Reviewerly/exHarmony huggingface dataset:
Path Planning Method for Soil Investigation using Mobile Robot

A lunar landing base is a key infrastructure for mass transportation from Earth to the Moon and for sustainable activities on the Moon in the future. Unmanned robots are expected to survey the soil stiffness of multiple points for securely constructing the landing infrastructure. There is a trade-off between the time or energy required for the survey, the number of survey points, and the completeness of the survey. This paper proposes a path planning for robotic soil investigation that determines the location and the number of additional survey points. The proposed method aims to obtain completeness of information related to the soil stiffness as well as to reduce the survey time. The simulation study reveals that the method achieves obtaining a certain level of completeness while it reduces the time required for the survey.
A novel multi document summarization with document-elements augmentation for learning materials using concept based ILP and clustering methods

Multi Document Summarization (MDS) is a technique for extracting succinct summaries from groups of related documents. The usage of MDS in the e-learning context is more appealing for providing summaries for learning materials, which helps students and teachers to focus on key concepts of the learning materials. In a learning material, availability of non-textual document-elements such as figures/diagrams, plots, graphs, tables and algorithms can be seen widely, but keeping such document-elements in the summary is not possible as they are non-textual. This proposed work incorporates the text summarization approach with document-elements augmentation to the summary to provide a detailed coverage of information without exceeding the summary length constraint. The key information in the source text is identified by important phrase features and sentence features, and the summaries are generated by selecting important sentences using the Integer Linear Programming (ILP) framework while reducing the redundancy using pre-trained sentence vectors. The relationships between the summary and document-elements are identified through document-element snippet extraction and a Hierarchical Agglomerative Clustering approach. Experimental results of the proposed summary extraction and augmentation on educational dataset (EduSumm) show better performance compared to the state-of-the-art approaches.
Erosion-clustering connected domain segmentation algorithm for phase error compensation

Compensation of phase errors has emerged as a paramount task in fringe projection profilometry. In this paper, an erosion-clustering connected domain segmentation algorithm is proposed to compensate for the phase errors of traditional temporal phase unwrapping (TPU). At the onset, two connected domain maps from the wrapped phase are generated with a threshold value of zero. Then the connected domain adhering parts due to noise and height abruptness are forcibly disconnected by an erosion algorithm. Finally, the separated valid points are clustered based on the minimum Eulerian distance method. The combination of several techniques enables the reconstruction of high-quality 3D geometry and endows our method with the flexibility to redress errors arising from the traditional TPU method. Experimental results demonstrate the effectiveness and versatility of the proposed method, building upon the existing TPU methods that obtain fringe orders.
Binocular depth estimation method based on multi-layer guiding feature fusion

3D convolution based stereo matching network has a wide range of research prospects at present, such as 3D measurement, unmanned driving, etc., but there is still room for improvement in accuracy. This paper proposes a threedimensional matching method based on deep learning: In the feature extraction part, a multi-layer learning parameter guiding feature fusion module is proposed, which can preserve the pixel gradient of the edge when sampling under single channel image guide. Then, the instance whitening noise of the output feature map is calculated, which effectively eliminates image pixel shift and feature similarity through the covariance threshold. In addition to using the traditional SmoothL1 loss function, the algorithm calculates the stereo focus loss by designing the confidence detection network to adjust the cost volume. The algorithm is tested on SceneFlow and Kitti series datasets. Using a multi-layer guiding module, instance bleaching loss, and stereo focus loss simultaneously compared to the original version, the error between test result and Ground Truth in the first frame (D1 Loss) of are reduced by 30.6%(Kitti2015), and the three-pixel error (3PE) is reduced by 6.3%(Kitti2012), which verifies the effectiveness of the algorithm.
Photon statistics of non-Gaussian entanglement based on two-mode photon-subtracted squeezed vacuum states

We investigate the photon statistics of two-mode photon-subtracted squeezed vacuum states (TMPSSV), which are important factors to affect the sensitivity and resolution of optical interferometers. The comparison between the photon statistics of TMPSSV and two-mode squeezed vacuum states (TMSV) shows that the non-Gaussian operation has the effect of narrowing the characteristic widths of photon number probability distributions. Different from the super-Poissonian statistical distribution of TMSV, when the initial squeezing parameter is lower, TMPSSV shows a sub-Poissonian statistical distribution which is desirable for enhancing the sensitivity and resolution of interferometers. Therefore, non-Gaussian entanglement based on TMPSSV provides a promising quantum resource for an ultra-sensitivity and super-resolution optical interferometer far beyond the standard quantum limit.
Non-uniform sampling based frequency shifting fringe projection technique

Recently, the frequency-shifting (FS) projection technique provides a promising alternative to the traditional phase-shifting (PS) fringe projection profilometry (FPP). It solves the absolute phase retrieval problem directly in a completely different fashion, without any phase unwrapping. However, due to the Nyquist sampling theorem (NST), a constant frequency-shift only supports for a limited effective field (EF). It only covers a limited region of the fringe pattern, and the phase retrieval quality degenerates dramatically for those out of the EF. This issue tends significant especially for a high-resolution fringe pattern, limiting the practical application in real life. To this end, we introduce the non-uniform sampling strategy into frequency-shifting technique. With a temporally varying frequency-shift, the EF can be improved significantly, achieving globally high quality across all regions in the pattern for the retrieved phase. Simulation and real experimental results have validated the efficacy of the proposed technique.
Reversible Image Steganography to Achieve Effective PSNR

Image Steganography is one of the most well-known methods where a top-secret data is embedded into the pixels of an image by altering some of its pixel values. Reversible Image Steganography is used extensively due to its property of reconstructing the original cover image without any loss. Various studies have been conducted to achieve stego image with high Embedding capacity, less time complexity, high imperceptibility, and robustness. Keeping the necessary parameters under consideration, we conducted our study on Reversible Image Steganography to achieve better Peak Signal to Noise Ratio (PSNR) value or high Embedding capacity which helps us in analyzing the quality. We have analyzed PSNR ratio and compared it with the standard tests ensuring the quality of the embedded image. Also, we have provided an overview of numerous Reversible Picture Steganography techniques based on compression, encryption, higher capacity, quality, and performance.
Catch Your Session, Track Your Pulse e-Health Service Using Blockchain

The well-adjusted measure of health is the best resource for a healthy society for any nation. Statistics show that more than 2.4 million Indians die prematurely each year from diseases with a lack of high-quality medical care at the right time. Patients have to face longer waiting times until they get their turn to consult the doctor, which is not appropriate for the current covid scenario and in our busiest lives. However, prospective patients won’t have the chance to access healthcare systems, particularly those with a high rate of arrival. When a patient’s data is scattered across many hospitals throughout the lifetime of an individual, getting health data from one provider to another at the proper time remains a major challenge. This paper addresses this issue using Blockchain technology which enhances trust, transparency, and quality control in providing efficient health care at the right moment. The appointment system is presented with a simple booking menu, which is a beneficial method for lowering waiting times and increasing the likelihood that individuals will have access to effective healthcare at the correct time. The developed system would check the doctor’s availability and be able to serve the patient at his designated slot in the concerned hospital for emergency services. The system manages and reschedules the slots, allowing patients to choose their preferred time frame. The system functionality is adaptable to incorporate blockchain, cloud, and IoT technologies to produce secure transparent healthcare for giving treatment at the correct moment.
CNN-Based Adversarial Embedding for Image Steganograpy

Image steganography technologies are frequently developed such that steganalytic characteristics including image stats can be preserved. Because machine learning i.e. an (ML)-based classification model is used for most advanced steganalytic approaches, counter-steganalysis through the use of the discrepancy of the ML based classifiers is appropriate. In the name of adverse embedding, though, the application of disturbances will simply cause data recovery loss and & even invite unforeseen devices that can be detected by several other classification models. Inside this report, the purpose is to hide a stego code, in conjunction, for the meantime, to trick the CCN-based steganalyzer, using a sophisticated operation known as Adversarial- Embedding (ADV-EMB). The suggested procedure functions as a conventional environment for minimizing distortion. In general, ADV-EMB is responsible for the costs of changing picture elements based on the back propagation of gradations from the CNN steganalyzer range. The possibility of a modified direction is thus relatively higher than the opposite sign of the gradation. Thus, the alleged adversarial technique based stego- images are created successfully. Experiments explicitly show, by increasing missing recognition rate, that the suggested steganographic methods achieve improved safety success against the unknown prey adversarial- steganalyzer. This also undermines the success of other adverse system-conscious steganalyzers, providing a route to an advent of international steganographic methods which can transcend & counteract strong steganalysis of CNN-based techniques.
Ansuni Baat—A Bridge to Sign Language Communication

Since technology has advanced throughout the corners of the world. The focus now is on solving a real-life problem. According to WFD, there are 72 million people that use sign language, i.e., making 72 million people who are dumb. Our idea of solving a real-life problem was met by the problems of deaf and dumb people which is easy communication. According to the data available it should be noted that in the world over 140 million people use sign language to indulge in conversations with family and friends who are deaf and dumb. Particularly in the case of the US, according to the data made available by the U.S. Consensus Bureau 3.6% of the population, i.e., approximately 12 million people are either deaf or having hearing challenges. Also, to add onto this research and statistics estimate that in America anywhere between 5 and 20 Lakh speak American Sign Language (ASL) making it the 5th most spoken language of the country behind the likes of Spanish, Italian, German, and French. Some good amount of state schools give extra credits for pursuing ASL as a foreign language. Research and studies show that learning a second language has a broad range of advantages including improved communication and cognition and delaying life-threatening diseases like Dementia and Alzheimer’s. Through our work, we want to create an application that can help people communicate without any hindrance. During this study, we will create a real-time video calling app that can convert sign language to subtitles and speech. We will train our deep learning model and use it in our ReactJS web application. Our target audience is large hence it will require us to train some datasets for the same.
Real-Time Facial Mask Detection Using Deep Learning

The COVID-19 outbreak shook the world with its continuous waves. Since, the virus was mainly spreading through respiratory droplets, wearing a face mask was made compulsory for every person in the world with other preventive measures like taking vaccine doses. As a famous saying “Prevention of disease is still better than the cure,” perfectly sums up the increase in use of face masks and even shows the importance of the masks. This research is centred around the implementation of deep learning and computer vision methodologies. Image classification has also been used on the different images of people wearing masks. Some pretrained models like MobileNetV2, VGG16, ResNet50 were used along with training a new model from scratch. Subsequently, the models were applied to a live video stream to determine whether an individual is wearing a mask correctly, incorrectly, or not at all. The use of several types of masks in various aspects and various places of society is also particularly important, wrong type of mask at wrong place can also cause severe problems. Surgical masks are used in hospitals, N95 masks in chemical labs, etc. So, it was decided to classify the masks into various categories and implement it into the model such that the model can classify the type of mask as well. To sum up the research and make the model presentable, an application was developed to make the model easy to use in different scenarios and platforms such as mobile phones, desktops, etc.
Deep Learning Based Segmentation Approach for Cervical Cancer Detection Using Pap Smears

Cervical cancer is fourth among all malignancies that afflicts women. An earlier and possibly life-saving cancer diagnosis may be achieved if only the warning symptoms were recognised sooner. Automatic and precise cervical cancer screening methods may be developed if Pap smear cell images are properly segmented and classified. In this study, we used a smaller Visual Geometry Group-like Network in conjunction with a MaskR-CNN to effectively separate cervical cells (VGG-likeNet). The Mask RCNN is a ResNet10-based RCNN that complements spatial data with historical information during training. We tested our method with the Herlev Pap Smear research data to ensure it works as intended. Mask R-CNN surpasses the prior segmentation method on the complete cell in terms of recall (0.91), accuracy (0.92), and ZSI (0.81). (0.90). Classifying the whole segmented cell using a VGG-like Net improves results in both the binary classification problem and the seven-class issue, with a sensitivity of over 96% and a low standard deviation (2.8%).
An Investigation of Learning Model Technologies for Network Traffic Classification Design in Cyber Security Exercises

With the proliferation of network systems, the boundaries between cyber and physical environments are blurring, leading to an increased risk of sophisticated cyber-attacks equipped with advanced technologies. In particular, as advancements in artificial intelligence through learning models have led to automated attacks and attack scenarios, countries are implementing cyber training and constructing training systems to respond to cyber security threats. This cyber training is based on existing cyber-attacks and conducted in virtual spaces similar to reality, generating network traffic through simulators and focusing on training for attack response and cyber resilience. However, the exponential increase in the number of network-based devices and the amount of network traffic they generate is leading to a gradual increase in threats to cyber security. In this study, we first investigated the existing port number-based network traffic classification technologies and payload-based network traffic classification technologies to identify their shortcomings in the current network environment. We then categorized existing studies into supervised, unsupervised, and reinforcement learning to analyze the technology of classifying network traffic based on learning models as well as classification methods, procedures, performance standards, evaluation methods, quality of service/quality of experience, etc. Based on the analysis, we presented limitations for application to training networks according to the learning method and suggested recommendations for establishing future research directions. Therefore, refining learning model-based network traffic classification technology will contribute to the construction of automated cyber training grounds such as cyber-attack–defense scenarios, network traffic anomaly detection, and maximizing cumulative rewards.
IntelliCon: Confidence-Based Approach for Fine-Grained Vulnerability Analysis in Smart Contracts

Ethereum smart contracts are programs that execute transactions on a distributed ledger platform without intermediaries. However, they are prone to various types of vulnerabilities that can affect their security and functionality. In this paper, we present IntelliCon, a novel framework that leverages a pre-trained identifier-aware encoder-decoder CodeT5 model and confident learning to detect seven types of vulnerabilities in Ethereum smart contracts. Confident learning is a technique that improves dataset quality by identifying and correcting noisy labels, particularly in the presence of multiple annotators with varying levels of accuracy. We fine-tune CodeT5 on a dataset of 27,426 smart contracts annotated by multiple tools and pruned by confident learning to ensure that the model learns genuine vulnerability features rather than tool-specific features. Furthermore, we utilize abstract syntax tree (AST) analysis to extract code gadgets with sliding windows to locate the function that may contain code vulnerabilities. We evaluate the effectiveness of our framework in vulnerability detection with F1-score. Our results indicate that IntelliCon achieves high Micro-F1 (0.9591) and Macro-F1 (0.9293), outperforming existing methods. Moreover, our framework demonstrates its ability to handle imbalanced data, noisy labels, and complex code structures. IntelliCon contributes to improving the security and reliability of smart contracts, providing insights for future research on code generation tasks.
Based on Financial Characteristics to Capture the Source of Funds of the Ponzi Scheme on Ethereum with Graph Traversal Technology

Detecting Ponzi schemes in blockchain is an urgent task due to the various illegal transactions generated on the blockchain, among which Ponzi schemes are difficult to identify. The high returns promised by Ponzi schemes lure more and more people into the fraud. To prevent further economic losses, this paper proposes a detection method based on the financial features of Ponzi schemes and graph theory. By constructing a transaction subgraph of the fund sources of Ponzi schemes and traversing the subgraph, the proposed method can locate the fund source nodes of Ponzi schemes according to the matched subgraph structure. This paper is the first to combine the financial features of Ponzi schemes with graph algorithm techniques to study the simultaneous de-anonymization of multiple nodes in Ponzi schemes.
ScamRadar: Identifying Blockchain Scams When They are Promoting

The vigorous growth of cryptocurrencies has infiltrated into social media, manifested by extensive advertisement campaigns popping up on platforms such as Telegram and Twitter. This new way of promotion also introduces an additional attack surface on the blockchain community, with a number of recently-identified scam projects distributed via this channel. Despite prior efforts on detecting scams, no existing works systematically study such promotion channels for detecting on-going scam projects. In this paper, we take the first step to propose a novel approach that leverages promotion messages to detect on-going blockchain-themed scam projects. We extract features spanning four dimensions: word frequency, sentiment analysis, potential profit margin and URL information. For automated detection, we first construct two datasets, and then train a classifier on them that achieves a high accuracy (over 90%). Through our large-scale study, we have identified 69, 148 highly suspicious scam messages, relating to 8, 247 blockchain addresses. By tracing the confirmed scam addresses, we further identify a cumulative illicit income of around 900 ETH. Our findings should help the blockchain community understand and promptly detect scam projects when they are promoting.
Securing Blockchain Using Propagation Chain Learning

Smart contract vulnerabilities are the most common and severe type of blockchain vulnerability, which may result in very serious economic and property losses. Vulnerability detection and repair are necessary to ensure the security of the blockchain. Currently, the-state-of-art smart contract vulnerability detection methods (e.g. Oyente and Securify) use heuristics based on human-designed algorithms, which have certain shortcomings in different application scenarios. Therefore, this paper proposes a smart contract vulnerability detection method, i.e. CuVuD, which uses Propagation Chain Learning to solve the current vulnerability detection problem. This method first parses the source code, then obtains and trims the propagation chain of smart contracts, and finally detects vulnerabilities in smart contracts. To verify the effectiveness of CuVuD, this paper compares the CuVuD method with seven the-state-of-art smart contract vulnerability detection methods on a large-scale smart contract dataset based on the Solidity language. The experimental results show that CuVuD’s effectiveness in detecting smart contract vulnerabilities is significantly higher than seven the-state-of-art smart contract vulnerability detection methods, significantly improving the ability to detect vulnerabilities.
Towards Efficient and Privacy-Preserving Hierarchical Federated Learning for Distributed Edge Network

Federated learning is a promising paradigm that utilizes widely distributed devices to jointly train a machine learning model while maintaining privacy. However, when oriented to distributed resource-constrained edge devices, existing federated learning schemes still suffer from heterogeneity challenge. Hierarchical federated learning divides devices into clusters based on their resources to enhance training efficiency. In addition, there are also security concerns such as the risk of privacy leakage during the interactions intra and cross the layers. In this paper, we propose PHFL, a Privacy-Preserving Hierarchical Federated Learning Scheme, which includes a personalized task assignment protocol and a heterogeneous-friendly aggregation scheme, so as to achieve an efficient and low-cost training for resource-constrained edge devices. To address the heterogeneity challenge, we combine synchronous and asynchronous aggregation, and design different aggregation weights for device differences. On top of that, a privacy-preserving scheme based on threshold homomorphic encryption is designed to accommodate the privacy requirements of the proposed hierarchical federated learning scenario. The security analysis and performance evaluation demonstrate that this approach improves model generalizability and learning efficiency with acceptable overhead, while also providing privacy protection.