Reviewerly/exHarmony huggingface dataset:
TrajCross: Trajecotry Cross-Modal Retrieval with Contrastive Learning

In this paper, we propose a new task namely trajectory cross-modal retrieval which achieves the cross-modal search between coordinate trajectories and images containing trajectories. Nevertheless, trajectory cross-modal retrieval is rather challenging in learning the representations of each modality and reduce the cross-domain discrepancy caused by the inconsistent data distribution at the same time. we proposes a cross-modal retrieval model TrajCross based on multi-level representation for trajectory cross-modal retrieval. Specifically, TrajCross extracts the location features and the shape information respectively for the represention of multi-modal data. we adopt a contrastive learning method to achieve semantic preservation among similar multi-modal data. Extensive experiments show that TrajCross significantly outperforms state-of-the-art cross-modal retrieval methods.
Query-Based Image Retrieval using Support Vector Machine (SVM)

In many industries, the picture of the day now plays an important role in extracting information about the item. Many traditional image retrieval techniques have been used. It answers the user's question interactively by asking whether the image is relevant. In this digital age, graphics have become an important part of information processing. The image plays an important role in extracting information about the object in a variety of areas, including weather systems, tourism, medicine, and geology, in the processing of image registrations. There are several methods for retrieving images. It determines a person's inquiry interactively by asking users whether the image is relevant (similar). The efficient image database business has improved the process's functioning in the content-based image recovery system (CBIR). Content-based image recovery (CBIR) research has grown in importance. As individuals, we have studied and investigated various features in this manner or in combinations. We discovered that image Registration Processing (IRP) is a critical area in the industries. Several research papers on color feature and texture feature extraction were reviewed, and it was determined that point cloud data structure is best for image registration using the Iterative Closest Point (ICP) algorithm.
Without detection: Two‐step clustering features with local–global attention for image captioning

The current image captioning methods usually integrate an object detection network to obtain image features at the level of objects and other salient regions. However, the detection network needs to be independently pre-trained on additional data. Thus, mainly due to the demand for extra training data and computing resources, the detection network's utilization will impose higher training costs on the overall captioning model. In this work, the authors propose a local–global attention model based on two-step clustering features for image captioning. The two-step clustering features can be obtained at a relatively low cost and have the presentation ability in objects or other salient image regions. To make the model perceive the image better, the authors introduce a novel local–global attention mechanism. The model will analyse the clustering features from local perspectives to global ones at each time step, making the model better understand the image contents. The authors evaluate the proposed method on the MSCOCO test server, achieving BLEU-4/METEOR/ROUGE-L scores of 36.8, 27.4, and 57.2, respectively. With the benefit of reducing training costs, the authors' model also achieves closing results compared with the models using detection features.
A Low-Parameter Rendering Algorithm for Fine Textures

This paper introduces a novel rendering algorithm for virtual textures, specifically those with characteristic length scales below 1 mm. By leveraging the relatively lossy mode of human tactile perception at this length scale, a virtual texture with wide-band spectral characteristics can be reduced to a spatial sequence of single-frequency <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">texels</i> , where each frequency is pulled stochastically from a distribution. A psychophysical study was conducted to demonstrate that, below a limiting physical texel length, virtual textures defined by identical frequency distributions are perceptually indiscriminable. Additionally, an exploratory study mapped the distribution parameters of the texel-based rendering to spectral characteristics of perceptually similar multi-frequency virtual textures.
Intelligent path planning algorithm for cooperative traversal task of multiple unmanned ground vehicles

The task assignment of multiple constant-thrust unmanned ground vehicles in a warehouse has been studied in the paper. In order to reduce energy consumption, a set of dynamic multiple targets with fixed moving ways and different sensor demands are transported by vehicles with multiple customized sensors to complete the task while minimizing the total traveling distance. Firstly, considering the influence of the load brought by the weight of the carried sensor, a path planning model for dynamic targets was designed. Then, a co-evolutionary mixed population genetic algorithm (CMPGA) that combined virtual coding strategy, classical evolution method, and mixed crossover mechanism has been proposed, which was used to search for feasible solutions under the condition that the loading and endurance capacity of vehicles were limited. The simulation results showed that the proposed algorithm was feasible. Compared with the traditional simulated annealing algorithm (SA), particle swarm optimization algorithm (PSO) and the principle of proximity(Nearby), the proposed algorithm has better advantages in reducing energy consumption.
Evaluation Of Bitrate Ladders For Versatile Video Coder

Many video service providers take advantage of bitrate ladders in adaptive HTTP video streaming to account for different network states and user display specifications by providing bitrate/resolution pairs that best fit client's network conditions and display capabilities. These bitrate ladders, however, differ when using different codecs and thus the couples bitrate/resolution differ as well. In addition, bitrate ladders are based on previously available codecs (H.264/MPEG4-AVC, HEVC, etc.), i.e. codecs that are already in service, hence the introduction of new codecs e.g. Versatile Video Coding (VVC) requires re-analyzing these ladders. For that matter, we will analyze the evolution of the bitrate ladder when using VVC. We show how VVC impacts this ladder when compared to HEVC and H.264/AVC and in particular, that there is no need to switch to lower resolutions at the lower bitrates defined in the Call for Evidence on Transcoding for Network Distributed Video Coding (CfE).
One Digital Health Is FAIR

The One Digital Health framework aims at transforming future health ecosystems and guiding the implementation of a digital technologies-based systemic approach to caring for humans' and animals' health in a managed surrounding environment. To integrate and to use the data generated by the ODH data sources, "FAIRness" stands as a prerequisite for proper data management and stewardship.
Kinect Based Therapy Games To Overcome Misperception in People With Dysgraphia Using Dynamic Difficulty Adjustment

Dysgraphia is a disorder that affects the ability to write that can be experienced by children. Misperception therapy can be given to children with dysgraphia, but this type of therapy makes children easily bored. There are many studies that have been done to conduct therapy using games and Kinect sensor. In contrast to existing research, this study develops dysgraphia therapy games to overcome misperceptions in children that can be fun. Therapy games are made using Dynamic Difficulty Adjustment with the zero-order Sugeno Fuzzy model so that the difficulty level in the game is dynamic. Tests for therapists indicate that this therapy game can be used as an alternative media for misperception therapy in children. The testing result shows that misperception therapy games are fun to play by children.
Lung Cancer Detection and Prediction of Cancer Stages Using Image Processing

Lung cancer is one of the most common and dangerous cancers in the world. However, lives can be saved through early diagnosis by CT scan images, which is the best imaging technique in the medical field for early treatment. Though CT scan imaging is the best technique, doctors and radiologists face some difficulties such as not being able to diagnose early and commence treatment and to interpret and identify cancer from CT scan images because of the limitation of equipment and specialists. Therefore, to identify the cancerous cells accurately, computer-aided diagnosis can be more helpful for doctors. Computer-aided techniques based on image processing and machine learning have been extensively researched, and are being implemented currently to address this issue. This research is mainly focused on evaluating and analyzing the different computer-aided techniques, find out their limitations and drawbacks and finally, propose a new model with improvements. In the methodology section of this research, lung cancer detection techniques were sorted and listed based on their detection accuracy. The techniques were analyzed on each step, and overall limitations and disadvantages were pointed out. It is found that some techniques have low accuracy and some have higher accuracy, but not nearer to 100%. Therefore, our project target is to make a lung cancer detection model using CT scan images with high accuracy and predicting the lung cancer stage.
Handwritten Kannada Digit Recognition System Using CNN with Random Forest

Handwritten digit recognition is a major role in the applications of pattern recognition field. Various applications of handwritten digit recognition are sorting postal mails, processing bank cheques, data entry form, etc. We proposed a widely usage of modern handwritten digit recognition, i.e. Kannada-MNIST (Modified National institute of Standards and Technology) database holds various handwritten digits from 0 to 9, also the MNIST dataset is divided into training and testing as suitable parameter. Initially two basic functions are used, namely feature extraction and feature classification for HDR (Handwritten Digit Recognition) is achieved by Convolutional Neural Network (CNN). This works with some specific algorithms based on the requirements. Deep Learning Python framework is used to implement CNN on the plain MNIST dataset gives 99.63%. Random Forest (RF) classifier is used to make the feature extracted output of CNN model to predict better accuracy, by combining with CNN. Automatic Feature extractor is one the main function of the CNN with the Random Forest as prior classifier. The main aim of this research is to make the implementation by integrating both CNN and RF for higher accuracy. This combined algorithms is in charge to gain the score upto 99.66%.
Intelligent Jamming of Deep Neural Network Based Signal Classification for Shared Spectrum

Deep neural networks (DNNs) have recently been applied in the classification of radio frequency (RF) signals. One use case of interest relates to the discernment between different wireless technologies that share the spectrum. Although highly accurate DNN classifiers have been proposed, preliminary research points to the vulnerability of these classifiers to adversarial machine learning (AML) attacks. In one such attack, a surrogate DNN model is trained by the attacker to produce intelligently crafted low-power &#x201C;perturbations&#x201D; that degrade the classification accuracy of the legitimate classifier. In this paper, we design four DNN-based classifiers for the identification of Wi-Fi, 5G NR-Unlicensed (NR-U), and LTE LAA transmissions over the 5 GHz UNII bands. Our DNN models include both convolutional neural networks (CNNs) as well as several recurrent neural networks (RNNs) models, particularly LSTM and Bidirectional LSTM (BiLSTM) networks. We demonstrate the high classification accuracy of these models under &#x201C;benign&#x201D; (non-adversarial) noise. We then study the efficacy of these classifiers under AML-based perturbations. Specifically, we use the fast gradient sign method (FGSM) to generate adversarial perturbations. Different attack scenarios are studied, depending on how much information the attacker has about the defender&#x0027;s classifier. In one extreme scenario, called &#x201C;white-box&#x201D; attack, the attacker has full knowledge of the defender&#x0027;s DNN, including its hyperparameters, its training dataset, and even the seeds used to train the network. This attack is shown to significantly degrade the classification accuracy even when the FGSM-based perturbations are low power, i.e., the received SNR is relatively high. We then consider more realistic attack scenarios, where the attacker has partial or no knowledge of the defender&#x0027;s classifier. Even under limited knowledge, adversarial perturbations can still lead to significant reduction in the classification accuracy, relative to classification under AWGN with the same SNR level.
Decision Trees with Soft Numbers

In the classical probability in continuous random variables there is no distinguishing between the probability involving strict inequality and non strict inequality. Moreover a probability involves equality collapse to zero without distinguishing among the values that we would like that the random variable will have for comparison. This work presents Soft Probability by incorporating of Soft Numbers into probability theory. Soft Numbers are set of new numbers that are linear combinations of multiples of ones and multiples of zeros. In this work, we develop a probability involving equality as a soft zero multiple of a probability density function.
A Comprehensive Overview of Machine Learning Algorithms and their Applications

Machine learning (ML) utilises data and algorithms to simulate the way people learn and improve their accuracy over time and it’s also a subdivision of artificial intelligence (AI) and computer science. In AI, ML is a relatively recent domain that involves studying computational methods for discovering new knowledge and managing existing knowledge. Methods of machine learning have been applied to a diversity of application domains. . However, in recent years, as a result of various technological advancements and research efforts, new data has become available, resulting in new domains in which machine learning can be applied. This paper introduces the definition of machine learning and its basic structure. These algorithms are used for various purposes, including data mining, image processing, predictive analytics, and so on. The primary benefit of using machine learning is that once an algorithm learns what to do with data, it can do so automatically. This survey replenishes a brief outline and outlook on numerous machine learning applications.
Deep Bangla Authorship Attribution Using Transformer Models

Authorship attribution is one of the renowned problems in the domain of Natural Language Processing (NLP). Leveraging the state-of-the-art (SOTA) techniques of NLP such as transformer models, this problem domain has achieved a considerable advancement. However, this progress is unfortunately only bound to the well-resourced languages like English, French, and German. Under-resourced language like Bangla is yet to leverage such SOTA techniques to make a breakthrough in this domain. In this study, we address this research gap and aim to contribute to the Bangla authorship attribution problem by building highly accurate models using several SOTA variants of transformer models like mBERT, bnBERT, bnElectra, and bnRoBERTa. Using the pre-trained weights of these models we have performed fine-tuning and tackled the task of authorship attribution of 16 prominent Bangla writers. Outcomes show that our bnBERT model can classify the authors with superior accuracy of 98% and also outperform all the existing models available in the literature.
Research and Construction of Marine Chinese Medicine Formulas Knowledge Graph

Marine Chinese medicine is an important part of Chinese medicine. The storage and expression of knowledge about marine Chinese medicine and formulas recorded in ancient books and modern documents is a key issue in the research and utilization of marine Chinese medicine. This article standardized the marine Chinese medicine and formulas literatures content contained in the Chinese Medical Dictionary and CNKI database, used Protege modeling tool to establish the marine Chinese medicine and formula domain ontology, and used the Neo4j graph database to construct a knowledge graph from marine Chinese formula ancient books and modern literatures. The collection and sorting of 7,353 items of marine Chinese medicine literatures and some modern documents in CNKI were completed, and the ontology and knowledge graph were constructed through analysis to realize the retrieval of the marine Chinese medicine graph database. Based on the ontology, the knowledge graph of marine Chinese medicine literatures is constructed from top to bottom. Based on the graph, constructed an information system, with which researchers can retrieve and utilize marine Chinese medicine accurately and efficiently.
Learning Code Representations Using Multifractal-based Graph Networks

Learning representations of software codes is a critical problem for a wide range of system applications, e.g., compiler optimization, software classification, malicious software detection, and performance optimization. Recently, learning graph-based representations of software programs has been used to model the inherent structural dependencies in programming languages (e.g., C++, Python). In this paper, we propose a novel graph neural network framework that utilizes multifractal analysis for LLVM intermediate representations (IR). We then show empirically that the proposed framework is capable of capturing long-range structural dependencies that appear in software codes. We conduct experiments and comparisons on two downstream system applications: (1) predicting heterogeneous compute device mappings (graph classification), and (2) compiler reachability analysis (node classification). We observe that introducing a structural inductive bias through multifractal topological features enables GNNs to capture long-range dependencies among nodes, thus, it improves the accuracy of GNN models for applications that require learning code representations.
Don’t Discard All the Biased Instances: Investigating a Core Assumption in Dataset Bias Mitigation Techniques

Existing techniques for mitigating dataset bias often leverage a biased model to identify biased instances.The role of these biased instances is then reduced during the training of the main model to enhance its robustness to out-of-distribution data.A common core assumption of these techniques is that the main model handles biased instances similarly to the biased model, in that it will resort to biases whenever available.In this paper, we show that this assumption does not hold in general.We carry out a critical investigation on two well-known datasets in the domain, MNLI and FEVER, along with two biased instance detection methods, partial-input and limitedcapacity models.Our experiments show that in around a third to a half of instances, the biased model is unable to predict the main model's behavior, highlighted by the significantly different parts of the input on which they base their decisions.Based on a manual validation, we also show that this estimate is highly in line with human interpretation.Our findings suggest that down-weighting of instances detected by bias detection methods, which is a widely-practiced procedure, is an unnecessary waste of training data.We release our code to facilitate reproducibility and future research.
SCCD-GAN: An Enhanced Semantic Code Clone Detection Model Using GAN

Code clone refers to a pair of semantically similar but syntactically similar or different code fragments that exist in code base. Excessive code clones in software system will cause a negative impact on system development and maintenance. In recent years, as deep learning has become a hot research area of machine learning, researchers have tried to apply deep learning techniques to code clone detection tasks. They have proposed a series of detection techniques using including unstructured (code in the form of sequential tokens) and structured (code in the form of abstract syntax trees and control-flow graphs) information to detect semantically similar but syntactically different code clone, which is the most difficult-to-detect clone type. However, although these methods have achieved an important improvement in the precision of semantic code clone detection, the corresponding false positive rate(FPR) is also at a very high level, making these methods unable to be effectively applied to real-world code bases. This paper proposed SCCD-GAN, an enhanced semantic code clone detection model which based on a graph representation form of programs and uses Graph Attention Network to measure the similarity of code pairs and achieved a lower detection FPR than existing methods. We built the graph representation of the code by expanding the control flow and data flow information to the original abstract syntax tree, and equipped with an attention mechanism to our model that focuses on the most important code parts and features which contribute much to the final detection precision.We implemented and evaluated our proposed method based on the benchmark dataset in the field of code clone detection-BigCloneBench2 and Google Code Jam. SCCD-GAN performed better than the existing state-of-the-art methods in terms of precision and false positive rate.
Local Self-Attention on Fine-grained Cross-media Retrieval

Due to the heterogeneity gap, the data representation of different media is inconsistent and belongs to different feature spaces. Therefore, it is challenging to measure the fine-grained gap between them. To this end, we propose an attention space training method to learn common representations of different media data. Specifically, we utilize local self-attention layers to learn the common attention space between different media data. We propose a similarity concatenation method to understand the content relationship between features. To further improve the robustness of the model, we also train a local position encoding to capture the spatial relationships between features. In this way, our proposed method can effectively reduce the gap between different feature distributions on cross-media retrieval tasks. It also improves the fine-grained recognition performance by attaching attention to high-level semantic information. Extensive experiments and ablation studies demonstrate that our proposed method achieves state-of-the-art performance. At the same time, our approach provides a new pipeline for fine-grained cross-media retrieval. The source code and models are publicly available at: https://github.com/NUST-Machine-Intelligence-Laboratory/SAFGCMHN.