Reviewerly/exHarmony huggingface dataset:
Timber De-Standardized

Timber De-Standardized is a framework that salvages irregular and regular shaped tree logs by utilizing a mixed reality (MR) interface for the design, fabrication, and assembly of a structurally viable tree log assembly.The process engages users through a direct, hands-on design approach to iteratively modify and design irregular geometry at full scale within an immersive MR environment without altering the original material.A digital archive of 3D scanned logs are the building elements from which users, designing in the MR environment, can digitally harvest (though slicing) and place the elements into a digitally constructed whole.The constructed whole is structurally analyzed and optimized through recursive feedback loops to preserve the users' predetermined design.This iterative toggling between the physical and virtual emancipates the use of irregular tree log structures while informing and prioritizing users' design intent.To test this approach, a scaled prototype was developed and fabricated in MR.By creating a framework that links a holographic digital design to a physical catalog of material, the interactive workfl ow provides greater design agency to users as co-creators in processing material parts.This participation enables users to have a direct impact on the design of discretized tree logs that would otherwise have been discarded in standardized manufacturing.This paper presents an approach in which complex tree log structures can be made without the use of robotic fabrication tools.This workfl ow opens new opportunities for design in which users can freely confi gure structures with non-standardized elements within an intuitive MR environment.
Error Bounds for Decoding Piecewise Constant Nanopore Signals in DNA Storage

Nanopore sequencing enables reading strings of A,C,G,T nucleotides in DNA strands by pulling them into nanopores with the help of motor proteins. Due to the discrete stepping of motor proteins, the signals produced by a DNA sequence tend to be piecewise-constant expansions of some underlying real-valued sequence. In this paper, we assume that every k-nucleotide sequence corresponds to a real-valued codeword of length <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> , and model the nanopore channel as a noisy duplication channel that stretches every sample of a codeword using a geometric distribution, and then adds Gaussian noise. We show that for this channel, a simpler variant of the dynamic time warping (DTW) algorithm performs maximum likelihood decoding. Next, we devise an <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(k^{2})$</tex> - algorithm for bounding the pairwise error probability between two codewords of length <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> . Finally, we use Scrappie to design codebooks with a storage efficiency of 1 bit per nucleotide and demonstrate using error simulations the accuracy of the calculated error bounds.
MOB-FL: Mobility-Aware Federated Learning for Intelligent Connected Vehicles

Federated learning (FL) is a promising approach to enable the future Internet of vehicles consisting of intelligent connected vehicles (ICVs) with powerful sensing, computing and communication capabilities.We consider a base station (BS) coordinating nearby ICVs to train a neural network in a collaborative yet distributed manner, in order to limit data traffic and privacy leakage.However, due to the mobility of vehicles, the connections between the BS and ICVs are shortlived, which affects the resource utilization of ICVs, and thus, the convergence speed of the training process.In this paper, we propose an accelerated FL-ICV framework, by optimizing the duration of each training round and the number of local iterations, for better convergence performance of FL.We propose a mobility-aware optimization algorithm called MOB-FL, which aims at maximizing the resource utilization of ICVs under shortlived wireless connections, so as to increase the convergence speed.Simulation results based on the beam selection and the trajectory prediction tasks verify the effectiveness of the proposed solution.
FedLE: Federated Learning Client Selection with Lifespan Extension for Edge IoT Networks

Federated learning (FL) is a distributed and privacy-preserving learning framework for predictive modeling with massive data generated at the edge by Internet of Things (IoT) devices. One major challenge preventing the wide adoption of FL in IoT is the pervasive power supply constraints of IoT devices due to the intensive energy consumption of battery-powered clients for local training and model updates. Low battery levels of clients eventually lead to their early dropouts from edge networks, loss of training data jeopardizing the performance of FL, and their availability to perform other designated tasks. In this paper, we propose FedLE, an energy-efficient client selection framework that enables lifespan extension of edge IoT networks. In FedLE, the clients first run for a minimum epoch to generate their local model update. The models are partially uploaded to the server for calculating similarities between each pair of clients. Clustering is performed against these client pairs to identify those with similar model distributions. In each round, low-powered clients have a lower probability of being selected, delaying the draining of their batteries. Empirical studies show that FedLE outperforms baselines on benchmark datasets and lasts more training rounds than FedAvg with battery power constraints.
Hydra: Lessons from the world's largest darknet market

Abstract Research Summary We present a comprehensive description of Hydra, the largest darknet marketplace in the world until its shutdown in April 2022. We document the main features of Hydra such as dead‐drop delivery, feedback and reputation system, escrow, and dispute resolution. Using data scraped from the platform, we quantitatively examine the scale and the structure of the marketplace. We find that it has been highly competitive, geographically covering at least 69% of the Russian population and trading a wide variety of drugs, while also allowing the wholesale trade of drugs and precursors. The dead‐drop delivery system used on Hydra was expensive, as the courier costs comprised a substantial proportion of the sale price of drugs on Hydra. We contribute to the research on drug cryptomarkets by studying an unprecedentedly large non‐Western marketplace that existed substantially longer than any other known darknet market. Policy Implications The phenomenon of Hydra shows that shut‐down policies applied to darknet marketplaces have a large effect and implicitly shape the whole drug market. Without these policies, a pervasive digitalization of the drug trade can occur. The major cost of allowing marketplaces to grow is the probable increase in the consumption of illegal drugs due to convenience for consumers and facilitated cooperation between suppliers. This cost must be weighed against the potential benefits, including a higher quality of drugs, a decrease in potential violence, and the incentives for a large marketplace to self‐regulate. The case of Hydra also suggests the relevance of financial regulation to limit the growth of darknet marketplaces.
Blockchain technology in building environmental sustainability: A systematic literature review and future perspectives

The significant environmental impact of buildings during their lifecycle has made it a critical area to address the global climate crisis. However, due to the involvement of multiple industrial sectors, a large number of related parties, and the complexity of environmental information flows in the whole lifecycle of buildings, the effectiveness of the traditional centralized management approach is limited, and it is necessary to adopt more advanced management technology tools. With its decentralized, transparent, and secure characteristics, blockchain technology has triggered various applications in several industries, as well as in the field of building environmental sustainability. Using bibliometric and content analysis, this paper comprehensively reviews the current state of research on blockchain technology in the field of building environmental sustainability. A total of 128 articles were reviewed, revealing that blockchain technology has been used to address various types of environmental problems in multiple stages of the building lifecycle, including building material production, construction, operation, and demolition phases, etc. It is shown that blockchain technology can already effectively improve the use of renewable energy and reduce overall energy consumption during the building operation phase, while the great potential for solving other environmental issues, such as the management of building embodied carbon emissions, needs to be further explored. This paper also explores potential research opportunities and future directions based on current gaps and limitations, presenting several critical topics from the perspective of building lifecycle and practice support. This review helps related researchers gain insights into the research frontier and future direction.
Artificial intelligence discovery of a charging protocol in a micromaser quantum battery

We propose a gradient-based general computational framework for optimizing model-dependent parameters in quantum batteries (QB). We apply this method to two different charging scenarios in the micromaser QB and we discover a charging protocol for stabilizing the battery in upper-laying Hilbert space chambers in a controlled and automatic way. This protocol is found to be stable and robust, and it leads to an improved charging efficiency in micromaser QBs. Moreover, our optimization framework is highly versatile and efficient, holding great promise for the advancement of QB technologies at all scales.
Impact of Traditional and Embedded Image Denoising on CNN-Based Deep Learning

In digital image processing, filtering noise is an important step for reconstructing a high-quality image for further processing such as object segmentation, object detection, and object recognition. Various image-denoising approaches, including median, Gaussian, and bilateral filters, are available in the literature. Since convolutional neural networks (CNN) are able to directly learn complex patterns and features from data, they have become a popular choice for image-denoising tasks. As a result of their ability to learn and adapt to various denoising scenarios, CNNs are powerful tools for image denoising. Some deep learning techniques such as CNN incorporate denoising strategies directly into the CNN model layers. A primary limitation of these methods is their necessity to resize images to a consistent size. This resizing can result in a loss of vital image details, which might compromise CNN’s effectiveness. Because of this issue, we utilize a traditional denoising method as a preliminary step for noise reduction before applying CNN. To our knowledge, a comparative performance study of CNN using traditional and embedded denoising against a baseline approach (without denoising) is yet to be performed. To analyze the impact of denoising on the CNN performance, in this paper, firstly, we filter the noise from the images using traditional means of denoising method before their use in the CNN model. Secondly, we embed a denoising layer in the CNN model. To validate the performance of image denoising, we performed extensive experiments for both traffic sign and object recognition datasets. To decide whether denoising will be adopted and to decide on the type of filter to be used, we also present an approach exploiting the peak-signal-to-noise-ratio (PSNRs) distribution of images. Both CNN accuracy and PSNRs distribution are used to evaluate the effectiveness of the denoising approaches. As expected, the results vary with the type of filter, impact, and dataset used in both traditional and embedded denoising approaches. However, traditional denoising shows better accuracy, while embedded denoising shows lower computational time for most of the cases. Overall, this comparative study gives insights into whether denoising will be adopted in various CNN-based image analyses, including autonomous driving, animal detection, and facial recognition.
Efficient solid waste inspection through drone‐based aerial imagery and <scp>TinyML</scp> vision model

Abstract Solid waste management is a significant challenge in the development of smart cities. Existing approaches for solid waste monitoring are often time‐consuming and resource intensive. Therefore, this study proposes a novel approach to solid waste monitoring that utilizes drone technology. The proposed method enables the efficient identification and classification of waste objects in the garbage discovered by the drone. This system can inspect every part of a smart city from a remote location, allowing for the timely and effective management of solid waste. Thus, the proposed system can be easily integrated in the existing waste management system for smart city. The drone‐based solid waste monitoring system comprises a drone equipped with a computer vision model for resource‐constrained devices and a software application that operates the drone and analyzes the captured image or video. The system utilizes the Internet of Things (IoT) to upload the collected data to the cloud, making it easily accessible whenever necessary. The proposed drone‐based solid waste monitoring system is a promising solution for the efficient and cost‐effective management of solid waste in smart cities. The system's innovative use of drone technology and IoT provides a scalable and adaptable solution that can be customized to meet the needs of any city.
Wolf in Sheep's Clothing: Evaluating Security Risks of the Undelegated Record on DNS Hosting Services

Leveraging DNS for covert communications is appealing since most networks allow DNS traffic, especially the ones directed toward renowned DNS hosting services. Unfortunately, most DNS hosting services overlook domain ownership verification, enabling miscreants to host undelegated DNS records of a domain they do not own. Consequently, miscreants can conduct covert communication through such undelegated records for whitelisted domains on reputable hosting providers. In this paper, we shed light on the emerging threat posed by undelegated records and demonstrate their exploitation in the wild. To the best of our knowledge, this security risk has not been studied before.
Energy-Efficient and Adaptive Gradient Sparsification for Federated Learning

Federated learning is an emerging machine-learning technique that trains an algorithm across multiple decentralized edge devices or clients holding local data samples. It involves training local models on local data and uploading model parameters to a server node at regular intervals to generate a global model which is transmitted to all clients. However, edge nodes often have limited energy resources, and hence performing energy-efficient communication of model parameters is a bottleneck problem. We propose an energy-adaptive model sparsification for Federated Learning. The central idea is to adapt the sparsification level in run-time by optimizing the ratio between information content and energy cost. We illustrate the efficiency of the proposed algorithm by comparing its performance with three baseline schemes. We validate the performance of the proposed algorithm for two cost models. Simulation results show that the proposed algorithm needs exponentially less amount of communication and energy as compared to the three baseline schemes while achieving the best accuracy and fastest convergence.
White-Box Adversarial Attacks on Deep Learning-Based Radio Frequency Fingerprint Identification

Radio frequency fingerprint identification (RFFI) is an emerging technique for the lightweight authentication of wireless Internet of things (IoT) devices.RFFI exploits unique hardware impairments as device identifiers, and deep learning is widely deployed as the feature extractor and classifier for RFFI.However, deep learning is vulnerable to adversarial attacks, where adversarial examples are generated by adding perturbation to clean data for causing the classifier to make wrong predictions.Deep learning-based RFFI has been shown to be vulnerable to such attacks, however, there is currently no exploration of effective adversarial attacks against a diversity of RFFI classifiers.In this paper, we report on investigations into white-box attacks (non-targeted and targeted) using two approaches, namely the fast gradient sign method (FGSM) and projected gradient descent (PGD).A LoRa testbed was built and real datasets were collected.These adversarial examples have been experimentally demonstrated to be effective against convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and gated recurrent units (GRU).
Cyber Threats Classifications and Countermeasures in Banking and Financial Sector

The banking and financial sector has always been a prime target for cyber threats due to the critical nature of the information they handle.With the increasing dependence on technology and digital transformation, the sector is facing more complex and sophisticated threats from cybercriminals.The banking sector serves as the backbone of a country's economy, interlinked with various other sectors like petroleum, mining, health, and industry.Any significant damage to the banking sector can send shockwaves through the entire economic landscape.Therefore, cyber threat classifications play a crucial role in risk management and provide a valuable framework for understanding and responding to cyber threats.Understanding the potential impact of a cyber-attack helps organisations assess the risks they face and develop appropriate risk mitigation strategies.The purpose of this research paper is to provide a comprehensive analysis of cyber threats in the banking and financial sectors, including identifying common threats, their nature and character to help in classification.One of the significant contributions of this research paper is to classify cyber threats to the banking and financial sectors based on their severity and technicality.This classification helps to identify the appropriate countermeasures required to mitigate the risks of each type of threat.Furthermore, the paper explores the technical, non-technical, organizational countermeasures and the legal and regulatory measures used to protect financial transactions from cyber threats.This research work delves into the challenges and limitations of cyber threat classifications, focusing specifically on those confronting the banking and financial sector in their pursuit of robust cybersecurity.Additionally, it analyses recent trends and developments in the field, highlighting the evolving nature of cyber threats to banking.The most significant challenge is the rapidly evolving nature of cyber threats, making it challenging to keep up with the latest trends and technologies.
EVM: Incorporating Model Checking into Exploratory Visual Analysis

Visual analytics (VA) tools support data exploration by helping analysts quickly and iteratively generate views of data which reveal interesting patterns. However, these tools seldom enable explicit checks of the resulting interpretations of data-e.g., whether patterns can be accounted for by a model that implies a particular structure in the relationships between variables. We present EVM, a data exploration tool that enables users to express and check provisional interpretations of data in the form of statistical models. EVM integrates support for visualization-based model checks by rendering distributions of model predictions alongside user-generated views of data. In a user study with data scientists practicing in the private and public sector, we evaluate how model checks infuence analysts' thinking during data exploration. Our analysis characterizes how participants use model checks to scrutinize expectations about data generating process and surfaces further opportunities to scaffold model exploration in VA tools.
A Secure Chaos-Based Lightweight Cryptosystem for the Internet of Things

This paper introduces a novel approach to addressing the security challenges of the Internet of Things (IoT) by presenting a secure Chaos-based lightweight cryptosystem.The proposed design incorporates a Pseudo-Chaotic Numbers Generator combined with the Speck64/128 lightweight block cipher, to meet the stringent requirements of security and lightweight characteristics.First, we subject the cryptosystem to a battery of rigorous tests, including various cryptanalytics such as brute-force, differential attacks, and statistical attacks.The results of these tests clearly demonstrate the cryptosystem's exceptional security resilience and its capacity to effectively withstand these attacks.Then, the novel cryptosystem architecture, specifically designed for resource-limited applications like IoT, was implemented on a Xilinx PYNQ-Z2 XC7Z020 FPGA platform, which aligns perfectly with the constraints of IoT devices.The investigation comprises a thorough analysis and evaluation of the developed cryptosystem according to the algorithm complexity and the achieved precision, hardware area, maximum operational frequency, throughput, efficiency, and power consumption.The findings prove the effectiveness of our approach in terms of computational complexity, memory requirements, and power consumption.Eventually, after reviewing and comparing our results to the existing literature, our cryptosystem's superiority becomes evident.The simulation results and performance analysis show that the proposed Chaos-Based Lightweight Cryptosystem (SCBLC) can be considered an ideal choice for securing communication in IoT devices with limited resources, making significant strides toward enhancing IoT network security.
Posterior Instance Injection Detector for Arbitrary-Oriented Object Detection From Optical Remote-Sensing Imagery

Arbitrary-oriented object detection (AOOD) from optical remote sensing imagery has to correctly generate delicate oriented boundary boxes (OBBs) and meanwhile identify their specific categories. However, how to make detectors learn delicate parameters of OBBs, especially for the crucial orientation information, and identify object category from complex background becomes a challenge task. Therefore, in this article, for exploring a better way to guide the detector to learn specific category and parametric information of OBBs, a novel one-stage anchor-free detector called Posterior Instance Injection Detector (PIIDet) is proposed for AOOD. First, as the anchor-free manner lacks prior information, an object-aware posterior guidance (OAPG) structure is proposed to generate specific-category instances used for conditioning on OBB prediction. This structure can assist the proposed PIIDet in better learning the relative parametric information of OBBs corresponding to their specific categories. Besides, to guarantee a high quality injection of specific-category instances, a new hierarchical feature fusion module is developed to establish a suitable multi-scale feature mapping space. Second, considering the negative optimization of angle regression, which is caused by the boundary discontinuity of angular periods and sudden shifts of the relation between width and height in training phase, a novel binary classification embedded angle regression space (BCE-RegSpace) is devised for providing continuous angle regression space and stable relation between width and height. Finally, extensive experiments are executed on three AOOD benchmarks (e.g., DOTA, DIOR-R and HRSC2016), and results proved that the proposed concise one-stage anchor-free PIIDet can reach the state-of-the-art (SOTA) performance and meanwhile have an impressive inference speed.