Reviewerly/exHarmony huggingface dataset:
Trustworthy Computing for O-RAN: Security in a Latency-Sensitive Environment

New 5G/6G mobile networks will allow to run distributed multi-tenancy workloads with high requirements on latency, throughput, and energy efficiency. This also demands a transformation of the underlying radio access networks (RANs) towards an architecture with open-source software, heterogeneous hardware, and interoperable interfaces as already described by the O-RAN ALLIANCE. In principle, virtualization of the RAN allows for defining additional interfaces which can be used for system verification and therefore can increase the trustworthiness of implementations. However, due to many security risks induced by the current O-RAN specification, it requires to rethink the overall security architecture from the ground up to establish trustworthy mobile networks in general.In this paper, we discuss hardware-enforced capabilities as a structuring principle for future O-RAN architectures. We present our approach of a hardware/operating system co-design to implement these principles. The evaluation results of architectural components demonstrate the technical feasibility of our approach and illustrate that security and low latency can be achieved simultaneously.
On End-to-End Learning of Joint Detection and Decoding for Short-Packet Communications

We propose a joint detection and decoding scheme for short-packet wireless communications in scenarios that require to first detect the presence of a message before actually decoding it. For this, we extend the recently proposed serial Turbo-autoencoder neural network (NN) architecture and train it to find short messages that can be, all “at once detected, synchronized, equalized and decoded when sent over an unsynchronized channel with memory. The conceptional advantage of the proposed system stems from a holistic message structure with superimposed pilots for joint detection and decoding without the need of relying on a dedicated preamble. This results not just in a higher spectral efficiency, but also translates into shorter messages and potentially lower latency when compared to the usage of a dedicated preamble. We compare the detection error rate (DER), bit error rate (BER) and block error rate (BLER) performance of the proposed system with a hand-crafted state-of-the-art conventional baseline. Our performance simulations show a significant advantage of the proposed autoencoder-based system over the conventional baseline in every scenario up to messages conveying $k=96$ information bits.
Knowledge-Assisted Few-Shot Fault Diagnosis in Cellular Networks

Data-driven network fault diagnosis faces the great challenge of lacking sufficient high-quality labeled data from real networks. Also, how to exploit existing domain knowledge to aid the diagnosis in an efficient and effective manner needs to be addressed. To obtain a high-performance diagnosis system based on a coarse data set and knowledge base, we propose a Robust Belief Weighting Framework (RBWF) based on abductive learning and belief rule structure for the few-shot fault diagnosis. The correction weight is obtained by the matching rules selected from the insufficient knowledge base to revise pseudo-labels for model retraining. Moreover, the revised pseudo-labels enhance the coarse knowledge base to become more comprehensive. Therefore, the knowledge base and the data-driven model can mutually reinforce to improve the respective performance. Experimental results illustrate the effectiveness and stability of the proposed framework using the cellular network data. Our method enables the automation of fault diagnosis for 5G and 6G networks to achieve highly reliable communication services.
Design of an Autonomous UAV for Racing Applications

A drone is an Unmanned Aerial Vehicle (UAV) that can be controlled manually by humans as well as by other algorithms not involving any human interaction. In this paper, we have described the way through which we have developed a robust and responsive racing drone alongside the simulation that includes the use of the ROS Mavlink as one of the ways to control the drone using a base station as well as path planning for autonomous flight travel. The drone will be controlled; by using a Pixhawk autopilot having a high processing capability, and easy interaction with other peripheral devices. With this, we were successfully able to design a low-weight racing drone, with each motor producing an upward thrust of 350g. Along with this, we have used a single ppm output receiver channel which reduces the number of wires and ultimately reduces the complexity of the drone, and that too at a very reasonable cost.
A Standardized Design for Sifting in Quantum Key Distribution Software

With 5G communication networks enjoying commercial adoption, work has begun towards defining future 6G networks. The emergence of commercially viable quantum communication technologies offer these future communication networks information-theoretic key agreement techniques enabling provably quantum resistant secure communications through quantum key distribution protocols. However, commercial quantum key distribution systems rely on independent software implementations of key agreement protocols. Thus, while these systems may be mathematically secure they may incur security vulnerabilities in software. To address this we design a standardized sifting module, which can be used to generalize the description and execution of the necessary sifting in most if not all discrete variable quantum key distribution protocols, including entanglement based protocols. We give a general description of the software stack underlying implementations of the post-processing stage of quantum key distribution protocols, including our formalization of the two-way sifting stage of said protocols and the machinery required to realize it in a completely general manner. Finally, we give some example configurations for some common quantum key distribution protocols.
A deep learning-based approach for hybrid nonlinear systems dynamics approximation

An increasingly important class of nonlinear systems includes the nonaffine hybrid systems, in particular those in which the underlying dynamics explicitly depends on a switching signal. When the inherent complexity is treatable and the phenomena governing the system dynamics are known an implicit model can be derived to describe its behaviour over time. When these assumptions are not met the system dynamics can still be approximated by regression-based techniques, provided datasets comprising input/output signals collected from the system are available. One approach relies on intelligent computing-based frameworks, in which artificial neural networks stand out as a class of universal approximation models. This paper, proposes a new approach for capturing nonlinear hybrid system dynamics based on 1D spatio-temporal convolutional neural networks, in which the inputs are represented by regressors and structural configuration parameters. The proposed deep neural network architecture is compared against a shallow multilayer layer perceptron framework, in which each structural configuration is independently approximated. Experimental results point out to the superiority of the 1D spatio-temporal convolutional network.
Food Supply Chain Traceability System using Blockchain Technology

Agricultural sector plays a major role in a country’s Gross Domestic Product (GDP). The existing Food Supply Chain (FSC) suffers from issues like quality, safety, conscious consumption, and regulatory issues. Blockchain technology is a promising technology that can handle all the short comes faced by the current FSC with its key multidimensional features like decentralization, immutability, security, and smart contract. Introducing blockchain technology to FSC systems enhances sustainability and ensures trust among different entities. The blockchain-based FSC system suffers from data handling capability and regulatory issues. In this paper, the problems of the existing blockchain-based FSC system are overcome by introducing a new Food Supply Chain Traceability System (FSCTS) using an Ethereum smart contract linked to an InterPlanetary File System (IPFS) which can handle huge amount of data. The interaction between the different entities is elaborated using sequence diagrams through the proposed cryptocurrency called Amrita Virtual Coin (AVCoin) which overcomes regulatory compliances. The paper also discusses the implementation of the proposed frameworks using enhanced algorithms and its advantages over the existing framework. Finally, different types of open research challenges in the blockchain-based FSC are also addressed in the paper.
Automated Oral Cancer Detection using Deep Learning-based Technique

Oral cancer (OC) is a key worldwide health concern and is typically prevailing in low- and middle-income countries. Enabling the identification of patients suffering from potentially cancerous and noncancerous or normal lesions would lead to low-cost and early diagnosis of the disease and in turn reduce oral cancer mortality. During a routine oral examination, oral lesions are normally screened manually. Due to lack of medical facilities and medical expertise, the provision of an automatic initial screening process using artificial intelligence (AI) will greatly support to identify possibly cancerous lesions. The present work is an effort towards implementing an automated detection technique for diagnosing OC from oral photographic image dataset using deep learning techniques (DLTs). For this research, three candidate pretrained DL-CNN models specifically VGG19, InceptionNetV3, and Xception are preferred. These candidate DL-CNN models using the transfer learning (TL) approach were further improved with extra layers. The efficacy of the modified models is examined on an oral cancer photographic image database. It is examined that the pre-trained Xception DL-CNN model with modified structure has surpassed other models in terms of performance parameters.
RIPPB: A Robust and Improved PBFT Protocol for Blockchain

In this paper, we propose RIPPB, a robust and improved PBFT protocol for Blockchain, to improve the quality of service for blockchain applications. At RIPPB, we introduce reputation for nodes and use reputation as the voting weight for node consensus to go against Sybil attacks. And we design a novel improved logistic regression model to assign reputations to nodes based on their behavior involved in block generation. With this model, nodes performing Stake-grinding attacks and Bribery attacks, face high reputation penalties, which is unacceptable for malicious nodes to reduce the probability of launching these attacks. In addition, RIPPB adds a pre-commit phase to the PBFT algorithm to reduce the number of inter-node communications, thus improving the performance of the protocol. Cost-of-attack analysis shows the high robustness of our protocol. Experimental results show a 28% improvement in the throughput of our protocol over PBFT.
Twitter Sentiment Analysis Based Public Emotion Detection using Machine Learning Algorithms

Due to the advancement and growth of technology, a bulk of unstructured data is generated by different internet users. These users belong to different areas of the world and have different perspectives of thoughts. These users are actively engaged in various social networking platforms (SNPs) like Twitter, Facebook, Instagram, Reedit, etc. Most often, people use these SNPs for amusement or to harm or target others. It is necessary to detect such public emotions in order to protect society. These public emotions heavily rely on the individual's mindset and point of view. The analysis of sentiments is a large area of natural language processing (NLP) that receives a lot of attention for classifying the polarity of the text, figuring out whether it contains subjective information, and detecting what kind of information it is supplying. In this study, We proposed a machine learning-based model for multi-class public emotion detection from Twitter data. The proposed model has been evaluated on the publicly available Kaggle dataset and achieved state-of-the-art performance in terms of precision, recall, and F-measure. The experimental results exhibit that the logistic regression (LR) model has outperformed others.
A New Residual Image Sharpening Scheme for Image Up-Sampling

The main challenges in image interpolation approaches are edge restoration and preserving texture details while producing high resolution (HR) images. Interpolation causes approximation error in terms of blurring because it predicts unknown pixels in any location of the image grid. To address the aforementioned issue, the suggested method employs residual image sharpening based on the high frequency (HF) details of a low resolution (LR) image. The sub-sample image edge is determined before upscaling with lanczos interpolation. To determine the non-uniform blurring effect in an upscaled image, the HR image is sub-sampled once more, resulting in an LR image denoted as (LR2). The difference between the LR and LR2 images is used to identify the degradation in HF due to upscaling. To compensate for interpolation loss, the edge details of the subsampled image is aggregated to the degraded image to form the edge-residual image, which is then up-sampled. To extract fine details from the interpolated residual image, a high order filter is used. The weighed sharpened image is aggregated with the previously obtained up-sample image to produce an image with preserved edge and texture. The intended algorithm’s performance is measured using images from various databases and compared to state-of-the-art techniques. The proposed scheme outperforms the existing interpolation schemes.
Text Classification of Judgement Documents Considering Sample Imbalance

As China’s smart judicial field has been continuously improved and the technologies in Deep Learning has been research hotspots, methods in Text Classification are more and more frequently used in legal judgment documents. As the carrier of the final legal judgment, the judgment document contains a lot of valuable information, including the description of the facts, the legal terms on which the judgment is based, the type of crime, and the name of the case. At present, the classification methods of judgment documents on criminal offenses and legal provisions mainly focus on machine learning and model fusion, but due to the excessive number of labels, it is easy to lead to extremely unbalanced distribution of sample categories. Therefore, this paper improves the focal loss by adding label prior knowledge, and solves the imbalance problem based on the RTR-SA model. Experimental results on the CAIL2018-Small dataset show that the proposed method outperforms existing methods.
Mobile Application based Tracking using GPS and GSM

Many Global Positioning System (GPS) and Global System for Mobile Communication (GSM) based trackers are available in the market. However, the current ones in the market are either costly, inaccurate, unreliable, or have poor User Interfaces mobile applications. This paper describes a practical model for tracking location, based on the GPS and GSM, which is inexpensive, versatile, secure, and reliable. The location can be known using a mobile application that fetches the location from a secure online database where the location gets stored after some interval of time. By incorporating an emergency protocol where a message goes to a specified mobile number containing the latest location with an alert in the mobile application, we have made the model better than most available in the market.
Easy Mandi - An Agricultural Production Trading Platform for Indian Markets

A large majority of people in India are employed in agriculture. To protect their interests, the government of India has implemented laws that restrict the trade of agricultural produce to only regulated marketplaces. These marketplaces are inefficient and need to be replaced with a less bureaucratic yet equally reliable system. This paper proposed a blockchain-based agricultural produce trading platform named “EasyMandi” to alleviate the problems in Indian agricultural markets. Using smart contracts on the Ethereum blockchain network, the agriculture product can be auctioned, and the highest bidder wins the product while the contract stores funds that automatically get transferred to the farmer upon delivery. The proposed EasyMandi platform tries to remove the concept of middlemen and the related inefficiencies in the previous marketing system and facilitate farmers to get better prices while being shielded from any risks of fraud or harassment.
Learning Disease Specific Knowledge Graph from Unstructured Radiology Reports and Electronic Health Records (EHRs)

With an increasing growth rate, cancer is now the second biggest cause of mortality in the globe, killing a growing number of individuals every year. Electronic health records (EHRs) and radiology reports contain an enormous amount of clinical information. Case studies are crucial because they provide a wealth of medical knowledge regarding disease, medications, therapies, and other aspects. However, it is difficult to work with this information because it is often available in unstructured notes. Moreover, the production rate is quick, the volume of data is enormous, and the format is unique. The optimal solution to this problem is a knowledge graph (KG), a potent tool for information interaction and knowledge organisation. The goal of this study is to create a disease specific KG to present medical information from EHRs and unstructured radiology reports with focused semantics and granularity. In this paper, an entire KG construction framework built on the foundation of EHRs is proposed. The research proposed a KG schema with 7 classes and 15 different types of semantic relationships, and developed the proposed KG in Neo4j Desktop by using the extracted knowledge through Named-Entity Recognition (NER) from radiology reports and EHRs. The proposed study would be a learning step towards using KG intelligently to analyse the progression of diseases course, learnt from EHRs data.
2D-VMD Based Speckle Reduction in Ultrasound Images using JS distance and Wavelet Thresholding

This paper proposes a technique called 2D-VMD Based Speckle Reduction in Ultrasound Images Using Jensen Shannon (JS) Distance and Wavelet Thresholding. First, the image is broken up into a set of functions known as intrinsic mode functions (IMFs). The JS distance between the probability density function (PDF) is the measure to tell the difference between IMFs that are dominated by signal and IMFs that are dominated by noise. The IMFs that depend on noise are then cleaned up using wavelet threshold denoising method. The signal-dependent IMFs and the denoised IMFs are used to rebuild the image and get the denoised image. The proposed technique is tested on simulated and real ultrasound images with different levels of noise. In terms of EKI, CC, PSNR, SSIM, MVR and ENL, the experiments confirms that the proposed algorithm is better than other methods that are already in use.
Using Image Processing for Determination of Some Physical Properties of Nano-materials

In this article we will process the images obtained through nanoscale devices to obtain some physical properties of nanomaterials by using image processing techniques, numerical methods, before we do an improvement of the images obtained for a precise image high clarity and then we use numerical methods which is a method of completion through which you can get all the physical properties of samples and others, Images, which obtained through nanoscale devices as the transmission electron microscope (TEM) to obtain some physical properties of nanomaterials by using image processing techniques, numerical methods as Cubic Convolution Interpolation (CCI), We will improve the images obtained for accurate image High clarity and then we use numerical methods, which is a method of interpolation method through which you can get all the physical properties of Nano-materials, We will provide a general introduction by obtaining nanoscale images, numerical methods image-processing methods and many other important aspects of high resolution imaging, for example pictures of glycerin and other materials to illustrate a variety of as the transmission electron microscope (TEM) applications to study nanomaterials. Finally, We will obtain set of images by Nano device as the transmission electron microscope (TEM) or any other device, for samples prepared samples in diverse conditions having different physical properties, and analyzed this a set of images using some numerical technique in image processing, which starting by converting images to data file $(^{\ast}.\mathrm{dat})$ using programming languages codes. After using filters and we will get the more results some physical properties of Nano-materials.
A study on the application of named entity recognition in resume parsing

In recent years, with the development of machine learning as well as deep learning, named entity recognition has also been rapidly developed. Currently, with the huge saturation of the talent market, resulting in a dramatic increase in the number of resumes, it has become more difficult to screen resumes, and company personnel have taken on a huge burden of screening work. The application of named entity recognition on resume parsing can greatly shorten the time of resume screening and apply more time to the screening of target talents. Based on the problems faced by named entity recognition in resume parsing, this paper proposes traditional machine learning models HMM, CRF and deep learning model BiLSTM+CRF to solve them. After a series of experiments, the effectiveness of the proposed method is proved.
Attention and Cost-Sensitive Graph Neural Network for Imbalanced Node Classification

Semi-supervised node classification of imbalanced graphs is one of the important tasks in the field of graph neural network (GNN). Most of the current methods focus on how to aggregate feature information from neighbor nodes, but they do not distinguish the importance of minority class and majority class samples in the process of aggregation. To this end, this paper introduces an attention mechanism in the process of aggregating feature information, which flexibly assigns individualized weights to minority and majority class samples. At the same time, we improve the loss function using cost-sensitive techniques to increase the minority class misclassification cost. Finally, we design experiments to verify the effectiveness of the proposed method.
HTransT++: Hierarchical Transformer with Temporal Memory and Spatial Attention for Visual Tracking

Transformer-based architectures have recently witnessed significant progress in visual object tracking. However, most transformer-based trackers adopt hybrid networks, which use the convolutional neural networks (CNNs) to extract the features and the transformers to fuse and enhance them. Furthermore, most of transformer-based trackers only consider spatial dependencies between the target object and the search region, but ignore temporal relations. Simultaneously considered the temporal and spatial properties inherent in video sequences, this paper presents a hierarchical transformer with temporal memory and spatial attention network for visual tracking, named HTransT ++. The proposed network employs a hierarchical transformer as the backbone to extract multi-level features. By adopting transformer-based encoder and decoder to fuse historic template features and search region image features, the spatial and temporal dependencies across video frames are captured in tracking. Extensive experiments show that our proposed method (HTransT ++) achieves outstanding performance on four visual tracking benchmarks, including VOT2018, GOT-10K, TrackingNet, and LaSOT, while running at real-time speed.