Reviewerly/exHarmony huggingface dataset:
Smart Contract Design in Distributed Energy Systems: A Systematic Review

Blockchain technology and, in particular, smart contracts based on it, offers a new, decentralized mechanism for entering into and fulfilling contracts in diverse markets. Energy markets are no exception, and indeed, the decentralized nature of the blockchain may be particularly important for them as the penetration of residential prosumers offering microgeneration to the grid grows. At this time, however, the literature on smart contracts in energy markets—and particularly their interaction with the technical infrastructure of the smart grid—is limited and scattered. There is a need to consolidate these studies into a comprehensive understanding of the state-of-the-art in smart contract design for the smart grid. However, no existing reviews focus on smart contracts in energy systems. The scope of our study is the role of smart contracts in energy systems and what limitations they encounter. We conduct a systematic review of this topic, focusing on systems that have been implemented as prototypes. These studies provide key evidence on the scalability of smart contracts for energy systems and their interaction with the technical elements of the smart grid. We selected a pool of 76 papers meeting our criteria, with three others excluded for misinterpreting fundamental aspects of blockchains and smart contracts. After reviewing each paper, we found that this literature falls into four categories: market operations, ancillary services, auditing and monitoring, and cybersecurity. We then identify and examine the cross-cutting concerns of data storage in and interoperability between blockchains. We finally discuss the implications of our findings for future research. In particular, there is likely to be a complex interplay between the data generated and stored via the blockchain versus the data required to meet energy system reliability targets and market obligations for participants.
BiGA-YOLO: A Lightweight Object Detection Network Based on YOLOv5 for Autonomous Driving

Object detection in autonomous driving scenarios has become a popular task in recent years. Due to the high-speed movement of vehicles and the complex changes in the surrounding environment, objects of different scales need to be detected, which places high demands on the performance of the network model. Additionally, different driving devices have varying performance capabilities, and a lightweight model is needed to ensure the stable operation of devices with limited computing power. To address these challenges, we propose a lightweight network called BiGA-YOLO based on YOLOv5. We design the Ghost-Hardswish Conv module to simplify the convolution operations and incorporate spatial coordinate information into feature maps using Coordinate Attention. We also replace the PANet structure with the BiFPN structure to enhance the expression ability of features through different weights during the process of fusing multi-scale feature maps. Finally, we conducted extensive experiments on the KITTI dataset, and our BiGA-YOLO achieved a mAP@0.5 of 92.2% and a mAP@0.5:0.95 of 68.3%. Compared to the baseline model YOLOv5, our proposed model achieved improvements of 1.9% and 4.7% in mAP@0.5 and mAP@0.5:0.95, respectively, while reducing the model size by 15.7% and the computational cost by 16%. The detection speed was also increased by 6.3 FPS. Through analysis and discussion of the experimental results, we demonstrate that our proposed model is superior, achieving a balance between detection accuracy, model size, and detection speed.
An incentive mechanism for horizontal federated learning based on principle of compound interest

Federated Learning (FL) enables training a global model without sharing the decentralized raw data stored on multiple devices to protect data privacy. In order to enhance the robustness of federated learning, it is essential to provide corresponding returns to users to stimulate high quality users participating in the learning process as long as possible. Available incentive mechanisms for federated learning did not take unreliable and malicious users into account. As a result, these schemes will not only pay high quality participators, but also make malicious and unreliable participators get benefits. In this paper, we propose an incentive mechanism for horizontal federated learning system. In order to motivate high-quality users to participate in federated learning for a long time, we adopt the principle of compound interest to design the incentive strategy. Meanwhile, with our incentive mechanism, the malicious user will not obtain more in return than it pays, so that this kind of users can be inhibited from participating in learning. Experimental results demonstrate the effectiveness of the proposed scheme. Without exceeding the task publisher’s budget, the higher the accuracy of the local model a user provided, and the longer its participation time, the higher its yield. In addition, with the proposed incentive mechanism, the participation rate of malicious users decreased by 65%.
PreBit — A multimodal model with Twitter FinBERT embeddings for extreme price movement prediction of Bitcoin

Bitcoin, with its ever-growing popularity, has demonstrated extreme price volatility since its origin. Extreme price fluctuations have been known to occur due to tweets from Elon Musk, Michael Saylor, and others. In this paper, we aim to investigate whether we can leverage Twitter data to predict these extreme price movements. Existing social media models often take a shortcut and include sentiment extracted from tweets. In this work, however, we want to embed the actual tweets in a domain-informed way, and investigate whether they have an impact. Hence, we propose a multimodal deep learning model for predicting extreme price fluctuations that takes as input candlestick data, prices of a variety of correlated assets, technical indicators, as well as Twitter content. To train the model, a new dataset of 5,000 tweets per day containing the keyword 'Bitcoin' was collected from 2015 to 2021. This dataset, called PreBit, is made available online1, as is our model.2 Our proposed hybrid multimodal model consists of an SVM model based on price data, which is fused with a text-based Convolutional Neural Network. In the text-based model, we use the sentence-level FinBERT embeddings, pretrained on financial lexicons, so as to capture the full contents of the tweets and feed it to the model in an understandable way. In an ablation study, we explore whether adding social media data from the general public on Bitcoin improves the model's ability to predict extreme price movements. Finally, we propose and backtest a trading strategy based on the predictions of our models with varying prediction threshold and show that it can be used to build a profitable trading strategy with a reduced risk over a 'hold' or moving average strategy.
Research on an unsupervised person re-identification based on image quality enhancement method

Research on person re-identification(Re-ID) has important value in pedestrian detection, target tracking, criminal investigation, and other related fields. In unsupervised pedestrian recognition algorithms, the accuracy of pseudo-labels is crucial to the recognition results. However, in practical scenarios, low-quality images caused by factors such as differences in camera resolution and shooting angles can affect the extraction of pedestrian features by these algorithms, thereby negatively impacting the accuracy of the labels and the learning process of the model. To address this problem, we propose an image quality enhancement algorithm for unsupervised person Re-ID (IQE). To the best of our knowledge, this study is the first to introduce detail enhancement and the application of low-light enhancement algorithms into unsupervised person Re-ID. By improving the feature extraction quality based on these two aspects, higher-quality pseudo-labels can be constructed. This method improves the accuracy of feature extraction and clustering, thereby increasing the accuracy of pseudo-labels and reducing the interference of noisy pseudo-labels. The experimental results showed that the IQE method outperformed state-of-the-art person Re-ID methods in terms of Rank-1 accuracy and mAP. Specifically, IQE achieved an 87.9% rank-1 accuracy and a 71.2% mAP on the Market-1501 dataset; a 78.1% rank-1 accuracy and a 61.7% mAP On the DukeMTMC-reID dataset; and a 51.1% rank-1 accuracy and 24.2% mAP on the MSMT17 dataset.
Efficient Verifiable Protocol for Privacy-Preserving Aggregation in Federated Learning

Federated learning has gained extensive interest in recent years owing to its ability to update model parameters without obtaining raw data from users, which makes it a viable privacy-preserving machine learning model for collaborative distributed learning among various devices. However, due to the fact that adversaries can track and deduce private information about users from shared gradients, federated learning is vulnerable to numerous security and privacy threats. In this work, a communication-efficient protocol for secure aggregation of model parameters in a federated learning setting is proposed where training is done on user devices while the aggregated trained model could be constructed on the server side without revealing the raw data of users. The proposed protocol is robust against users’ dropouts, and it enables each user to independently validate the aggregated result supplied by the server. The suggested protocol is secure in an honest-but-curious environment, and privacy is maintained even if the majority of parties are in collusion. A practical scenario for the proposed setting is discussed. Additionally, a simulation of the protocol is evaluated, and results demonstrate that it outperforms one of the state-of-art protocols, especially when the number of dropouts increases.
Three Dimensional Shape Reconstruction via Polarization Imaging and Deep Learning

Deep-learning-based polarization 3D imaging techniques, which train networks in a data-driven manner, are capable of estimating a target's surface normal distribution under passive lighting conditions. However, existing methods have limitations in restoring target texture details and accurately estimating surface normals. Information loss can occur in the fine-textured areas of the target during the reconstruction process, which can result in inaccurate normal estimation and reduce the overall reconstruction accuracy. The proposed method enables extraction of more comprehensive information, mitigates the loss of texture information during object reconstruction, enhances the accuracy of surface normal estimation, and facilitates more comprehensive and precise reconstruction of objects. The proposed networks optimize the polarization representation input by utilizing the Stokes-vector-based parameter, in addition to separated specular and diffuse reflection components. This approach reduces the impact of background noise, extracts more relevant polarization features of the target, and provides more accurate cues for restoration of surface normals. Experiments are performed using both the DeepSfP dataset and newly collected data. The results show that the proposed model can provide more accurate surface normal estimates. Compared to the UNet architecture-based method, the mean angular error is reduced by 19%, calculation time is reduced by 62%, and the model size is reduced by 11%.
Lessons from crypto assets for the design of energy efficient digital currencies

This paper considers the energy profile of the main components and technological options of digital currencies, including crypto assets. Digital currencies differ widely in energy consumption depending on how they are designed. We draw on academic, institutional, and industry estimates of the energy use resulting from the processing and settlement of payments to compare digital currencies to each other and to existing payment systems. Results show that, depending on their designs, digital currencies have the potential to aid in reducing energy consumption compared to the current payment system. Energy efficient designs limit payment system actors and redundancies. For distributed ledger technologies (DLT), the key factors affecting energy consumption are the ability to control participation and the consensus algorithm. Estimates for prospective non-DLT digital currencies match the lowest energy consumption of the DLT-based designs. We discuss the relation between these findings and the energy profile of potential Central Bank Digital Currencies (CBDC) that are under consideration.
Towards rigorous understanding of neural networks via semantics-preserving transformations

Abstract In this paper, we present an algebraic approach to the precise and global verification and explanation of Rectifier Neural Networks , a subclass of Piece-wise Linear Neural Networks (PLNNs), i.e., networks that semantically represent piece-wise affine functions. Key to our approach is the symbolic execution of these networks that allows the construction of semantically equivalent Typed Affine Decision Structures (TADS). Due to their deterministic and sequential nature, TADS can, similarly to decision trees, be considered as white-box models and therefore as precise solutions to the model and outcome explanation problem. TADS are linear algebras, which allows one to elegantly compare Rectifier Networks for equivalence or similarity, both with precise diagnostic information in case of failure, and to characterize their classification potential by precisely characterizing the set of inputs that are specifically classified, or the set of inputs where two network-based classifiers differ. All phenomena are illustrated along a detailed discussion of a minimal, illustrative example: the continuous XOR function.
Decision-making under uncertainty: beyond probabilities

Abstract This position paper reflects on the state-of-the-art in decision-making under uncertainty. A classical assumption is that probabilities can sufficiently capture all uncertainty in a system. In this paper, the focus is on the uncertainty that goes beyond this classical interpretation, particularly by employing a clear distinction between aleatoric and epistemic uncertainty. The paper features an overview of Markov decision processes (MDPs) and extensions to account for partial observability and adversarial behavior. These models sufficiently capture aleatoric uncertainty, but fail to account for epistemic uncertainty robustly. Consequently, we present a thorough overview of so-called uncertainty models that exhibit uncertainty in a more robust interpretation. We show several solution techniques for both discrete and continuous models, ranging from formal verification, over control-based abstractions, to reinforcement learning. As an integral part of this paper, we list and discuss several key challenges that arise when dealing with rich types of uncertainty in a model-based fashion.
Machine Learning Algorithms to Predict Breast Cancer Recurrence Using Structured and Unstructured Sources from Electronic Health Records

Recurrence is a critical aspect of breast cancer (BC) that is inexorably tied to mortality. Reuse of healthcare data through Machine Learning (ML) algorithms offers great opportunities to improve the stratification of patients at risk of cancer recurrence. We hypothesized that combining features from structured and unstructured sources would provide better prediction results for 5-year cancer recurrence than either source alone. We collected and preprocessed clinical data from a cohort of BC patients, resulting in 823 valid subjects for analysis. We derived three sets of features: structured information, features from free text, and a combination of both. We evaluated the performance of five ML algorithms to predict 5-year cancer recurrence and selected the best-performing to test our hypothesis. The XGB (eXtreme Gradient Boosting) model yielded the best performance among the five evaluated algorithms, with precision = 0.900, recall = 0.907, F1-score = 0.897, and area under the receiver operating characteristic AUROC = 0.807. The best prediction results were achieved with the structured dataset, followed by the unstructured dataset, while the combined dataset achieved the poorest performance. ML algorithms for BC recurrence prediction are valuable tools to improve patient risk stratification, help with post-cancer monitoring, and plan more effective follow-up. Structured data provides the best results when fed to ML algorithms. However, an approach based on natural language processing offers comparable results while potentially requiring less mapping effort.
Experimental Evaluation of Graph Databases: JanusGraph, Nebula Graph, Neo4j, and TigerGraph

NoSQL databases were created with the primary goal of addressing the shortcomings in the efficiency of relational databases, and can be of four types: document, column, key-value, and graph databases. Graph databases can store data and relationships efficiently, and have a flexible and easy-to-understand data schema. In this paper, we perform an experimental evaluation of the four most popular graph databases: JanusGraph, Nebula Graph, Neo4j, and TigerGraph. Database performance is evaluated using the Linked Data Benchmark Council’s Social Network Benchmark (LDBC SNB). In the experiments, we analyze the execution time of the queries, the loading time of the nodes and the RAM and CPU usage for each database. In our analysis, Neo4j was the graph database with the best performance across all metrics.
Towards Efficient Index Construction and Approximate Nearest Neighbor Search in High-Dimensional Spaces

The approximate nearest neighbor (ANN) search in high-dimensional spaces is a fundamental but computationally very expensive problem. Many methods have been designed for solving the ANN problem, such as LSH-based methods and graph-based methods. The LSH-based methods can be costly to reach high query quality due to the hash-boundary issues, while the graph-based methods can achieve better query performance by greedy expansion in an approximate proximity graph (APG). However, the construction cost of these APGs can be one or two orders of magnitude higher than that for building hash-based indexes. In addition, they fail short in incrementally maintaining APGs as the underlying dataset evolves. In this paper, we propose a novel approach named LSH-APG to build APGs and facilitate fast ANN search using a lightweight LSH framework. LSH-APG builds an APG via consecutively inserting points based on their nearest neighbor relationship with an efficient and accurate LSH-based search strategy. A high-quality entry point selection technique and an LSH-based pruning condition are developed to accelerate index construction and query processing by reducing the number of points to be accessed during the search. LSH-APG supports fast maintenance of APGs in lieu of building them from scratch as dataset evolves. Its maintenance cost and query cost for a point is proven to be less affected by dataset cardinality. Extensive experiments on real-world and synthetic datasets demonstrate that LSH-APG incurs significantly less construction cost but achieves better query performance than existing graph-based methods.
Gaussian Process Gaussian Mixture PHD Filter for 3D Multiple Extended Target Tracking

This paper addresses the problem of tracking multiple extended targets in three-dimensional space. We propose the Gaussian process Gaussian mixture probability hypothesis density (GP-PHD) filter, which is capable of tracking multiple extended targets with complex shapes in the presence of clutter. Our approach combines the Gaussian process regression measurement model with the probability hypothesis density filter to estimate both the kinematic state and the shape of the targets. The shape of the extended target is described by a 3D radial function and is estimated recursively using the Gaussian process regression model. Furthermore, we transform the recursive Gaussian process regression problem into a state estimation problem by deriving a state space model such that the estimation of the extent can be integrated into the kinematic part. We derive the predicted likelihood function of the PHD filter and provide a closed-form Gaussian mixture implementation. To evaluate the performance of the proposed filter, we simulate a typical extended target tracking scenario and compare the GP-PHD filter with the traditional Gamma Gaussian Inverse-Wishart PHD (GGIW-PHD) filter. Our results demonstrate that the proposed algorithm outperforms the GGIW-PHD filter in terms of estimating both kinematic states and shape. We also investigate the impact of the measurement rates on both filters; it is observed that the proposed filter exhibits robustness across various measurement rates, while the GGIW-PHD filter suffers under low-measurement-rate conditions.
HIDE-Healthcare IoT Data Trust ManagEment: Attribute centric intelligent privacy approach

The cloud-based Internet of Things (IoTs) storage enables patients to monitor their health remotely and offers services for physicians of various Medical Institutions (MIs) to diagnose and treat them on time. As a matter of trust, patients are legally expected to hide their real identity and ensure data privacy in the cross-domain of IoT-healthcare, whether it is stored correctly or modified due to external and internal attacks in the cloud. Additionally, physicians treat patients and continuously store duplicated data in cloud storage, which increases the cost of computing. In this context, this paper presents HIDE-Healthcare IoT Data privacy trust management framework, focusing on attributes. Patients’ attributes are used to encrypt and decrypt sensory data between patients and different entities by incorporating the idea of trustworthy and secure shared keys. HIDE uses an intelligent object’s pointer to store the same patient’s sensory data in various versions to prevent data duplication, which will help track MIs that treat patients. An intelligent content-based emergency data access control is developed to monitor multiple patient health criticalities in HIDE. The security analysis and experimental evaluation attest to the benefits of the proposed HIDE framework, considering security and privacy metrics.
Deep learning-based affine medical image registration for multimodal minimal-invasive image-guided interventions – A comparative study on generalizability

Multimodal image registration is applied in medical image analysis as it allows the integration of complementary data from multiple imaging modalities. In recent years, various neural network-based approaches for medical image registration have been presented in papers, but due to the use of different datasets, a fair comparison is not possible. In this research 20 different neural networks for an affine registration of medical images were implemented. The networks' performance and the networks' generalizability to new datasets were evaluated using two multimodal datasets - a synthetic and a real patient dataset - of three-dimensional CT and MR images of the liver. The networks were first trained semi-supervised using the synthetic dataset and then evaluated on the synthetic dataset and the unseen patient dataset. Afterwards, the networks were finetuned on the patient dataset and subsequently evaluated on the patient dataset. The networks were compared using our own developed CNN as benchmark and a conventional affine registration with SimpleElastix as baseline. Six networks improved the pre-registration Dice coefficient of the synthetic dataset significantly (p-value < 0.05) and nine networks improved the pre-registration Dice coefficient of the patient dataset significantly and are therefore able to generalize to the new datasets used in our experiments. Many different machine learning-based methods have been proposed for affine multimodal medical image registration, but few are generalizable to new data and applications. It is therefore necessary to conduct further research in order to develop medical image registration techniques that can be applied more widely.