Reviewerly/exHarmony huggingface dataset:
Self-imitation guided goal-conditioned reinforcement learning

Goal-conditioned reinforcement learning (GCRL) aims to control agents to reach desired goals, which poses a significant challenge due to task-specific variations in configurations. However, current GCRL methods suffer from limitations in sample efficiency and the need for substantial training data. While existing self-imitation-based GCRL approaches can improve sample efficiency, their scalability to large-scale tasks is limited. In this paper, we propose integrating self-imitation learning with goal-conditioned RL methods into a compatible and reasonable framework. Specifically, we introduce a novel target action value function to aggregate self-imitation learning and goal-conditioned reinforcement learning. The designed target value effectively combines these two policy training mechanisms to accomplish specific tasks. Moreover, we theoretically demonstrate that our approach can learn a superior policy compared to both self-imitation learning and goal-conditioned reinforcement learning. Additionally, experimental results showcase the stability and effectiveness of our method compared to existing approaches in various challenging robotic control tasks.
Anomaly detection with a container-based stream processing framework for Industrial Internet of Things

Online anomaly detection is a key challenge for industrial internet of things (IIoT) applications, as anomalies may occur in data streams from sensors and cause losses or damages. However, most existing methods for online anomaly detection have limitations in efficiency, effectiveness and timeliness, especially with the massive and distributed data streams from IIoT devices. Therefore, developing a data stream processing framework to discover anomalies in time and ensure the proper operation of the system is an urgent issue for IIoT. In this paper, we propose a flexible stream processing framework that enables online anomaly detection for IIoT applications. The framework exploits a distributed computing architecture based on docker containers to improve flexibility, migration capability and customisation. The framework also uses a central mediator to coordinate data stream processing tasks running on different docker nodes. Moreover, we develop a prediction-based online anomaly detection model that consists of batch model training and data stream anomaly detection processes. The model uses long short-term memory (LSTM) neural networks to predict data stream values and a dynamic sliding window method to model prediction errors and detect anomalies. We implement a case study to detect abnormal heating temperatures from an industrial heating plant and evaluate the performance of the proposed framework and anomaly detection model. The results show that our framework and model can achieve high accuracy and low latency in detecting anomalies, and they outperform existing methods in terms of scalability, efficiency and adaptability for IIoT applications.
UnTDIP: Unsupervised neural network for DEM super-resolution integrating terrain knowledge and deep prior

Digital Elevation Models (DEMs) are essential for comprehending the three-dimensional (3D) structure of the Earth’s surface. When the resolution of airborne or satellite sensors fail to meet the requirements for high-precision terrain observation, super-resolution methods offer an alternative solution, which infers the high-resolution DEMs from coarse-resolution DEMs. This study aims to create an unsupervised DEM super-resolution method that utilizes untrained neural networks, integrating both deep prior from network structure and terrain knowledge, namely UnTDIP. Specifically, the untrained neural network iteratively processes a single DEM, and the deep prior from network structure learns low-order statistical information from the DEM that is then applied to the super-resolution task. Furthermore, the terrain knowledge for topographic consistency is integrated into the iterative process and synergies with the deep prior to reconstructing the DEM with the correct 3D structure. Most neural network-based DEM super-resolution methods are data-driven, while the proposed method does not require any training datasets or pre-trained network parameters, making it broadly applicable. The UnTDIP method was evaluated in three study areas with different spatial resolutions of 5 m, 10 m, and 30 m. The method performs well in multiple scale factors and spatial resolutions, even surpassing the fully supervised DEM super-resolution method in some evaluation metrics. Moreover, the integration of terrain knowledge reduces the Mean Absolute Error (MAE) by 6.08 % to 34.65 %. Overall, the proposed method is well-suited for DEM data and can be adapted for super-resolution tasks for other geospatial data with the integration of domain knowledge.
Multi-layer collaborative optimization fusion for semi-supervised learning

Recently, the Cooperative Training Algorithm (CTA), a well-known Semi-Supervised Learning (SSL) technique, has garnered significant attention in the field of image classification. However, traditional CTA approaches face challenges such as high computational complexity and low classification accuracy. To overcome these limitations, we present a novel approach called Weighted fusion based Cooperative Training Algorithm (W-CTA), which leverages the cooperative training technique and unlabeled data to enhance classification performance. Moreover, we introduce the K-means Cooperative Training Algorithm (Km-CTA) to prevent the occurrence of local optima during the training phase. Finally, we conduct various experiments to verify the performance of the proposed methods. Experimental results show that W-CTA and Km-CTA are effective and efficient on CIFAR-10 dataset.
Higher-order games with dependent types

In previous work on higher-order games, we accounted for finite games of unbounded length by working with continuous outcome functions, which carry implicit game trees. In this work we make such trees explicit. We use concepts from dependent type theory to capture history-dependent games, where the set of available moves at a given position in the game depends on the moves played up to that point. In particular, games are modelled by a W-type, which is essentially the same type used by Aczel to model constructive Zermelo-Frankel set theory (CZF). We have also implemented all our definitions, constructions, results and proofs in the dependently-typed programming language Agda, which, in particular, allows us to run concrete examples of computations of optimal strategies, that is, strategies in subgame perfect equillibrium.
Analysis of multiplication algorithms in Galuis fields for the cryptographic protection of information

The mathematical basis for processing a digital signature is elliptic curves. The processing of the points of an elliptic curve is based on the operations performed in the Galois fields GF(pm). Fields with a simple foundation are not well-studied and very interesting for research. In this paper, a comparison of the complexity of algorithms for the realization of the multiplication operation in Galois fields GF(pm) with different bases is carried out. Conducts a comparison of the 3 most common multiplication algorithms. Found that fields with a base greater than 2 will have greater complexity of the algorithm.
Deep Learning-based Anomaly Detection for 5G Core Mobility Management

Machine learning is becoming an increasingly critical tool in next-generation telecommunications ecosystems. Effective anomaly detection tools are more necessary than ever as mobile solutions continue to become more complex, with an array of features designed to enhance network capabilities. This article presents a novel approach to detect anomalies and forecast traffic for 5G core networks, aiming to prevent severe outages and reduce traffic impact, especially for mission-critical services. By collecting 5G network functions (NFs) metrics, we developed an unsupervised learning model utilizing Autoencoder architecture with bidirectional LSTMs. Our experiments demonstrate the effectiveness of this technique on a 5G network, showing promising potential for future applications.
Enhancing the Privacy of Machine Learning via faster arithmetic over Torus FHE

The increased popularity of Machine Learning as a Service (MLaaS) makes the privacy of user data and network weights a critical concern. Using Torus FHE (TFHE) [1] offers a solution for privacy-preserving computation in a cloud environment by allowing computation directly over encrypted data. However, software TFHE implementations of cyphertext-cyphertext multiplication needed when both input data and weights are encrypted are either lacking or are too slow. This paper proposes a new way to improve the performance of such multiplication by applying carry save addition. Its theoretical speedup is proportional to the bit width of the plaintext integer operands. This also speeds up multi-operand summation. A speedup of 15x is obtained for 16-bit multiplication on a 64-core processor, when compared to previous results. Multiplication also becomes more than twice as fast on a GPU if our approach is utilized. This leads to much faster dot product and convolution computations, which combine multiplications and a multi-operand sum. A 45x speedup is achieved for a 16-bit, 32element dot product and $\mathrm{a}\sim 30\mathrm{x}$ speedup for a convolution with a 32x32 filter size.
A New Multi-Feature Recommendation Model Based on Recurrent Neural Network

With the problems of handling single-feature and overlooking user preferences in the recommendation algorithms, this paper proposes a Recurrent Neural NetWork-based Multi-feature Hybrid Recommendation Model (RN-MHRM). Firstly, features are extracted from user-item interaction data using the Latent Factor Model (LFM), and an improved Recurrent Neural NetWork (RNN) is used to replace the linear inner product of LFM vectors With non-linearity, Which aims at learning richer features that capture user's short-term interests. Secondly, to avoid single-feature, item information is introduced and the BERT model is used for extracting multi-features. Thirdly, both short-term and long-term interests are considered, and the user's long-term interests are trained by a FeedforWard Neural NetWork (FNN), Which greatly improves the recommendation performance. Experiments designed on multiple real datasets have shown that RN-MHRM effectively improves recommendation performance compared to the baseline model.
Utilizing Machine Learning to Develop Cloud-Based Apprenticeship Programs Aligned with Labor Market Demands

There is a potential disparity between academic pursuits and labor market requirements. We proposed an approach exploring the feasibility of leveraging machine learning to tailor apprenticeship programs and enhance learning outcomes. The proposed approach involves the collaboration of four key stakeholders, namely, the employer, trainer, university management, and apprentice, each with unique roles in the apprenticeship program. A machine learning algorithm is employed to customize Occupational Learning Outcomes (OLO) for each job, with the use of blockchain technology to facilitate the student credit system. The entire system is hosted on a cloud-based centralized database to enable dynamic and sustainable program modification. The paper concludes by highlighting the potential of digital technologies to transform apprenticeships and create new opportunities and challenges.
Security sharing of smart city communication data based on blockchain technology

When the smart city communication data is safely shared, the sharing process is mainly realized through single authorization attribute base encryption, and the key and ciphertext cannot be updated in time, which makes the encryption and decryption process in the data security sharing process take long. Therefore, relying on the blockchain attribute encryption algorithm, a new smart city communication data security sharing method is designed. Relying on the principle of attribute base encryption, the data encryption algorithm is designed to realize the encryption and decryption of smart city communication data. In chronological order, all encrypted data are stored on the block sharing chain, and the corresponding keys and ciphertext are quickly generated by the request of multiple nodes participating in joint computing. Finally, through the OBDD (orderly binary decision chart) access structure, the data on the sharing chain is efficiently accessed to realize the secure sharing of communication data. The experimental results show that when the proposed method is applied to share the data in smart city communication, the average time of data encryption and decryption is 1. 9s,0.2s, which greatly reduces the cost of data security sharing.
A Simple Completely Adjacency List Oriented Relational Extraction Model

Entity relationship extraction aims to extract important triplet information from massive unstructured data, which is the basis of downstream tasks such as building a knowledge map. The graph perspective is used to analyze the entity and relationship extraction and build adjacency list oriented model to solve the problem of large space consumption of the adjacency matrix, but it uses complex operations to extract entities and relationships sequentially. Therefore, we propose a simple completely adjacency list oriented relationship extraction model. This model firstly introduce a realtion label-aware module to supplement sentence information and a feature separation module to alleviate the error accumulation problem caused by sequential extraction, and then sequentially extracts subjects, objects, and relations. Extensive experiments on two common datasets have shown that our model maintains high accuracy of 92.8while also significantly improving inference speed down from 35.7ms to 22.9ms.
A Systematic Review on Detection of Manipulated Satellite Images

The widespread use of image editing software and the development of image processing technology have made manipulated images very easy. Image forgery, especially in remote sensing satellite images, may bring incalculable and serious consequences, which makes researchers focus on how to verify the integrity of remote sensing images. This paper introduces remote sensing images and manipulating operations in remote sensing images, mainly splicing operations. After that, we discuss methods in depth for detecting and locating manipulation in remote sensing images in recent years. The generative model reflects its unparalleled advantages in these methods. Finally, the future development direction is prospected.
Smart System using Blockchain for Medical Diagnosis

Blockchain is a technology that is currently widespread in many fields, it is used to transmit information between two or more parties in a secure way, without the possibility of data loss or interception by an unwanted third party. A blockchain network represents the creation of innovative solutions for the healthcare system, as patient data can be exchanged across hospitals, diagnostic labs, pharmacy firms and doctors. In this paper the topic of blockchain technology and its significant benefits in the field of medical diagnostics is addressed. With the proposed solution a misdiagnosis can be avoided, as there is the possibility to ask for other opinions from other health professionals. The highest level of security information storage allows authorised doctors to access your data from any geographical location, with no territorial limitations. Provides fast, versatile interconnection and enhanced security of patient information. In conclusion, this technology can be incorporated into the current medical system.
An Improved Apriori Algorithm Based on Transaction Sequence Counting

Apriori algorithm has the advantages of simplicity and easy realization, which can realize the search for some association between data. However, it also has unavoidable flaws. The traditional serial Apriori algorithm has the disadvantages of frequent scanning database, generating large amount of candidate item-set data and consuming a lot of memory resources. This paper proposes some technologies based on association principle, and proposes an improved Apriori algorithm to overcome the shortcomings of Apriori algorithm. When dealing with transactions, the algorithm filters out transactions that cannot generate frequent item-set, which can greatly reduce the amount of data processing. Theoretical analysis and experimental results show that the proposed scheme has the best performance in efficiency and stability.
Performance Evaluation of Block Size Influence on Blockchain-Enabled IoT Data Storage

The increasing number of IoT devices has led to a significant growth in the number of generated data packets that require secure storage and processing solutions. Blockchain technology offers potential benefits for storing IoT data, leveraging its decentralized, distributed, and immutable nature. In a blockchain, the data provided by IoT sensors is stored as transactions within data blocks that form the blockchain network. However, the block size plays a crucial role in ensuring the security and scalability of IoT applications. To address this, our research paper focuses on determining the optimal block size for storing information from IoT sensors. By carefully selecting the optimal block size, we aim to address the unique requirements of IoT applications, ensuring their security and scalability while leveraging the potential of blockchain technology for securely storing and processing IoT data packets.
A Terminal Device Authentication Scheme Based on Blockchain Technology in WBAN

The wireless body area network (WBAN) plays a vital role in the application of smart health care. However, due to the openness of wireless networks, the potential threats posed by attackers increase concerns about the security and privacy of patient data. In this paper, we combine blockchain technology with WBAN and propose a lightweight terminal device authentication scheme based on blockchain technology suitable for WBAN. This scheme uses physiological information combined with a limited number of hash operations and XOR operations to share session keys during authentication, ensure confidentiality, achieve cross-domain authentication, and improve authentication efficiency. Finally, the safety analysis shows that the scheme achieves a better safety.
Soccer Pitch Areas Segmentation with Hierarchical U-Net on the SoccerNet Dataset

Soccer video analysis is a challenging area of research in computer vision. Several soccer video analysis systems exist for tasks such as player detection and tracking, player performance analysis or team behaviour analysis. These systems are composed of several building blocks, such as image classifiers for shot classification, object detectors for player and ball detection, or object trackers for player and ball tracking. Another useful building block is an image semantic segmentation module, which may be used to segment different elements in the frame. In our setting, it is used to segment the different areas of the soccer field. This paper describes a semantic segmentation network, that segments the 10 different areas of the soccer pitch. This work builds on our previous work for soccer field line segmentation. Our current method directly segments the areas in the image, using a Deep Learning Convolutional Neural Network, based on U-Net, with hierarchical outputs, and balanced or unbalanced loss weights. The hierarchical output contains four outputs with segmentation masks for different segmentation tasks, arranged in a hierarchical tree. Balanced or unbalanced loss weights allow the system's training to be more influenced or less influenced by the accuracy of a particular output. Our best model produces visually convincing results, and is able to achieve an Average Precision of 80.4%, Average Recall of 73.1%, Average F-score of 76.1%, Average Accuracy of 95.1%, and Average IoU of 62.4%.
Research on Application of Generative Adversarial Neural Network in Image Restoration

In recent years, more and more researchers use deep learning to process inpainting tasks. Among them, the use of generation countermeasure network to process inpainting tasks has become more and more popular and has achieved good results. However, there are still issues with blurry repair results and unsmooth structure. In this paper, we propose a method of inpainting based on u-net structure for generation adversarial network, the first two layers of our encoder use multi-scale shallow feature extraction modules (MSFEM) to extract lowdimensional texture and structural information. We introduce multi-scale spatial attention module (MSAM) into skip connections to obtain more shallow features and improve repair performance. The decoder uses improved dense convolutional blocks to fully utilize and extract feature information. The experiment used two datasets, CelebA and Palace2, through experiments, the repair effect of our proposed method is better than the state-of-the-art image inpainting approaches.
A Secret and Traceable Approach for Cloud Data Sharing

With the development of cloud computing, data owners generally use cloud services to reduce storage and computing overhead. However, data stored in cloud servers is out of the direct control of the data owners, causing serious security issues. Access control mechanism based on cryptography can effectively protect the security of cloud data and prevent unauthorized access to it. Nevertheless, users may redistribute data to other users after obtaining it, which can harm the copyright interests of the data owner. To address this issue, this paper proposes a secret and traceable approach for cloud data sharing. We combine the lattice-based proxy re-encryption with digital watermarking technology for redistribution tracking in cloud data sharing scenario. The lattice cipher used in this scheme is an encryption algorithm with homomorphic property based on the Ring-LWE problem. It has the advantage of reducing the ciphertext length and improving the efficiency of encryption. This paper demonstrates the feasibility and effectiveness of this scheme from a series of experiments. The encryption of an image of size 1024*1024 pixels takes only 9.78 seconds on average. The computational performance of this scheme is roughly 91.434% better than the existing data sharing schemes supporting redistribution tracking.
Security Considerations at the Service Level Agreement in Cloud Computing Services

The objective of this research is to highlight the significance of ensuring Service Level Agreement (SLA) security to address the low adoption rates of Cloud Computing services in the economy. The initial segment of this study addresses the limitations hindering the widespread adoption of cloud services, providing statistical data on adoption rates across various countries and industries. Subsequently, the concept of SLA is introduced, focusing on its role as the governing document in the customer-provider relationship within cloud computing. Furthermore, the key criteria for developing an effective SLA are discussed, emphasizing the need for security measures to instill confidence in the provided services. Lastly, a set of metrics is proposed to enhance trust in cloud services and can be incorporated into SLA documents. These metrics encompass aspects such as availability and maintenance time, the range of services and resources offered to customers, application response time, advance notification schedule for network changes impacting users, help desk response time for different issue categories, and the repercussions in case of agreement breaches.