Reviewerly/exHarmony huggingface dataset:
A Blockchain of Blockchains Structure Based on Asset Trading Scheme

In the realm of blockchain applications, the issue of value isolation, commonly referred to as the value island problem, imposes inherent limitations on the applicability of corresponding assets. To overcome this challenge, researchers have dedicated their efforts to exploring diverse solutions for facilitating cross-chain transactions involving various assets. These solutions include centralized exchanges and hash time lock protocols. However, these approaches are subject to significant limitations. Centralized exchanges, while providing transactional functionalities, undermine the decentralized nature intrinsic to blockchain technology. Conversely, hash time lock protocols necessitate a high level of trust between transacting parties. Furthermore, although decentralized exchanges have emerged to support atomic cross-chain exchanges, their transactional architectures are restricted to either a bilateral relationship between two blockchains or a subset of pre-established blockchains. Therefore, the construction of a non-trust-based, decentralized exchange that supports multiple blockchain networks remains an ongoing challenge. In this research, we propose Vchain, a cross-chain relay transaction scheme, designed with a BoBs(Blockchain of Blockchains) structure, to address the aforementioned challenges. Vchain enables the participation of various accounts within blockchain ecosystems that support smart contract functionalities. It ensures the security and integrity of user transactions by leveraging third-party cryptocurrency assets as collateral. Moreover, users have the flexibility to selectively choose transaction counterparties based on the quantity of collateral assets provided, thereby maximizing their transactional interests. Consequently, Vchain enables non-trust-based, multi-chain asset cross-chain transactions, addressing the limitations of existing solutions.
Selective Encryption of Compressed Image Regions on the Edge with Reconfigurable Hardware

It is becoming more and more common for people to take photos on edge devices such as smartphones, and wish to transmit them in a secure manner. For example, a patient may wish to send an image to a medical practitioner for a quick screening analysis and be given advice for further follow-up. We investigate possible implementations of DCT followed by RSA encryption. We use the AMD Zynq processor System on Chip comprising an embedded ARM processor and FPGA fabric to investigate design trade-offs. Our implementation of DCT plus RSA exhibits a 3.4x improvement in latency over an optimized software implementation. We further investigate selectively encrypting the compressed image for both speeding up the encryption process and reducing the amount of data needed to be transmitted. This design space exploration results in lessons that can be applied to future implementations.
Combining Reverse Temporal Attention Mechanism and Dynamic Similarity Analysis for Disease Prediction

In recent years, the explosive growth of the Internet has made it an indispensable part of our daily study, work and life. With the rapid development of the medical industry and information technology, electronic health record (EHR) system has been widely used, and more and more researchers choose to conduct experiments based on EHR data sets. However, due to the complexity and heterogeneity of patient EHR data, and electronic health record is a long-term process. Therefore, it is difficult for researchers to analyze EHR data sets for experiments such as predictions and recommendations. In this paper, we propose a disease prediction model (RTAMDSA model), which integrates Reverse Time Attention Mechanism (RTAM) and Dynamic Similarity Analysis (DSA). The model mainly includes a patient representation module, a similarity analysis module and a disease prediction module. The patient representation module uses a reverse temporal attention mechanism with two levels of attention, to run Recurrent Neural Network in reverse chronological order, to obtain the embedding representation of the patient from the patient's historical disease trajectory, so that the patient's most recent visit records receive more attention than the old records, because the patient's recent visit information is more informative. The similarity analysis module performs searches using dynamic similarity analysis, to calculate the similarity of each patient for each visit, and obtain the specific patient sequences that are most similar to the target patient. The disease prediction module integrates the obtained patient embeddings and similar patient sequences to output the final prediction result for the target patient. Comparing with the baseline models on the MIMIC-III dataset, experimental results show that the RTAMDSA model improves prediction accuracy on the disease prediction task, exhibits better robustness and stability, and the reverse time attention mechanism provides good interpretability for the model.
Safeguarding Devices and Edge Computing: A Responsive Anti-Scam Approach

The current methods for detecting scams often mirror those used for identifying spam messages. However, scams exhibit distinct characteristics, including their targeted nature and potential for manual creation, allowing them to evade many traditional anti-spam techniques. This paper introduces a novel solution to overcome this limitation. The approach empowers message senders to pre-register their identities across multiple platforms. When a message raises suspicion as a possible scam, recipients can verify its legitimacy by cross-referencing the sender's identity on the registered platforms. This process encourages a response from the genuine sender, promoting ethical practices within the ecosystem and contributing to the enhanced accuracy of scam status determination. A comparative analysis is conducted between the proposed approach and established anti-spam methods. The versatility of this approach makes it well-suited for application in edge devices and IoT devices, bolstering its relevance in these domains.
A Comparison of Denoising Techniques with Signals Affected by Non-Gaussian Noise and Sample Entropy as an Indicator of Performance

In this work, we compare three denoising techniques: Wavelet Transform (WT), Multiscale Principal Component Analysis (MSPCA), and Variational Mode Decomposition (VMD). Normally, it is assumed that signals in the process of measurement or transmission over a communication channel are affected by AWGN noise. However, in real environments, signals can be affected by other types of noise. In this way, we evaluate the performance of these techniques using four types of signals with different waveforms and synthetically add three types of noise: AWGN, sub-Gaussian noise, and super-Gaussian noise. In our experiments, WT was superior to the other techniques. It was evidenced visually and quantitatively with two indicators: Improved Signal-to-Noise (SNR) and Sample Entropy (SE). SE is not normally used to evaluate the denoising of signals, however, as we demonstrate in this work, it is more appropriate since it considers the level of distortion caused by denoising techniques. With the two indicators, the superiority of WT is demonstrated. In addition to this comparison, we apply the three techniques to signals measured in a real environment and show that an indicator like SNR is not suitable for evaluating performance. On the other hand, SE, in addition to indicating noise mitigation, has the characteristic of showing which technique distorts the processed signal to a lesser extent.
Forecasting Day-Ahead Solar Irradiance for Puerto Rico Using the WRF Model and NSRDB

Accurately predicting solar energy resources is a major challenge in integrating photovoltaics generation on the electric grid. Numerical weather prediction has been recognized by the solar energy community as a major approach to provide solar resource forecasts at various locations and for a variety of timescales. In this study, as a part of the Puerto Rico Grid Resilience and Transitions to 100% Renewable Energy Study (PR100), we develop day-head solar irradiance forecast data using the Weather Research and Forecasting (WRF) model at 3 km and hourly/5-minute. The global horizontal irradiance (GHI) and direct normal irradiance (DNI) forecasts simulated from the WRF model are postprocessed by a simple optimization method using satellite-derived gridded observations from the National Solar Radiation Data Base (NSRDB) to reduce error and bias of the solar irradiance forecasts covering 2018–2020. The NSRDB contributes to improving the GHI and DNI forecasts and also offers the opportunity for an in-depth analysis to evaluate their accuracy over a wide range of Puerto Rico regions. Preliminary results show overall improvements of GHI forecasts up to 37% (DNI: 15%) for mean absolute error and 97% (DNI: 76%) for mean bias error by applying a postprocessing technique to WRF model output.
TenSQL: An SQL Database Built on GraphBLAS

Relational Database Management Systems (RDBMS) have been the most prominent form of database in the world for several decades. While relational databases are often applied within high-frequency/low-volume transactional applications such as website backends, the poor performance of relational databases on low-frequency/high-volume queries often precludes their application to big data analysis fields like graph analytics. This work explores the construction of an RDBMS solution that uses the GraphBLAS API to execute Structured Query Language (SQL) in an effort to improve performance on high-volume queries. Tables are redefined to be collections of sparse scalars, vectors, matrices, and more generally sparse tensors. The explicit values (nonzeros) in these sparse tensors define the rows and NULL values within the tables. A prototype database called TenSQL was constructed and evaluated against several SQL implementations including PostgreSQL. Preliminary results comparing the performance on queries common in graph analysis applications offer performance improvements as high as 1,400x over PostgreSQL for moderately sized datasets when returning results in a columnar format.
Using VR to Present a Mobile MRI Unit in Confined Physical Space: Reporting Results of a Field-Deployment at a Radiology Exhibition

We investigated the suitability of virtual reality (VR) to exhibit spacious medical equipment in a limited exhibition space. The basis of our research was a previously field-tested VR system populated with a real-size model of a mobile magnetic resonance imaging (MRI) unit. The VR-based system, which already used external sensors to a linked physical wheeled chair with the virtual environment, was modified with teleportation features and equipped with eye-tracking capabilities. These changes allowed us to use the system in more confined spaces. Next, we deployed our system during a three-day-Iong medical exhibition where sixty-eight attendees used our cyber-physical solution, out of which five agreed to participate directly in the in-depth user study. We report the results of these field-tests and system evaluation in a non-laboratory, real-world environment of medical exhibition.
A High Secure Video Steganography Based on Effective Quasigroup-Based Encryption and Shearlet Transform

This research proposes a novel video steganography method that uses the shearlet transform and quasigroup-based encryption to conceal secret data. The suggested method finds the key frames by utilizing scene change detection. DFT and Shearlet transform is then applied to the key frames to obtain the subbands and the orientation. The secret data is encoded in those values. To improve security, SVD and Quasigroup based encryption are used as preprocessor before embedding the secret data. The, embedding capacity is 69%, the PSNR value is between 65 and 69 dB, and the SSIM is 0.9852. The testing outcome best exemplifies how the algorithm not only offers greater security but also excellent video quality and robustness.
Explainable Ensemble-Based Detection of Cyber Attacks on Internet of Medical Things

As new applications of the Internet of Things emerge in the health and medical services, malicious actors target these applications increasingly. These growing application bring a variety of privacy and security challenges. In this paper, we present an explainable machine learning ensemble designed to detect attacks on Internet-of-Medical-Things with high accuracy. The proposed system was tested using WUSTL-EHMS-2020 dataset. Tests showed that the proposed ensemble is capable of delivering an accuracy exceeding 99%, with an score exceeding 0.99. The proposed system was explained using SHAP values to provide insights into the most impactful features, and the nature of their impact on the system's decisions.
Analyzing Edge IoT Digital Forensics Tools: Cyber Attacks Reconstruction and Anti-Forensics Enhancements

Digital forensics is a rapidly growing sub-domain of forensic science, primarily due to technological advancements and their integration into everyday life. In this paper, our study delves deep into the potential of digital forensics tools, particularly emphasizing their capability to reconstruct the timelines of cyber attacks on edge Internet of Things (IoT) systems. Our research compares ten distinct and widely used digital forensics tools to discern the most detailed, efficient, and user-friendly instrument for IoT attack reconstruction. Our analysis over fourteen different cyber attacks found that Splunk Enterprise is the most optimal static PCAP and CSV file analysis solution, while Microsoft Azure stands out for live analysis in the context of attack reconstruction. Furthermore, we outline potential enhancements for these tools with consideration to the evolving dynamics of anti-forensics.
Effect of Angle and Direction of Solar Panels in the Desert Climate of Abu Dhabi, United Arab Emirates

In this work, we study the effect of angle and direction on the solar cell performance in Abu Dhabi, United Arab Emirates. Understanding the optimal angle and direction of solar panels in a desert climate can be key in mitigating some of the negative effects. These include very high temperature, humidity, and soiling. Our results show the optimal direction in Abu Dhabi, UAE is due south that provides the highest performance with an angle around 30 degrees. Moreover, a solar tracker can be used to keep the solar cell at the maximum efficiency.
Online Learning Meets Semantic Communication over Wireless Channels

One major challenge for semantic communication networks is to deploy the network in practice where channel environments change dynamically. When adjusting the network weights on a subframe basis to the changing channel environments, the online over-the-air (OTA) training data is extremely limited, making it difficult for networks with a large number of learnable parameters to converge. In this paper, we present an online reservoir computing (RC) aided adaptation approach for the semantic joint source and channel coding (JSCC) network to adjust the network weights on a subframe basis according to the channel changes. Specifically, the semantic JSCC network is first trained offline to learn a set of the initial weights with sufficient training data. During the online stage, where the channel model differs from the offline stage and the OTA training data is scarce, RC is adopted for equalizing the channel and adapting the end-to-end network weights to the change of channel. Experimental results demonstrate the effectiveness of the introduced approach in improving the performance of the semantic JSCC network on a subframe basis when the channel models do not match during the offline and online stages. Our work provides a potential solution for deploying semantic communication networks in practice in dynamic channel environments with a limited amount of available training data.
Cyber Vulnerabilities in Blockchain Electronic Health Records: An In-Depth Threat Analysis

The rapid evolution of information systems and the surge in data generation and dissemination have fostered the emergence of Electronic Health Records (EHRs). Specifically, blockchain-enabled EHRs are revolutionizing modern healthcare by centralizing patient data promoting transparency, accessibility, and interoperability. However, amidst increasing cyberattacks on healthcare institutions, the security of these EHRs is a critical concern. Existing research predominantly focuses on the advantages and disadvantages of incorporating blockchain technology into EHRs, leaving a noticeable gap in analyzing specific threats. Our study bridges this gap by leveraging a hybrid threat modeling methodology, integrating attack tree models with an exhaustive review of existing literature on threats to blockchain-based standardized EHRs. This approach has identified and organized 26 unique threats into an attack tree. Each threat is elaborated upon and discussed within the context of the Blockchain EHRs.
Considering the Variability of Soiling in Long-Term PV Performance Forecasting

This study presents the development of a methodology for evaluating the variability associated with soiling on long-term PV forecasting. Independent engineering firms typically build P50 forecasts for large PV plants through the use of the PVsyst software, where monthly soiling losses are one of many inputs to the P50 model. Subsequently, long-term performance distributions, or Pvalues, are constructed through a Monte Carlo analysis that includes various factors such as: satellite irradiance modeling uncertainty, uncertainty in the PVsyst model, and long-term irradiance variability. Often the PVsyst model uncertainty is increased to account for sites with significant soiling concerns but no systematic method has been presented in the literature to specifically include soiling variability within Pvalues. In this work soiling information from 16 sites in the U.S. Southwest are combined with 20 years of rainfall data to generate 20 years of energy production with soiling losses and then subsequently generate Pvalues. The results show that the spread of Pvalues (P1-P99) can increase from 0-13% when interannual soiling variability is included.
Fault-Tolerant Data Aggregation with Error-Checking for Smart Grids

With the rapid development of smart grid technology, a new challenge has been brought about by the massive data generated from distributed devices. Data aggregation technology plays a crucial role in smart grid schemes, in terms of energy data statistics and resource allocation. However, achieving fast error-checking and improving device fault-tolerance are still open issues in the smart grid data aggregation. To address these issues, this paper proposes an error-tolerant data aggregation scheme integrated with error-checking mechanisms. The scheme proposes a tree-based aggregation structure, facilitating swift error-checking of data. We combine masking values with user anonymity in the ElGamal cryptographic scheme to ensure the security of sensitive data and resist collusion attacks. Both theoretical and experimental analyses demonstrate that this scheme enables a lightweight, secure data aggregation, ensuring enhanced reliability and error-checking.
A High Capacity Combined Carrier Steganography Without Embedding Image

The technique of image steganography is the embedding of secret information into the carrier image by means of algorithms that are widely used in the field of information transmission, but steganalysis tools built with traditional steganographic algorithms can easily identify them. The technique of steganography without embedding (SWE) is the mapping of the secret information onto the noise and the generation of an image by mapping the noise of the secret information. This SWE technique is difficult to find without directly using secret information. But the capacity of secret information is less and the extraction accuracy is low when the SWE is currently hidden. Therefore, a steganography without embedding technique for carrier secret information is proposed in this paper based on the above problems. Initially, the secret information is mapped into the carrier noise, which can increase the secret information capacity of steganography and improve the accuracy of extracting information. Finally, the effectiveness and generality of the design method will be verified by experiments.
Deep Learning-Based Detection of CSRF Vulnerabilities in Web Applications

Cross-Site Request Forgery (CSRF) attacks pose a significant threat to web applications, potentially leading to data breaches and service disruptions by exploiting user trust and enabling malicious actors to perform unauthorized actions on behalf of users. Existing approaches for CSRF vulnerability detection mainly rely on rule-based or machine learning techniques, which often face limitations in accurately identifying complex attack patterns. This article introduces a novel deep learning-based framework designed for the detection of CSRF vulnerabilities. The framework's core functionality lies in its ability to analyze and classify incoming HTTP requests as either security-sensitive or benign. Our framework represents a pioneering effort in leveraging deep learning techniques for CSRF vulnerability detection, outperforming the Machine Learning based existing solutions.
Tomography of ultrabroadband polarization-frequency hyperentangled photons

We generate photon pairs entangled in both frequency and polarization spanning the telecom C- and L-bands, characterize high-fidelity states in multiple wavelength channels, and show full tomography in 16- and 32-dimensional Hilbert spaces.
LIDarknet: Experimenting the Power of Ensemble Learning in the Classification of Network Traffic

The Darknet is an encrypted corner of the internet, intended for users who wish to remain anonymous and mask their identity. Because of its anonymous qualities, the Darknet has become a go-to platform for illicit activities such as drug trafficking, terrorism, and dark marketplaces. Therefore, it is important to recognize Darknet traffic in order to monitor and detect malicious online activities. This paper investigates the potential effectiveness of machine learning algorithms in identifying attacks using the CICdarknet2020 dataset. The dataset includes two distinct classification targets: traffic label and application labels. The objective of our research is to identify optimal classifiers for traffic and application classification by employing ensemble learning methods, aiming to achieve the highest possible results. Through our experimentation, we have found that the best-performing models surpassing all other state-of-the-art machine learning models are LightGBM, achieving a 93.41% f1-score in the Application classification, and Random Forest, achieving a 99.8% f1-score in the traffic classification.
Feature Selection on Anonymized Datasets

Privacy concerns have become increasingly prominent in data analysis, leading to adoption of privacy-preserving approaches including anonymization techniques. However, the application of anonymization techniques may impact the significance of features in the anonymized dataset, potentially affecting the accuracy and reliability of subsequent machine learning tasks. In this paper, we present a comprehensive comparative study to investigate the preservation of feature significance in the original dataset versus its anonymized version. We conduct a set of experiments to provide valuable insights into the implications of anonymization techniques on preserving the features' relevancy, which advances privacy-aware data analysis in various domains.