Reviewerly/exHarmony huggingface dataset:
Signal Detection for IoT Networks with Unknown Channel Models: A Knowledge-Driven Approach

This paper considers a signal detection problem for uplink transmission in Internet-of-Things (IoT) networks. Due to the non-idealities of IoT devices such as amplifier’s non-linearity, the exact end-to-end channel model as well as the accurate channel state information (CSI) is not available at the receiver (i.e., base station), which precludes the possibility of using classical model-based signal detection methods. Deep learning (DL) techniques have been recognized recently as an effective tool to deal with this challenge. However, for IoT scenarios, devices typically transmit data using short packets with few pilot symbols. The amount of training data is insufficient to train a detector for each device individually. In order to combat the data scarcity barrier and enable few-shot learning, this paper proposes to aggregate pilot symbols from different devices in an intelligent manner to train a universal signal detector which is applicable to all possible channel conditions. To be specific, a knowledge-driven signal detector architecture is devised following the modular design methodology for classical communication system receivers. Under this framework, three neural networks (NN), termed as channel feature extractor, signal feature extractor, and signal classifier, respectively, are employed to form decision statistics and make estimates on the transmitted symbols. Furthermore, borrowing the ideas in domain adaptation, a novel component termed as link discriminator is integrated into the architecture to improve the generalization capability of the detector. Simulation results demonstrate that the proposed knowledge-driven detector outperforms the existing solutions in the sense that it enjoys higher detection accuracy and can be well trained with less training data.
MUABR: Multi-user Adaptive Bitrate Algorithm based Multi-agent Deep Reinforcement Learning

Adaptive bitrate (ABR) algorithms have been considered as efficient ways to improve the performance of video streaming by adaptively selecting appropriate video transmission bitrate. However, traditional ABR algorithms show poor adaptability to complex and dynamic networks, thus limit user’s quality of experience (QoE). In the case of multi-user streaming, multiple users simultaneously requesting video from the server often compete for a bottleneck link. The bandwidth sharing problem should also be carefully considered, otherwise it will result in poor fairness and insufficient bandwidth utilization. To address these issues, this paper proposes a multi-user adaptive bitrate algorithm (MUABR) based on multi-agent reinforcement learning. MUABR takes the overall QoE of multi-user as the optimization goal, designs neural network structure and training method by reinforcement learning, so as to solve the problem of bitrate selection and poor adaptability when performing video streaming. Furthermore, the bandwidth allocation strategy adopted in MUABR ensures fairness to a certain extent and improves bandwidth utilization. Comprehensive experimental results show that MUABR has reached the desired goal. Compared with Pensieve and Festive algorithms, MUABR can improve the average QoE of users by about 7.5%-24.6%.
Modified Identify and Broadcast-based Encryption Scheme to Secure Cloud

Cloud computing is foreseen as the lineage framework model for Distributed Computing Environment and has escalated itself in due course of time owing to the advantages it provides to its service users, and also due to the unfolding ease, it renders to its customers. In recent times, especially because of pandemics, there has been a tremendous acceleration and dependency on the cloud environment and a sudden outburst in the increase of cloud users. However, it is significant to mention that the Cloud environment is quite susceptible to numerous attacks from intruders, thereby resulting in jeopardizing the system security and user privacy. Hence, one of the biggest challenges encountered by the researchers in the cloud domain is to propose algorithmic approaches or frameworks that ensure cloud security and maintain data integrity. Service users often hesitate to synchronize and store their data to cloud since they are not sure if their data privacy and confidentiality on the cloud environment will be preserved or compromised. This vulnerability of cloud systems eventually hinders cloud storage development. The objective of this paper is to project the trajectory towards maintaining privacy of the user data by assigning a restricted grant of permission to the user data stored on the cloud using cryptographic techniques. This work demonstrates an approach through which Identity validation along with Broadcast based Encryption mechanism aims to secure cloud data. This work depicts the functionality of various modules like set-up, encryption, and decryption processes, and the work is then finally compared with its peer research works based on the revocation rate.
Comparative Analysis of Image Enhancement Techniques for Chest X-ray Images

X-ray radiography plays a crucial part in diagnosis of various diseases in human body like Covid-19, Cancer and Pneumonia. The images obtained through X-ray radiography is interpreted by Surgeons, Pathologists and Radiologists for detecting anomaly in scanned body part. Chest X-ray is one of the cheapest and easily accessible tests of functioning of chest and lungs. However, images obtained through X-ray are not very clear, low in contrast and with lesser variation in gray level. Image enhancement is done for better visualization of images and bringing forward the underlying details of image. The Kaggle repository of total 6334 chest X-ray images were used for experimentation and calculation works. In this paper, we have compared various combinations of contrast enhancement techniques such as CLAHE, Morphological operations (black and white hat transforms) and noise reduction techniques like Median filter, DCT and DWT. The Comparison was done on the basis of image quality assessment parameters such as MSE, PSNR, and AMBE. The results showed that fusion of CLAHE and DWT techniques gave best results with highest PSNR value and lowest AMBE among the various models discussed. The proposed methodology shall be very helpful in diagnosis of diseases from chest X-ray images.
Encryption Algorithm using Matrix Manipulation

In this modern world we live, everything is connected with internet. To communicate and share the data uses underlying infrastructure and internet protocols. All the data that is being shared over internet need goes through layers of networks. Throughout the transmission process from one node to another node there can be many more access holes for unauthorized access or data breach just simply check that by hacking the connection medium. Thus data encryption is used before transmitting the data. There are various encryption method but in view highly connected, heterogeneous environment there is always thrust for improved algorithms with better performance. Encryption and authentication are two ways to prevent data unauthorized access and keeping data safe from attacks and prevent threats. To surge better approach for data security, in this research paper we propose a low complexity data encryption and decryption algorithm based on 256 random bits and matrix manipulation. The data that is being sent from one node to another node is encrypted using either a private key or a public key this algorithm uses a symmetric key encryption method to encrypt the data that is available on both ends of connection.
A Comparative Review between Modern Encryption Algorithms viz. DES, AES, and RSA

The Internet, a vast network of computer systems, currently connects the entire world. And maintaining safe communication and data sharing over these networks is a significant task. Cryptography is a critical aspect of information security in today’s world to make digital data storage and transfer safe. Cryptography is the process of disguising a message such that only the intended receivers can read it and hence provides privacy to the users. Because of the increased risk of digital cryptographic assaults, a slew of encryption methods is being developed to protect against them. This review paper presents a survey of various modern Encryption Algorithms.
Trash Can! An AI system for automatic classification of waste

Countries around the world struggle with the problem of waste recycling and management. This problem, if not addressed appropriately, poses serious consequences to the health and development of people globally. In this work an approach for a computer vision-based system which detects and classifies waste into various recyclable and non-recyclable categories is presented. It detects the objects present in an image frame and classifies them in their correct category- plastic, paper, metal, glass or trash (non-recyclable) with an accuracy of 67.4%. The system is designed as a highly portable system which can be used in almost all domestic or office settings. The detection is done through images captured by a Raspberry Pi 4 camera module with the help of a Raspberry Pi. The architectural model used for this task is the EfficientDet-Lite0 object detection model. This system can greatly help in increasing the amount of waste recycled and reducing the contamination of recycling appropriate materials.
Few-shot Electric Equipment Classification via Mutual Learning of Transfer-learning Model

Commonly, developing an accurate model to classify power equipment requires a host of equipment images. However, because of the security and sensitivity problems, the industry could provide a limited number of those images. It, therefore, raises a challenge: what makes a limited number of images effective to generate feature information for training the classification model. To address this challenge, this paper develops a Mutual Learning of Transfer-Learning (MLTL) method based on few-shot learning. Firstly, this paper utilized enhanced preprocessing on the image dataset, including rotation, random cropping, and normalization, to expand the original data. Secondly, two transfer-learning based models with different parameters are trained by the weighted sum of the cross-entropy loss and the self-supervised loss. The output of each model provides a regularization term for each other, which can be further improved by mutual learning between two models. Finally, given the K shots for each new class on the support set, the MLTL computers class weight vectors and make a prediction on the query set. Case studies on EEI-40 demonstrate that the developed MLTL method achieves 95.4% classification accuracy by collecting only 5-shot images. Additionally, to ensure the interpretability and reliability of the MLTL model, this paper visualizes the parameters of each convolution layer.
Knowledge Graph Construction for Electric Substation Based on Multiple-source Data

Knowledge graph (KG) is a database that stores the information and relationship between entities in the form of graph. This paper presents a knowledge graph construction method for grid substation knowledge graph based on multi-source data. The device connection, alarm information and ledgers are contained in KG, which benefits the substation operation and maintenance. First, entities and relationships are selected according to the actual composition of substation. Then, triples are extracted through domain dictionaries and conditional random field. Entity disambiguation is carried out before establishing the knowledge graph based on corresponding data sources. Finally, knowledge fusion is used to remove the redundancy in the database to complete the construction of the KG. Compared to traditional relational database, graph database can more intuitively show the connection relationship between devices. By adding more links to knowledge graph, it takes less time for monitoring personnel to locate accidents when dealing with alarm information.
A provable secure signature in the quantum random oracle model

With the development of quantum computation, quantum computers will break the almost classical cryptography. It is imminent to design post-quantum cryptography algorithm that could resist quantum adversaries. Lattice-based problem has been verified that could resist quantum attacks. Our goal is to design a signature and try to prove it is secure in the quantum random oracle model. Our new signature scheme is redesigned based on the signature proposed by Lyu, but that scheme only proved secure in the random oracle model. The difference between the two signature is that we introduce the middle-product operation. So our signature is based on middle-product LWE hard assumption, which enjoys a security proof from PL WE. Then we combine the secure chameleon hash function and our new scheme, prove that our new combined signature is still secure in the quantum random oracle model.
Redactable Blockchain Using Lattice-based Chameleon Hash Function

Blockchain as a tamper-proof, non-modifiable and traceable distributed ledger technology has received extensive attention. Although blockchain's immutability provides security guarantee, it prevents the development of new blockchain technology. As we think, there are several arguments to prefer a controlled modifiable blockchain, from the possibility to cancel the transaction and necessity to remove the illicit or harmful documents, to the ability to support the scalability of blockchain. Meanwhile, the rapid development of quantum technology has made the establishment of post-quantum cryptosystems an urgent need. In this paper, we put forward the first lattice-based redactable consortium blockchain scheme that makes it possible to rewrite or repeal the content of any blocks. Our approach uses a consensus-based election and lattice-based chameleon hash function (Cash and Hofheinz etc. EUROCRYPT 2010). With knowledge of secret trapdoor, the participant could find the hash collisions efficiently. And each member of the consortium blockchain has the right to edit the history.
A Blockchain Consensus Algorithm Based on Node Random Number Calculation

This paper proposes a new blockchain consensus algorithm. The basic principle of the algorithm is: firstly, select the function that maintains the randomness of the input variables, and then generate a random number for each node participating in the consensus calculation. Then, take these random numbers as variables to calculate the function value, and select a node with the most setting relationship between the random number and the function value as the accounting node. The algorithm realizes the random determination of the accounting node of the blockchain.
SRRS: A Blockchain Fast Propagation Protocol Based on non-Markovian Process

With the widespread use of Bitcoin, more demands on the underlying blockchain technology have been raised for more efficient transactions. Thus, in the last decade, many solutions have been proposed to improve the propagation of blocks. Motivated by the social reinforcement rumor spreading (SRRS), we propose a blockchain fast propagation protocol based on a non-Markovian process, which refers to that the verification and transmission of a block also depends on its propagation history. The simulated experimental results show that our protocol can significantly accelerate the propagation of blocks. Compared with existing major solutions, our protocol does not change the main procedure of Bitcoin and the block data structure. This is a huge advantage for practical applications. Moreover, employing the theories of complex networks and percolation, we develop a method that presents a theoretical analysis for our protocol, which can find the optimal setting of our protocol.
Medical Data Storage Model Based on an Alliance Chain

Aiming at the problems of centralized storage, low sharing efficiency, and the security and privacy of traditional medical data, a medical data storage model based on a consortium chain is proposed. First, the Distance algorithm is designed based on the geographical relationship of nodes, which reduces the amount of communication between nodes, improves the communication efficiency between A nodes, and ensures the efficiency and reliability of grouping. Second, the dynamic election is combined with the Distance algorithm to design the election strategy of the primary node, which realizes the scientific and reasonable ranking of the medical nodes, improves the reliability of the selection of the primary node, and ensures the efficiency of the medical nodes in the alliance chain to quickly reach consensus when the medical data are uploaded to the chain. Finally, the model designs a method of information separation, which combines the distributed database IPFS (InterPlanetary File System) with the blockchain, which greatly reduces the pressure of medical data storage in the blockchain and improves the operation efficiency. The experimental results show that the medical data storage model can effectively improve the problem of the sharp increase in the amount of communication between medical nodes in the network and reduce the impact of malicious consensus nodes.
Quantum Steganography Based on the B92 Quantum Protocol

This paper presents a communication algorithm in which a grayscale image, shared between two parties, can be used to transmit a secret message, by applying the idea introduced in the B92 quantum protocol. The least significant qubits of the pixels’ representation in certain regions of the image are used. With the help of a server, the algorithm generates a random message, which can further act as a secret key for cryptographic algorithms in order to secure the data that two parties might want to exchange later on. The chosen representation of the image is NEQR (novel enhanced quantum representation) and the platform used for testing the described idea is IBM Quantum Experience, along with the open-source Python framework called Qiskit. This solution allows users to design, implement quantum circuits (containing various quantum gates), and simulate them using real devices and local simulators. An implementation using this platform for a sample image and the corresponding results are also presented in this paper.
Drawn into the future: The epistemic work of visual scenarios in the configuration of human–robot encounters

This article looks at artistic impressions of future robotics and considers how they inspire research into human–machine interaction. Our analysis of visual scientific practices and the epistemic ramifications of these speculative drawings emerges from a long-term participant observation study in a multi-disciplinary project on smart and autonomous technologies in public spaces. We discuss the design, appropriation and modulation of visual scenarios, and scrutinize how these diegetic futurescapes are imaginatively engaging and suggestive of scientific progress and experimentation. The article argues that the future-oriented scenes defy common notions of post hoc scientific representations. Instead, they are ex ante presentations of the ambition to imagine human–machine relations in the future and to draw the large-scale research venture together. The register of evaluation thereby shifts from aesthetic criteria to scientific parameters. More than just visual tokens, the scenarios became a catalyst for collaboration.
Photovoltaic power intra‐ and day‐ahead predictions with differential learning producing <scp>PDE</scp>‐modular models based on the node L‐transform derivatives

Abstract Predictions of photovoltaic (PV) energy supply, based on data statistics of weather and PV records, are required in short‐intra and day‐ahead planning of PV plant operations. Numerical weather prediction (NWP), based on physical consideration, can simulate the progress of local cloudiness, although their prognoses are usually delayed by a few hours and are not provided with the quality desired by PV operators. Differential polynomial neural network (D‐PNN) is an unconventional hybrid regression technique, based on a novel learning strategy and able to resolve some problems in modeling patterns in high‐dynamical and chaotic non‐deterministic systems. D‐PNN splits the general partial differential equation (PDE) of the k th order into a summation form of two‐variable PDEs of a predetermined low order. PDE derivatives are transformed and separated into the Laplace counterparts, from which the unknown node originals are restored using the inverse L‐operation. D‐PNN searches for the best 2‐input couples, inserting node by node into its changeable tree structure, to expand the modular sum model by usable PDE components. The PDE‐modularization enhances the D‐PNN capability in modeling high uncertainty in weather patterns in the ground‐layer atmosphere, analogous to NWP. The designed PV intra‐ and day‐ahead prediction schemes, using hourly increasing and fixed‐time prediction horizons, were compared with different computing approaches based on differential, probabilistic and statistical learning. Machine learning (ML) models calculate the output PV power in each single hour separately or in an all‐day processing series with trained input delay. The results are analyzed and interpreted for each day and prediction hour. The quantitative results for both prediction procedures are comparable, showing the average intraday 11.6% and all‐day 9.6% (or rather 12.3% respectively) inaccuracy with respect to the daily peak power. Training data samples with spatial overlap in the regional scope and ML initialized similarity intervals are related to the fixed‐day or hourly increased input–output delay. This approach can compensate for unpredictable midterm variances in local weather patterns (caused by terrain or convection anomalies) and possible errors in model initialization. PV power was converted to and restored from the clear sky index in the modeling input/output, which represents its relative values, disregarding the actual solar cycle time.
Internal Resource Allocation to Information Security of Smart Cities Using Evolutionary Game Model

The internal resource allocation approach is proposed for the information security of smart cities using the evolutionary game model. Focusing on the information resource allocation of smart cities, the relationship among information security factors is first analyzed, then the directed connection graph and adjacency matrix are constructed to obtain its directed hierarchical structure diagram by calculating the reachable matrix, thereby developing an explanatory structure model for information security resources in smart cities. Furthermore, an evolutionary game model is established, and the replicator dynamics are used to adjust the model. Finally, three types of typical problems are investigated through simulation experiments to verify and explain the structural model.