Reviewerly/exHarmony huggingface dataset:
Three-Dimensional Object Segmentation Method based on YOLO, SAM, and NeRF

The neural radiance field (NeRF) representation has shown promising results in capturing 3D scenes. However, when it comes to editing or moving specific objects within virtual environments, it is essential to segment individual objects from the neural radiance field. Existing methods primarily rely on feature computation and clustering for 3D segmentation, but they often suffer from poor segmentation quality and low accuracy. Taking advantage of recent advancements in 2D image segmentation, we propose a 3D segmentation method that generates single-object segmentation from multiple viewpoint images. First, we compute camera poses using feature points from the original images. Then, with user guidance, we use the You Only Look Once (YOLO) network model to roughly locate the objects and utilize the resulting information as hints. Subsequently, we employ the Segment Anything Model (SAM) for fine-grained segmentation. The processed dataset is then rendered into a 3D segmented object using the neural radiance field, producing high-quality rendered images. By training on specific objects, we can segment object categories that the publicly available YOLO model cannot recognize, thereby enhancing generalization. Through experiments, we analyze the real-time performance of each module and qualitatively and quantitatively evaluate the effectiveness of our method. Compared to the N3F and ISRF methods, our approach achieves an average 17% improvement in Intersection over Union (IoU) and an average 9% improvement in Pixel Accuracy (Acc). We validate the performance of our method on the LLFF, mip-Nerf360 datasets, demonstrating its generalizability and effectiveness.
A Security Feature Extraction Method for RF Amplifier Module of 5G Base Station

With the increasing popularity of 5G, 5G base stations are facing an increasing number of security threats, and their security status characterization has become a current research hotspot. This paper studies the security feature extraction for RF amplifier modules of 5G base station, providing data support for the security status perception and prediction for RF amplification module of 5G base station,which is one of the core functional modules of 5G base station. A parameter selection method based on correlated information entropy measurement and a feature extraction method based on sparse preserving projection are realized. On this basis, the security sensitive parameter selection set of the RF amplifier module is constructed, and the security features of the RF amplifier module are extracted. The results show that the security feature extraction method for RF amplifier module of 5G base station proposed in this paper can efficiently characterize the security status of 5G base station RF amplifier module.
Multi-food source differential salp swarm algorithm

Salp swarm algorithm (SSA) divides the population into leaders and followers, and employs two different search methods to achieve steady convergence. However, the search methods adopted by SSA are relatively simple, hence SSA always trapped by local optima on complex multimodal problems. To improve the exploration of SSA, this paper proposes a multi-food source differential salp swarm algorithm (MFD-SSA) by transferring multi-leader mechanism and differential mutation strategy into SSA. MFD-SSA regards a portion of high fitness individuals as food sources and employ them for updating the position of leaders, the differential evolution mutation and the chaotic crossover are adopted to enhance swarm diversity. Then, the differential mutation and crossover operator are employed for enlarging search range. The MFD-SSA is tested on CEC2017 test suite and compared with five SSA variants and five other meta-heuristics. The test results indicate that MFD-SSA bears strong exploration, it outperforms five SSA variants and five meta-heuristics. On Planetary gear train design problem, MFD-SSA performs better than other five SSA variants too.
SVM in Classification of stage 0~II and III~IV with Breast Cancer : A Retrospective Cohort Study on a bicentric cohort

Objective: The objective is to develop a predictive model utilizing Support Vector Machines (SVM) for the purpose of classifying the clinical stage of breast cancer.
Coherent light scattering from a telecom C-band quantum dot

Abstract Quantum networks have the potential to transform secure communication via quantum key distribution and enable novel concepts in distributed quantum computing and sensing. Coherent quantum light generation at telecom wavelengths is fundamental for fibre-based network implementations, but Fourier-limited emission and subnatural linewidth photons have so far only been reported from systems operating in the visible to near-infrared wavelength range. Here, we use InAs/InP quantum dots to demonstrate photons with coherence times much longer than the Fourier limit at telecom wavelength via elastic scattering of excitation laser photons. Further, we show that even the inelastically scattered photons have coherence times within the error bars of the Fourier limit. Finally, we make direct use of the minimal attenuation in fibre for these photons by measuring two-photon interference after 25 km of fibre, demonstrating finite interference visibility for photons emitted about 100,000 excitation cycles apart.
Research on Text Emotion Analysis Based on LSTM

Sentiment analysis of text based on deep learning has become an active research direction in the field of natural language processing, where the sentiment analysis of text is used to automatically identify the sentiment tendency embedded in the text, such as positive or negative. Sentiment analysis based on deep learning usually adopts neural network models, which are trained on a large amount of labelled data to capture the sentiment information in the text. The commonly used neural network models include Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and so on. In this study, Long Short-Term Memory (LSTM) is used to build a model to construct a text sentiment analyzer, which, after testing 23982 textual data, can predict whether the sentiment expressed is negative or positive according to the input statements, and the accuracy of its prediction results can reach 90.37%. The experimental results show that it is more flexible and generalizable than the traditional rule-based or feature engineering methods, and can further improve the performance of sentiment analysis.
Intelligent Composition Based on Fibonacci Sequence and Hidden Markov Model

Recent advances in artificial intelligence architectures have provided templates for automated composition in music generation. However, these automated composition techniques often suffer from a lack of emotional expression and structural coherence. This paper proposes a method for the intelligent generation of music melodies in different styles and modes based on the Fibonacci sequence. Specifically, the pitch frequency of musical notes is quantized using A440Hz, and the direction of melody development is represented by chords processed with the Fibonacci transformation matrix. To meet the predefined requirements for style and mode, the Hidden Markov Model and the Viterbi algorithm are employed to find the optimal solution. Using the example of C major and dominant seventh chords, this paper first generates a state transition matrix by calculating the distances between chords before and after applying the Fibonacci transformation matrix. Then, an observation matrix is constructed based on the probabilities of chords derived from a collection of works in C major by Haydn and Jazz compositions. Finally, the Viterbi algorithm is applied to find the music melody segment that corresponds to the maximum probability. The results demonstrate that the intelligent composition based on the Fibonacci sequence exhibits originality, orderliness, and aesthetic value. The practical application of the algorithm validates its applicability and accuracy, providing an effective method for the advancement of intelligent composition.
Predicting Code Runtime Complexity Using ML Techniques

There are several approaches to solving every coding algorithm in Computer Science. To achieve the same result, these methods may use various techniques and reasoning. The difficulty is that as the number of inputs increases, certain algorithms tend to perform poorly. Several metrics may be used to assess the quality of any code. The code runtime complexity is one of these measurements. To determine this runtime complexity, substantial study and a thorough understanding of algorithms are necessary, which is a challenging manual undertaking. In this study, the worst-case runtime complexity of codes in programming languages C, Java and Python are calculated as Big-O notations utilising code features like Abstract Syntax Trees, ML approaches and static code analysis. The novelty of the research is our labelled runtime complexity dataset which was constructed manually, implementing Deep Learning Algorithms like Bi-LSTM and calculating Code Runtime Complexity for the worst-case scenario as Big-O notations for codes in three languages, C, Java and Python. To predict the runtime complexity for a given code more accurately than the traditional legacy methods such as manually asserting code runtime complexity or running code for different amounts of input, we have presented a more effective manner for the same. The results portray that the XGBoost classifier outperforms the other models with an accuracy of 96%. The current study can also be extended to other high-level programming languages, including more training samples and making use of graph neural networks.
Denoising of Synthetic Aperture Radar Images Using Dual Tree Curved Wavelet Transform with Modified Cellular Neural Networks

Synthetic aperture radar, sometimes known simply as SAR, is a technique for imaging a target from space that uses microwave radiations to illuminate the object that is the focus of the picture. These brief bursts of microwave radiation are sent with the help of a piece of equipment known as RADAR. Denoising is an important pre-treatment step in image processing that has to be finished before an application-friendly picture can be produced. This step is necessary to create an image. They are versatile enough to be used for everything from digital photography to photographing satellites. Their range of applications is rather extensive. The imaging method known as synthetic aperture radar may be used in any weather, both during the day and at night, and it uses airborne radar to illuminate the earth’s surface. Satellites inspired the development of this particular technology. Processing must first be performed on the recorded backscattered signals before synthetic aperture radar images can be produced using them. Because microwaves can travel through clouds and dirt, they are often used in settings where traditional imaging would be difficult. Speckle noise is a natural occurrence that may be observed as granular patterns in synthetic aperture radar images. These images are produced by synthetic aperture radar (SAR) technology. It could be difficult to eliminate speckles, a random multiplicative noise. Speckle is a sort of noise. Speckle is a kind of noise that occurs in coherent systems and is formed when echoes interact with transmitted signals. This interaction may result in either constructive or destructive interference. The appearance of the speckle lowers the overall image quality, which means that the application cannot use it since it is improper. Denoising SAR pictures is a tough approach, but it is important since there is noise in the images and it has to be removed. Denoising is a strategy that should be used to eliminate the noise, but all of the essential visual information should be preserved. Denoising may be done in either the spatial domain or the transform domain, both of which are acceptable choices. Combining the dual tree curved wavelet transform with modified cellular neural networks is said to produce a one-of-a-kind filter, which has both been hypothesized and produced as a possibility (DTCWT-MCNN). The recommended filter is implemented using SAR images and put through its paces to see how well it works. The recommended approach is given a score based on its efficacy, which is determined by utilizing objective metrics, and its performance is analysed to see how effectively it functions. Performance evaluation metrics such as Noise Mean Value (NMV), Mean Square Difference (MSD), Equal Number of Looks (ENL), Noise Standard Deviation (NSD), and Speckle Suppression Index were utilized to carry out quantitative confirmation of the findings. These metrics were utilized to confirm the findings (SSI).
Sentiment Exploring on Feedback of E-commerce Data Using Machine Learning Algorithms

In today’s fast-growing Internet world, customer ratings and reviews play an essential role in online buying on e-commerce websites such as Amazon, Flipkart, and others. Sentiment analysis is crucial for increasing customer satisfaction on e-commerce sites since it contains a lot of consumer feedback. In this work, we have used Amazon Women's E-Commerce Clothing Reviews dataset. We have used CountVectorizer and TF-IDF and trained the data on five machine learning (ML) classifiers, namely logistic regression (LR), multinomial Naive Bayes (MNB), Bernoulli Naive Bayes (BNB), support vector machine (SVM), random forest (RF), and AdaBoosting (AB). When comparing the ML model’s accuracy scores concerning the CountVectorizer, it was discovered that MNB and LR models had the highest accuracy of 0.94, while RF had the lowest accuracy of 0.90. SVM achieved the maximum accuracy of 0.94 using the TF-IDF approach, and MNB achieved the lowest accuracy of 0.89. The accuracy, precision, recall, F1-score, and AUC-ROC curve help us to determine the performance of the ML algorithms. To examine the dataset’s attributes and comprehend the relationships between the variables, many statistical techniques were applied.
Special Issue NL4AI 2022: Workshop on natural language for artificial intelligence

The 2022 edition of NL4AI was co-located with the 21st International Conference of the Italian Association for Artificial Intelligence (AIxIA 2022) and took place on November 30th in Udine, Italy. The call for papers attracted 17 submissions by 52 different authors from Italy (44), the UK (2), Algeria (4), and Germany (2). After the review process, 13 of 17 papers were accepted for publication (acceptance rate 76.47%). In terms of topics, the contributions to the workshop span from pure NLP works to broader proposals bridging NLP with other AI applications. Among the accepted articles, we selected two that we considered the most relevant and inspiring for the Italian Natural Language Processing and Artificial Intelligence communities. The authors of these papers have been invited to extend their contribution to this volume, creating an exclusive venue to provide visibility to their egregious work.
Semi-supervised fuzzy C means based on membership integration mechanism and its application in brain infarction lesion segmentation in DWI images

In this paper, a novel semi-supervised fuzzy clustering algorithm, MFM-SFCM, based on a membership fusion mechanism is proposed for Diffusion-weighted imaging (DWI) brain infarction lesion segmentation. The proposed MFM-SFCM algorithm addresses the issue of weakened constraints and insufficient influence of labeled samples on the clustering process that arises in the semi-supervised fuzzy C-means clustering (SFCM) when emphasizing supervised information. By using a new membership fusion mechanism, MFM-SFCM eliminates this issue, greatly improving the accuracy of clustering results and accelerating convergence speed. This allows fuzzy clustering to achieve good results in the segmentation of DWI brain infarction lesions using a small amount of labeled information. The effectiveness of the MFM-SFCM algorithm is demonstrated through experiments conducted on a real-world dataset of DWI brain images.
A Hybrid Approach for Classification of Text Documents Using Naïve Bayes and Instance-Based Learning

In this paper, an enhanced classification model using Naive Bayes with various similarity measures is proposed. In this work, the Bayes formula is used to vectorize a text document such that the vector gives the probability distribution of a document with respect to each predetermined probable category to which the document may belong to. The probability distribution may assign the document to any of the topics, for example, the ones that are found in ‘mini-20-newsgroup’. The proposed approach can be used on any document collections and will show an improved classification accuracy thereby competing with some of the well-known classifiers such as SVM.
Hybrid fuzzy response threshold-based distributed task allocation in heterogeneous multi-robot environment

Task allocation is a vital challenge in a multi-robot environment. A hybrid fuzzy response threshold-based method is proposed to address the problem of task allocation in a heterogeneous mobile robot environment. The method follows a distributed task allocation approach where every robot chooses its task and performs it, resulting in concurrent execution. The algorithm uses a fuzzy inference system to determine the capability of the robot to carry out a task. Then, the robot employs the response threshold model, utilizing the obtained capability to decide on the task to complete. The objective here is to maximize the tasks completed with the resources available while balancing the affinity with which the task is done. The proposed algorithm is initially applied to the static scenario where there is no failure among the mobile robots. The algorithm is then improved to run in the dynamic scenario to study the effect on the allocation. The proposed algorithm is empirically evaluated in simulation for multiple runs under different environment instances. The results show a good increase in tasks performed successfully across all the instances in static and dynamic scenarios. The proposed algorithms are validated using FireBird V mobile robots in an experimental environment.
Fake Currency Identification System Using Convolutional Neural Network

Fake currency is a serious problem in India, and detecting it is crucial to maintain the integrity of the country's currency. There are various software programs available in India for detecting fake currency. These software programs use image processing techniques to analyze images of currency notes and identify any irregularities that indicate counterfeit notes. Existing methods for detecting counterfeit notes rely primarily on image processing techniques. A web-based detection system provides an easy-to-use interface for users to upload images of currency notes, send them to the CNN model (Venkata Raghu et al. in Int J Creat Res Thoughts (IJCRT) 10:2320–2882, 2004), and view the analysis results. Convolutional Neural Networks (CNNs) (Pallavi et al. in Int Res J Modernizat Eng Technol Sci 50:4076–4081, 2002) are used in the detection of counterfeit currency by analyzing the security features of currency notes and learning to differentiate between genuine and counterfeit notes based on those features. Using a real-time camera view (Selvi Rajendran and Anithaashri in IOP Conf Ser Mater Sci Eng 992:01201, 2020), this study will identify Indian banknotes by extracting features from notes, the model can identify counterfeit money. The model is trained with 80–20% training and test split, with each layer receiving the same learning rate of 0.001. After training the network for 200 cycles with 306 images, the training accuracy score 90.6%.
Sentimental Analysis of COVID-19 Twitter Data Using Machine Learning

Corona virus is considered as a scourge all over world by which most of the people got infected. Since the virus was spreading very quickly governments had to bring in very tough rules such as lock down. Lock down disrupted almost all sector like education, business etc.… During lock down and after the lock down people have used social media as their platform to express the opining regarding the pandemic. Tweeter is one such social media where people had shared their thoughts on the pandemic. In this study tweets which are made by the public is considered to analyses the sentiments. And in this research machine learning models are used to classify the data into five different classes namely positive, negative, neutral, extremely positive, and extremely negative. Decision Tree, Random Forest, Support Vector Machine, Logistic Regression, and Naïve Bayes are the ML models used to classify the sentiment and are achieving on accuracy of 99%, 99%, 97%, 94%, and 73%, respectively. Further precision, recall, F1-score, and world clod are calculated for each and every class.
Moderating Role of Corporate Social Responsibility for the Effect of Managerial Ability on Financial Performance: Evidence from an Emerging Economy

The present study endeavours to examine the moderating role of corporate social responsibility (CSR) in the interaction between managerial ability (M.A.) and firm performance (F.P.). For estimation, the study analysed an unbalanced panel of 219 non-financial listed firms on the Pakistan Stock Exchange (PSX) for 2008-2021. Fixed effect regression with robust standard errors clustered at the firm level is applied to address the concerns of heteroscedasticity and autocorrelation. Findings show that overall managerial ability enhances firm performance. Furthermore, research has established a positive correlation between CSR and a company's financial performance. In addition, the interaction between M.A. and CSR has a substantial positive correlation with F.P. The results reveal that CSR positively moderates the linkage between M.A. and F.P. and behaves as a complement. The ongoing research is limited to the non-financial sector; however, the study findings are unquestionably valuable for corporate investors and managers in the context of developing economies.
Detection of Malaria in Blood Cells Using Convolution Neural Network

There are a variety of automated diagnostic techniques and models using numerous supervised learning models, but most of these models cater especially to the diseases that are seen in the Western countries, and they rarely see the outbreak of diseases such as malaria, dengue, etc. Early detection of these diseases can control the mortality rate and help save lives. Malaria while being a curable disease still has no vaccine available for it, so early detection of malaria can help determine the risk and can prove to be lifesaving. And with the time taken to collect, analyse and diagnose malaria in the blood is valuable time that can be the difference between a patient’s life or death. And less developed countries do not have the proper resources for fast response against the disease. In this project, we are hoping to develop an effective and efficient automated diagnostic model using machine learning models. For this, we have implemented a model based on CNN architecture to detect malarial parasites in blood cells and then use advance image processing techniques to contour and isolate the parasite to track the progression of the disease.