Reviewerly/exHarmony huggingface dataset:
Listening to the sociomaterial: When thinking through making extends beyond the individual

Current discussions in practice-led design research concentrate on the thoughtful dimension of working with materials and the problem of analyzing acts of making at the scale of individual practice. Informed by the theoretical tenets of sociomateriality, this paper encloses a practice-led study addressing both issues. We followed the making of a collaboration-based, material-intensive workshop designed by the first author, where he also participated as a maker. This approach afforded us an insider's perspective to delineate how thinking through making transcends the individual and distributes across the entangled becoming of the social and the material. Our findings shed light on four features of sociomaterial assemblages that warrant critical attention in practice-led design research: temporality, contingency, epistemic capability, and analytical agency.
Secure and Lightweight Blockchain-Enabled Access Control for Fog-Assisted IoT Cloud Based Electronic Medical Records Sharing

As for the advancement of IoT and cloud computing in healthcare, outsourcing encrypted Electronic medical records (EMRs) created by the aggregation of medical treatment applications and health data collected from IoT devices enables high accessibility, effective collaboration, and zero computational operation cost. Current applications and research works generally concern the privacy of the finest EMRs that are encrypted with secure and lightweight cryptographic protocols before they are outsourced to the cloud. However, this process does not consider the security and privacy of the data collected by IoT devices, where the data being transferred can be leaked before they are aggregated. Furthermore, existing IoT-cloud based access control solutions have not addressed the outsourced encryption, privacy of IoT data transmission and aggregation, and the policy update of the EMRs in an integrated manner. In this paper, we propose an access control scheme called LightMED which provides secure, fine-grained, and scalable EMR sharing in a cloud-based environment integrated with fog computing, CP-ABE, and blockchain technology. We propose a secure IoT data transmission and aggregation method based on lightweight encryption and digital signing. At the core, we introduce outsourced encryption with a privacy-preserving access policy scheme and an outsourced encryption and decryption algorithm leveraged by the collaboration between fog nodes and blockchain. In addition, we introduce a novel lightweight policy update algorithm to enable the data owners of EMRs to effectively manage their policies in a secure and effective manner. Finally, we performed the comparative analysis to illustrate the computation cost and conducted experiments to evaluate the performance of our scheme and related works. The experimental results showed that our scheme outperformed existing works since it yielded least processing cost of both encryption and decryption at end-users’ devices, which demonstrates the higher efficiency and practicality of our scheme.
Multi-Camera 3D Object Detection for Autonomous Driving Using Deep Learning and Self-Attention Mechanism

In the absence of depth-centric sensors, 3D object detection using only conventional cameras becomes ill-posed and inaccurate due to the lack of depth information in the RGB image. We propose a multi-camera perception solution to predict the 3D properties of the vehicle obtained from the aggregated information from multiple static infrastructure-installed cameras. While a multi-bin regression loss has been adopted to predict the orientation of a 3D bounding box using a convolutional neural network, combining it with the geometrical constraints of a 2D bounding box to form a 3D bounding box is not accurate enough for all the driving scenarios and orientations. This paper leverages a vision transformer that overcomes the drawbacks of convolutional neural networks when there are no external LiDAR or pseudo-LiDAR pre-trained datasets available for depth map estimation, particularly in occluded regions. By combining the predicted 3D boxes from various cameras using an average weighted score algorithm, we determine the best bounding box with the highest confidence score. Comprehensive simulations for performance analysis are shown from the results obtained by utilizing the KITTI standard data generated from the CARLA simulator.
A Survey of Advancements in Real-Time Sign Language Translators: Integration with IoT Technology

Real-time sign language translation systems are of paramount importance in enabling communication for deaf and hard-of-hearing individuals. This population relies on various communication methods, including sign languages and visual techniques, to interact with others. While assistive technologies, such as hearing aids and captioning, have improved their communication capabilities, a significant communication gap still exists between sign language users and non-users. In order to bridge this gap, numerous sign language translation systems have been developed, encompassing sign language recognition and gesture-based controls. Our research aimed to analyze the advancements in real-time sign language translators developed over the past five years and their integration with IoT technology. By closely examining these technologies, we aimed to attain a deeper comprehension of their practical applications and evolution in the domain of sign language translation. We analyzed the current literature, technical reports, and conference papers on real-time sign language translation systems. Our results offer insights into the current state of the art in real-time sign language translation systems and their integration with IoT technology. We also provide a deep understanding of the recent developments in sign language translation technology and the potential for their fusion with Internet of Things technology to improve communication and promote inclusivity for the deaf and hard-of-hearing population.
An efficient and secure compression technique for data protection using burrows-wheeler transform algorithm

Data stored on physical storage devices and transmitted over communication channels often have a lot of redundant information, which can be reduced through compression techniques to conserve space and reduce the time it takes to transmit the data. The need for adequate security measures, such as secret key control in specific techniques, raises concerns about data exposure to potential attacks. Encryption plays a vital role in safeguarding information and maintaining its confidentiality by utilizing a secret key to make the data unreadable and unalterable. The focus of this paper is to tackle the challenge of simultaneously compressing and encrypting data without affecting the efficacy of either process. The authors propose an efficient and secure compression method incorporating a secret key to accomplish this goal. Encoding input data involves scrambling it with a generated key and then transforming it through the Burrows-Wheeler Transform (BWT). Subsequently, the output from the BWT is compressed through both Move-To-Front Transform and Run-Length Encoding. This method blends the cryptographic principles of confusion and diffusion into the compression process, enhancing its performance. The proposed technique is geared towards providing robust encryption and sufficient compression. Experimentation results show that it outperforms other techniques in terms of compression ratio. A security analysis of the technique has determined that it is susceptible to the secret key and plaintext, as measured by the unicity distance. Additionally, the results of the proposed technique showed a significant improvement with a compression ratio close to 90% after passing all the test text files.
HMM-based gesture recognition for eye-swipe typing

Eye-swipe typing requires users to simply look in the vicinity of the keys forming the desired word, similar to swiping their finger on a touch screen device. This work presents a novel HMM-based approach for swipe typing which can be used with any eye movement recording technique. In this study, three different HMM-based methods are developed, tested, and compared to the state-of-the-art performing LCSMapping algorithm with eye movement data acquired from the electrooculogram (EOG). When tested by ten subjects, the top performing Key-based HMM yielded an average top-five rate of 91.00±6.63% in comparison to an average top-five rate of 76.00±12.61% achieved by the LCSMapping algorithm. This study also presents the analysis of a real-time eye controlled swipe typing application which yielded an average typing speed of 12.85±2.14 WPM in contrast to an average typing speed of 3.83±1.24 WPM achieved using a dwell-based alternative.
Geographic information system‐based prediction of solar power plant production using deep neural networks

Abstract The study aims to predict solar energy generation to ensure the successful operation of solar power plants. This objective is crucial in light of the increasing energy demand, global warming concerns, and greenhouse gas emissions. To achieve this, the study employs multiple linear regression and feature selection techniques to calculate energy generation. Additionally, long short‐term memory (LSTM) is used to predict energy generation levels based on climate conditions. Furthermore, the spatial distribution of energy generation is analyzed using inverse distance weighting. The results of the study reveal that temperature, solar radiation, relative humidity, wind speed, wind direction, and vapor pressure deficit are the most significant parameters for predicting energy generation. The LSTM method proves to be highly accurate in predicting fluctuating energy generation patterns. Notably, the southern regions of the study area exhibit a greater potential for energy generation compared to the northern regions. Approximately 30% of the region generates over 1400 kWh, with the southern areas, characterized by hot and dry climates, producing around 1500 kWh, while the northern regions, with cold and humid climates, generate approximately 1100 kWh.
Physics-added neural networks: An image-based deep learning for material printing system

Drop-on-demand material printing condition optimization with empirical knowledge based on the trial-and-error method and implementation of computational fluid dynamics (CFD) requires a significant amount of time and costs. In addition, current state-of-the-art neural network algorithms can reduce such time and cost by predicting droplet formation over a temporal domain; however, insufficient data and physical inconsistencies across output results limit their extrapolative prediction performance. In this study, we performed multi-GPU-implemented CFD simulations to collect large-scale training data, and we propose a novel algorithm for embedding physics knowledge in neural networks: physics-added neural networks. Integrating an image-to-image deep learning algorithm with our novel physics-embedding module, we obtain more consistent physical characteristics of the material printing process, such as mass and energy. Furthermore, by guiding the neural network to adhere to mass and energy conservation laws, the possible solution space of neural network training is restricted, which enhances convergence stability and decreases training time.
Federated Learning Enabled Edge Computing Security for Internet of Medical Things: Concepts, Challenges and Open Issues

The rapid emergence of the Internet of Medical Things (IoMT) has resulted in the ubiquitous home health diagnostic network. Despite these substantial advancements in recent years, cloud-based healthcare applications continue to be underutilized due to their inability to meet demanding security, privacy, and service quality standards. Amidst these advancements and their potential to collect behavioral patterns, the devices like smart fitness watches are still neglected in the healthcare system. Edge computing and federated learning (FL) have gained traction as a possible option in such situations. In this chapter, we analyze the services of edge computing and FL in medicine to evaluate the potential of intelligent processing of clinical visual data. The duo allows remote healthcare centers with limited diagnostic skills to securely benefit from multi-modal data. This study explains the motivation behind integrating FL with edge computing for IoMT and also the areas where the duo is applied for the betterment of IoMT services. Finally, this chapter elaborates on the challenges and future directions.
MDGAD: Meta domain generalization for distribution drift in anomaly detection

Anomalies often experience distribution drift over time in anomaly detection. Traditional anomaly detection methods detect anomalies with the same distribution effectively, but it is difficult to detect anomalies with changing distributions. In this paper, we propose a meta domain generalization based anomaly detection (MDGAD) framework to detect anomalies when distribution drifts. The framework first divides the data into a series of subsets. We measure the domain shift between sets with a class-sensitive distance metric, and merge similar sets, thereby generating different source domains to enhance domain diversity. We utilize the distribution shifts that existed on these source domains to simulate the distribution shifts that would be encountered when the model was applied. Meta learning is employed during training. First, the network is updated once on the meta-training set, and then another update is performed on the meta-verification set. These two gradients together determine the update direction of the network. At the same time, our framework will complete the domain alignment task on multiple source domains to extract domain invariant features and enhance the generalization of feature learning. We use Devnet as the base model and test our framework on 5 simulated data sets. The results confirm that our MDGAD outperforms current popular algorithms in detecting unknown anomalies and has better robustness. In addition, we also test our framework on real financial datasets to demonstrate the effectiveness.
Stochastic momentum methods for non-convex learning without bounded assumptions

Stochastic momentum methods are widely used to solve stochastic optimization problems in machine learning. However, most of the existing theoretical analyses rely on either bounded assumptions or strong stepsize conditions. In this paper, we focus on a class of non-convex objective functions satisfying the Polyak-Łojasiewicz (PL) condition and present a unified convergence rate analysis for stochastic momentum methods without any bounded assumptions, which covers stochastic heavy ball (SHB) and stochastic Nesterov accelerated gradient (SNAG). Our analysis achieves the more challenging last-iterate convergence rate of function values under the relaxed growth (RG) condition, which is a weaker assumption than those used in related work. Specifically, we attain the sub-linear rate for stochastic momentum methods with diminishing stepsizes, and the linear convergence rate for constant stepsizes if the strong growth (SG) condition holds. We also examine the iteration complexity for obtaining an ϵ-accurate solution of the last-iterate. Moreover, we provide a more flexible stepsize scheme for stochastic momentum methods in three points: (i) relaxing the last-iterate convergence stepsize from square summable to zero limitation; (ii) extending the minimum-iterate convergence rate stepsize to the non-monotonic case; (iii) expanding the last-iterate convergence rate stepsize to a more general form. Finally, we conduct numerical experiments on benchmark datasets to validate our theoretical findings.
Best (orthogonal) fitting ellipsoid with quaternions

AbstractThe aim of this study is the determination of the best fit ellipsoid to given points by quaternions. The problem of the fitting ellipsoid is frequently encountered in image processing, computer games, medicine, engineering and science applications, geodesy, etc. The ellipsoid fitting problem is the process of determining the ellipsoid that best fits a given set of points in 3D. In the fitting process, it is generally done over two models. The first of these is the algebraic method and the second one is orthogonal (geometric) method. In this study, we tried to solve the problem of algebraic and orthogonal ellipsoid fitting based on Euler angles for the first time over quaternions. The superiority of quaternions over Euler rotation angles is well known. In addition, the variance–covariance matrix of the parameters of the fitted ellipsoid will also be calculated. Numerical applications show that the proposed method can be used successfully.Keywords: Orthogonal fittingAlgebraic fittingQuaternionVariance–covariance matrixGeometric parameters of fitted ellipsoidLeast squares method AcknowledgementsWe thank the Editor and anonymous reviewers for their valuable comments and suggestions which clearly helped to improve this manuscript.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsSebahattin BektasSebahattin Bektas is a lecturer in the division of Geodesy in Department of Geomatics Engineering in Engineering Faculty at Ondokuz Mayis University (OMU), Turkey. He received PhD degree with 3D Network Adjustment titled thesis in 1991. From 1984 until today he worked as a researcher. Since 1999, he is Professor at OMU. His research interests are, geodesy, surface fitting, optimization, adjustment, engineering education.
Transformer-Based Context Condensation for Boosting Feature Pyramids in Object Detection

Abstract Current object detectors typically have a feature pyramid (FP) module for multi-level feature fusion (MFF) which aims to mitigate the gap between features from different levels and form a comprehensive object representation to achieve better detection performance. However, they usually require heavy cross-level connections or iterative refinement to obtain better MFF result, making them complicated in structure and inefficient in computation. To address these issues, we propose a novel and efficient context modeling mechanism that can help existing FPs deliver better MFF results while reducing the computational costs effectively. In particular, we introduce a novel insight that comprehensive contexts can be decomposed and condensed into two types of representations for higher efficiency. The two representations include a locally concentrated representation and a globally summarized representation, where the former focuses on extracting context cues from nearby areas while the latter extracts general contextual representations of the whole image scene as global context cues. By collecting the condensed contexts, we employ a Transformer decoder to investigate the relations between them and each local feature from the FP and then refine the MFF results accordingly. As a result, we obtain a simple and light-weight Transformer-based Context Condensation (TCC) module, which can boost various FPs and lower their computational costs simultaneously. Extensive experimental results on the challenging MS COCO dataset show that TCC is compatible to four representative FPs and consistently improves their detection accuracy by up to 7.8% in terms of average precision and reduce their complexities by up to around 20% in terms of GFLOPs, helping them achieve state-of-the-art performance more efficiently. Code will be released at https://github.com/zhechen/TCC .
Quantum emulation of the transient dynamics in the multistate Landau-Zener model

Quantum simulation is one of the most promising near term applications of quantum computing. Especially, systems with a large Hilbert space are hard to solve for classical computers and thus ideal targets for a simulation with quantum hardware. In this work, we study experimentally the transient dynamics in the multistate Landau-Zener model as a function of the Landau-Zener velocity. The underlying Hamiltonian is emulated by superconducting quantum circuit, where a tunable transmon qubit is coupled to a bosonic mode ensemble comprising four lumped element microwave resonators. We investigate the model for different initial states: Due to our circuit design, we are not limited to merely exciting the qubit, but can also pump the harmonic modes via a dedicated drive line. Here, the nature of the transient dynamics depends on the average photon number in the excited resonator. The greater effective coupling strength between qubit and higher Fock states results in a quasi-adiabatic transition, where coherent quantum oscillations are suppressed without the introduction of additional loss channels. Our experiments pave the way for more complex simulations with qubits coupled to an engineered bosonic mode spectrum.
Optimizing SPARQL queries over decentralized knowledge graphs

While the Web of Data in principle offers access to a wide range of interlinked data, the architecture of the Semantic Web today relies mostly on the data providers to maintain access to their data through SPARQL endpoints. Several studies, however, have shown that such endpoints often experience downtime, meaning that the data they maintain becomes inaccessible. While decentralized systems based on Peer-to-Peer (P2P) technology have previously shown to increase the availability of knowledge graphs, even when a large proportion of the nodes fail, processing queries in such a setup can be an expensive task since data necessary to answer a single query might be distributed over multiple nodes. In this paper, we therefore propose an approach to optimizing SPARQL queries over decentralized knowledge graphs, called Lothbrok. While there are potentially many aspects to consider when optimizing such queries, we focus on three aspects: cardinality estimation, locality awareness, and data fragmentation. We empirically show that Lothbrok is able to achieve significantly faster query processing performance compared to the state of the art when processing challenging queries as well as when the network is under high load.