that would face country with dual challenge. Research estimates the total damage of hostilities on the territory of Ukrainian regions by type of property and the destruction of environmental. Article analyses the total damage assessment in monetary terms by regression summary direct damage assessment for dependent variable “total volume for industry recovery’’. In research evaluates the total bilateral financial aid commitments to Ukraine, assets, other funds and investments that should be used for post-war reconstruction of Ukraine. In article it is developed the basic principles of sustainable and systemic solutions into reconstruction of Ukraine as information support for green post-war reconstruction of the country and plan for returning life to the previously occupied territories.
Automatic Guided Vehicle Path Planning Based on Artificial Potential Field - Beetle Antennae Search Algorithm

The conventional potential field method has the problem that easily falling into local minima, in order to overcome this defect, this paper proposes an artificial potential field-beetle antennae search algorithm, so that the automatic guided vehicle can escape from the local minimum and reach the destination. In addition, the initial path is fitted using Bessel curves to improve the smoothness and reduce the number of turns. At last, in order to verify the performance of the improved algorithm, experiments are run in MATLAB to compare it with the RRT algorithm and RRT-Connect algorithm. The results show that the improved algorithm has better performance than the above algorithms in the same experimental environment.
English to Kannada Translation Using BERT Model

Translation of content on the internet from one language to the other is essential for conveying information to the people around the world. This can be text, audio or video-based information. Today, the BERT model which stands for Bidirectional Encoder Representations from Transformers helps computers understand the meaning of ambiguous language in text by using surrounding text to establish context. In this paper, an English to Kannada translation model using the pre-trained uncased BERT framework has been presented. Low-resource language like Kannada requires a vocabulary creation and an appropriate preprocessing. WordPiece and SentencePiece tokenizers have been used for appropriately clean and prepare the data for model building. Uncased pre-trained BERT model is used as an encoder for model building and the FairSeq algorithm is used for decoding. The model is trained and tested on Samanantar dataset consisting of 6 lakhs of records. The proposed custom model has outperformed for about 29% compared to multilingual BERT model and it also outperformed ordinary transformer model by 50%. These findings have proved that custom language models perform better in case of Indic languages, especially the Dravidian languages like Kannada.
Research on Human Motion Recognition in Sports Training Based on Deep Learning

Action recognition is a fundamental technology for health monitoring, human-computer interaction, robotics, and many other applications, and has always received widespread attention from researchers. In action recognition, it is very important to fully obtain and efficiently utilize feature information from multimodal action data, which is conducive to applying the model to practical life.The global average pooling layer and full connection layer are used to generate class labels with standard cross entropy loss. This article utilizes an extremely lightweight neural network model that combines skeleton joint distance features with three scale motion information to efficiently achieve skeleton based action recognition. The proposed model was experimentally compared on three datasets: SHREC, JHMDB, and NTU-RGBD. The experimental results proved the effectiveness of the model, with an accuracy of 80.2%.The human motion recognition technology in this paper is beneficial to the recognition of training movements and the effect of exercise.
Graph Structure-Based Implicit Risk Reasoning for Long-Tail Scenarios of Automated Driving

With the development of Artificial Intelligence (AI) technology, autonomous vehicles (AVs) have entered the general public's view, however, the challenges brought by "long-tail" scenarios are still faced in the real driving process. Since long-tail scenarios rarely exist in the training dataset, the current data-driven autonomous driving system is hard to handle the implicit risk of these scenarios, which seriously affects the safety of driving. To alleviate this situation, A reasoning methodology is proposed to recognize implicit risk supporting making safety decisions in long-tail scenarios. The method firstly constructs the knowledge leading to long-tailed risks into a knowledge graph structure with spatial relations, and manages the knowledge graph based on GloVe (Global Vectors for Word Representation) word vector embedding technique with hash table structure. Following, by designing a real-time scenario topology graph generation model, this work computes the similarity between real-time topology graph and knowledge map through graph structure and spatial relationship, to obtain the probability of occurrence of long-tailed risk. Finally, the feasibility of the solution is verified in Carla simulation environment by a long-tail scenario example, which indicates the approach succeeds in identifying the implicit risk. This research provides a path for enhancing risk management and safety decision making in autonomous driving, especially in addressing the challenges of long-tail scenarios.
Automatic IQ Estimation from Written text using Stylometry Methods

Stylometry is the process of making estimations or predictions (e.g. Gender, Language of Origin, true author) based on the analysis of writing styles. The task of quantifying a person's intelligence has been a goal of psychologists from before the beginning of the century. The area of estimating IQ using Stylometry has been a developing area of research and the effectiveness of using Machine Learning and Stylometric Analysis for the estimation of IQ has been demonstrated in literature whose conclusions suggest that using a large dataset and further research in the field could improve the quality of estimation. The focus of this research is to propose a method that can be applied on a large dataset to estimate the IQ of an individual. The results obtained showed that our method performs better than existing methods when applied on a large dataset
BERT for Sentiment Analysis on Rotten Tomatoes Reviews

Transformer is a neural network model whose trend is rising because ChatGPT shocked the world with its general answering and questioning system capabilities. The bidirectional encoder representation transformer (BERT) is also one of the other pre-trained transformer models. However, there are research opportunities to use BERT in sentiment analysis for the Rotten Tomatoes review. Our research aims to use BERT for sentiment analysis on the Rotten Tomatoes dataset and evaluate the results. Our research step is to get the Rotten Tomatoes dataset from the Huggingface dataset. We do pre-processing for the BERT model, tune the BERT model, then train the model using the dataset. We use the lightweight pre-trained DistilBERT as part of our proposed model. We compare the performance of our BERT model with three state-of-the-art benchmark methods: support vector machine with term frequency-inverse document frequency (SVM+TF-IDF), naïve Bayes with TF-IDF (NB+TF-IDF), and convolutional neural network (CNN). We use several test metrics such as accuracy, precision, recall, f1-score, and area under the curve (AUC) from the receiver operating curve (ROC). In the training optimization process, we learned that AdamW is more effective than Adam optimizer. Our test results show that BERT’s performance is better than the other three models with Accuracy, Precision, Recall, and F1-Scores of 0.753, 0.754, 0.754, and 0.753, respectively. Finally, BERT is also the best in terms of AUC, with a value of 0.826.
Collaborative Filtering Movie Recommendation System using K-means Clustering with Particle Swarm Optimization

Movies are one of the forms of entertainment that many people are interested in. With the availability of streaming platforms, users can now conveniently enjoy movies anytime and anywhere. In addition, thanks to the recommendation system on the platform, it is easier for users to find films that suit their interests. Collaborative filtering is a commonly employed paradigm in recommendation systems. However, this paradigm has many challenges, one of which is scalability. Therefore, this research proposes a collaborative filtering method by implementing film groupings based on genre. The clustering process in this study was carried out utilizing the K-means algorithm. The Particle Swarm optimization Algorithm is employed to facilitate the optimization of the determination of the initial centroid in K-Means. The PSO-KM method is subjected to a comparative analysis with the pre-existing system that has been deployed using the Movielens dataset. The experimental findings indicate that the method put forth exhibits greater optimality in terms of precision and recall when compared to the previous method. However, MAE only produces better values in just a few K values. Furthermore, our proposed system achieves promising results regarding scalability and provides accurate personalized movie recommendations while mitigating the common issue of a cold start problem. These findings indicate that grouping items using K-means integrated with PSO produces effective results in a recommender system.
Ensuring Success in Quick Commerce by Evaluating Its Application Development Capability Maturity Using COBIT 2019

In the digital era, the demand for quick access to goods (q-Commerce) has driven retail companies to develop online shopping applications, aiming to attract more consumers. To cater to the demands of q-Commerce, specialized mobile applications that prioritize fast delivery and user-friendly experiences are crucial. However, developing and maintaining these applications present challenges in terms of speed of development and security. This research focuses on a retail company, PT X, with a q-Commerce application serving 3.5 million active users monthly. The research aims to assess PT X's capability maturity level in application development governance using the COBIT 2019 Capability Maturity Model. Additionally, the study investigates the differences in assessment methods between COBIT 2019 and previous versions. The findings contribute to understanding in which level of capability maturity that companies can achieve in developing quick commerce applications with a large user base. Findings of this research provides an overview for PT X to initiate the necessary initial steps to comply with the good application development according to COBIT 2019 standard. This research also highlights to future researchers that COBIT 2019 is primarily a framework for enterprise-level assessment and need to be adjusted to be suitable for measurements below the enterprise level.
Real-Time Ocean Waves Three-Dimensional Reconstruction Research Based on Deep Learning

Obtaining precise space-time data is crucial for marine research on wave fields. Stereo vision techniques, which can generate high-resolution three-dimensional (3D) point clouds, have gained popularity for reconstructing small to medium-scale wave fields. However, accurate reconstruction often requires extensive computation time, limiting the applicability of stereo-based analysis approaches in wave forecasting and offshore engineering design. This paper introduces a real-time 3D reconstruction algorithm for ocean wave sequences based on deep learning. Firstly, we incorporate the prior knowledge of disparity common area during the network model training, to enhance the quality of the resulting disparity maps. Then, instead of employing complex point cloud filtering procedures, the disparity masks are leveraged to eliminate outliers and reduce processing time. Finally, we evaluate our algorithm on the Acqua Alta dataset and compare it with the WASS algorithm. The results demonstrate the superior reconstruction effectiveness of our method, with a 75.03% improvement in effective wave height estimation accuracy and a 50.00% improvement in peak wave period estimation accuracy. Additionally, the processing speed of our algorithm remains at 40 frames per second (fps).
Indonesian Headline Detection on Twitter Social Media

Social media post such as tweet in Twitter is one of main source of text analysis. However, the large amount of data collected often contain noise, such as irrelevance post that affect system inaccuracy. One that may affect system inaccuracy is the abundance of headlines text. Headlines text is a part of the text that describes the text below it. Therefore, this study designed an effective method to collect text headlines and non-headline texts, as well as building a model to detect text headlines. The data collection method developed had low error rates, making it a good reference for obtaining text headlines and non-headline text. The model built to detect text headlines had good performance with the highest accuracy is achieved by the IndoBERTweet model at 0.9921. The CNN and fastText models built had lower accuracy of 0.9081 and 0.8793 respectively, but were 200-2000x faster than IndoBERTweet. Moreover, our findings revealed that more than half of the total data in the four COVID-19 related topics comprised text headlines. Furthermore, the observed errors in the studied models can be attributed to the complex intrinsic properties of a headline text, fastText limitations in representing non-standardized words, and the training data used to build the fastText model.