Reviewerly/exHarmony huggingface dataset:
A Deep Learning-based Model for Human Activity Recognition using Biosensors embedded into a Smart Knee Bandage

Today, biosensors are effectively utilized in illness detection, prevention, rehabilitation, patient medical monitoring, and personal health promotion. The advancement of biosensing technology offers rigorous methods for measuring an individual's motor function, which contributes significantly to restoring motor function. An smart knee bandage was recently designed to analyze and decode diverse biosignals recorded by knee bandage-integrated biosensors. This research aims to examine human activity recognition (HAR) using biosignals gathered by a smart knee bandage. To accomplish this, a deep residual neural network was established for biosensor-based HAR. For performance assessment, we utilized a significant public biosensor dataset known as the CSL-SHARE dataset, which gathers electromyography (EMG), electrogoniometer (EGM), and inertial measurement unit (IMU) signals of individuals completing 22 simplex and complicated everyday tasks. Five-fold cross-validation was used to train and assess deep learning models. According to our findings, integrating features derived from the EMG, EGM, and IMU signal produced the maximum accuracy of 91.60% and the highest F1-score of 92.13%.
A hybrid whale optimization algorithm based on equilibrium concept

This research paper proposes a hybrid Whale Optimization Algorithm (WOA) variant based on Equilibrium Optimizer (EO), named Equilibrium Whale Optimization Algorithm (EWOA). The major finding lies in an efficient hybridization of bio-inspired (WOA) and physics-based (EO) metaheuristic algorithms. Upon mathematical modelling, EWOA proposes a main architecture that combines WOA's encircling and net-bubble attacking mechanisms via EO's weight balance strategy. The proposed algorithm was tested on 23 classical, 28 constrained CEC 2017, 30 unconstrained CEC 2017, 10 CEC 2019, and 10 CEC 2020 benchmark problems, in comparison with six recently proposed state-of-the-art algorithms (including WOA and EO). EWOA outperforms other algorithms with the best statistical mean performance on 46 out of 101 functions and the most promising clustering data in the graph, respectively. The fact that EWOA could achieve best statistical SD performance on 2 of the total 5 benchmark sets proves that EWOA is competitively robust. EWOA can converge to the optimum before 50% iterations of most benchmark functions, achieving the fastest convergence rate compared to other algorithms. The major contribution thereby lies in the successful development of this hybrid algorithm, which yields better optimization efficiency than the original and other state-of-the-art algorithms in terms of statistics, convergence and clustering.
RGSO-UAV: Reverse Glowworm Swarm Optimization inspired UAV path-planning in a 3D dynamic environment

Three-dimensional path planning for UAVs is a very complex, NP-hard optimization problem. It is an effort to resolve the best feasible trajectory between the source and the destination while prohibiting collision in a dynamic environment. Many researchers have proposed different deterministic and non-deterministic solutions to the path-planning problem. However, it is still open to new promising methods to acquire better quality solutions with minimum time complexity. This article presents a novel methodology inspired by the Reverse Glowworm Swarm Optimization (RGSO) algorithm to resolve this problem for the UAV without colliding with any dynamic and static obstructions appearing in its path. A tuned path is generated after the initial one to optimize further and smoothen the trajectory. The proposed method has been extensively experimented with, and the outcomes show it outperforms the other algorithm in a three-dimensional dynamic environment in a path-planning strategy. The result shows that our proposed scheme reduces the value of the cost function by approximately 49% - 107% less than other existing approaches, and the execution time of this mechanism reduces by 2% - 61% compared with other existing methods. Thus, the proposed technique RGSO-UAV is an optimal and energy-efficient path-planning model in flying ad-hoc networks that enhance UAV's lifetime.
Optimal learning of quantum Hamiltonians from high-temperature Gibbs states

We study the problem of learning a Hamiltonian H to precision $\varepsilon$, supposing we are given copies of its Gibbs state $\rho =\exp(-\beta H)/\mathrm{Tr}(\exp(-\beta H))$ at a known inverse temperature $\beta$. Anshu, Arunachalam, Kuwahara, and Soleimanifar [AAKS21] recently studied the sample complexity (number of copies of $\rho$ needed) of this problem for geometrically local N-qubit Hamiltonians. In the high-temperature (low $\beta$) regime, their algorithm has sample complexity poly (N, 1/$\beta$, 1/$\varepsilon$) and can be implemented with polynomial, but suboptimal, time complexity. In this paper, we study the same question for a more general class of Hamiltonians. We show how to learn the coefficients of a Hamiltonian to error $\varepsilon$ with sample complexity $S=O(\log N/(\beta\varepsilon)^{2}$) and time complexity linear in the sample size, O(SN). Furthermore, we prove a matching lower bound showing that our algorithm’s sample complexity is optimal, and hence our time complexity is also optimal. In the appendix, we show that virtually the same algorithm can be used to learn H from a real-time evolution unitary $e^{-i t H}$ in a small t regime with similar sample and time complexity.
Verifiable Quantum Advantage without Structure

We show the following hold, unconditionally unless otherwise stated, relative to a random oracle with probability 1: •There are NP search problems solvable by BQP machines but not BPP machines.•There exist functions that are one-way, and even collision resistant, against classical adversaries but are easily inverted quantumly. Similar separations hold for digital signatures and CPA-secure public key encryption (the latter requiring the assumption of a classically CPA-secure encryption scheme). Interestingly, the separation does not necessarily extend to the case of other cryptographic objects such as PRGs.•There are unconditional publicly verifiable proofs of quantumness with the minimal rounds of interaction: for uniform adversaries, the proofs are non-interactive, whereas for non-uniform adversaries the proofs are two message public coin.•Our results do not appear to contradict the Aaronson-Ambanis conjecture. Assuming this conjecture, there exist publicly verifiable certifiable randomness, again with the minimal rounds of interaction.By replacing the random oracle with a concrete cryptographic hash function such as SHA2, we obtain plausible Minicrypt instantiations of the above results. Previous analogous results all required substantial structure, either in terms of highly structured oracles and/or algebraic assumptions in Cryptomania and beyond.
Blockchain-Based Internet of Things: Review, Current Trends, Applications, and Future Challenges

Advances in technology always had an impact on our lives. Several emerging technologies, most notably the Internet of Things (IoT) and blockchain, present transformative opportunities. The blockchain is a decentralized, transparent ledger for storing transaction data. By effectively establishing trust between nodes, it has the remarkable potential to design unique architectures for most enterprise applications. When it first appeared as a platform for anonymous cryptocurrency trading, such as Bitcoin, on a public network platform, blockchain piqued the interest of researchers. The chain is completed when each block connects to the previous block. The Internet of Things (IoT) is a network of networked devices that can exchange data and be managed and controlled via unique identifiers. Automation, wireless sensor networks, embedded systems, and control systems are just a few of the well-known technologies that power the IoT. Converging advancements in real-time analytics, machine learning, commodity sensors, and embedded systems demonstrate the rapid expansion of the IoT paradigm. The Internet of Things refers to the global networking of millions of networked smart gadgets that gather and exchange data. Integrating the IoT and blockchain technology would be a significant step toward developing a reliable, secure, and comprehensive method of storing data collected by smart devices. Internet-enabled devices in the IoT can send data to private blockchain networks, creating immutable records of all transaction history. As a result, these networks produce unchangeable logs of all transactions. This research looks at how blockchain technology and the Internet of Things interact to understand better how devices can communicate with one another. The blockchain-enabled Internet of Things architecture proposed in this article is a useful framework for integrating blockchain technology and the Internet of Things using the most cutting-edge tools and methods currently available. This article discusses the principles of blockchain-based IoT, consensus methods, reviews, difficulties, prospects, applications, trends, and communication between IoT nodes in an integrated framework.
FedMix: Mixed Supervised Federated Learning for Medical Image Segmentation

The purpose of federated learning is to enable multiple clients to jointly train a machine learning model without sharing data. However, the existing methods for training an image segmentation model have been based on an unrealistic assumption that the training set for each local client is annotated in a similar fashion and thus follows the same image supervision level. To relax this assumption, in this work, we propose a label-agnostic unified federated learning framework, named FedMix, for medical image segmentation based on mixed image labels. In FedMix, each client updates the federated model by integrating and effectively making use of all available labeled data ranging from strong pixel-level labels, weak bounding box labels, to weakest image-level class labels. Based on these local models, we further propose an adaptive weight assignment procedure across local clients, where each client learns an aggregation weight during the global model update. Compared to the existing methods, FedMix not only breaks through the constraint of a single level of image supervision but also can dynamically adjust the aggregation weight of each local client, achieving rich yet discriminative feature representations. Experimental results on multiple publicly-available datasets validate that the proposed FedMix outperforms the state-of-the-art methods by a large margin. In addition, we demonstrate through experiments that FedMix is extendable to multi-class medical image segmentation and much more feasible in clinical scenarios. The code is available at: https://github.com/Jwicaksana/FedMix.
Thus spoke GPT-3: Interviewing a large-language model on climate finance

This paper is an interview with a Large Language Model (LLM), namely GPT-3, on the issues of climate change. The interview should give some insights into the current capabilities of these large models which are deep neural networks with generally more than 100 billion parameters. In particular, it shows how eloquent and convincing the answers of such LLMs can be. However, it should be noted that LLMs can suffer from hallucination and their responses may not be grounded on facts. These deficiencies offer an interesting avenue for future research.
Analysis and Comparison of International Digital Competence Frameworks for Education

Different frameworks are available for assessing and developing digital competences, which poses a choice challenge for potential users. This article aims to analyze and compare international digital competence frameworks for education. The study compares characteristics such as the frameworks’ purpose, structure, competences, and levels, as well as indicators for instrument development. The results indicate that the objective, the theoretical background, and the target group define the framework characteristics. Most analyzed frameworks focus on teacher training. The comparison identified common competences: communication, collaboration, sharing, information and data, content, technical, teaching, learning, and ethics. All frameworks include profiles, objectives, descriptors, activities, examples, and cases of knowledge, skills, and attitudes. The article concludes that digital competence frameworks should be segmented by educational actors (students, teachers, and administrators) and levels (early childhood, primary, higher, and corporate) with corresponding assessment instruments.
Countering Cybercrime Risks in Financial Institutions: Forecasting Information Trends

This article aims to forecast the information trends related to the most popular cyberattacks, seen as the cyber-crimes’ consequences reflecting on the Internet. The study database was formed based on online users’ search engine requests regarding the terms “Cyberattacks on the computer systems of a financial institution”, “Cyberattacks on the network infrastructure of a financial institution”, and “Cyberattacks on the cloud infra-structure of a financial institution”, obtained with Google Trends for the period from 16 April 2017 to 4 October 2022. The authors examined the data using the Z-score, the QS test, and the method of differences of average levels. The data were found to be non-stationary with outliers and a seasonal component, so exponential smoothing was applied to reduce fluctuations and clarify the trends. As a result, the authors built additive and multiplicative cyclical and trend-cyclical models with linear, exponential, and damped trends. According to the models’ quality evaluation, the best results were shown by the trend-cyclic additive models with an exponential trend for predicting cyberattacks on computer systems and the cloud infrastructure and a trend-cyclic additive model with a damped tendency for predicting cyberattacks on the network infrastructure. The obtained results indicate that the U.S. can expect cybercrimes in the country’s financial system in the short and medium term and develop appropriate countermeasures of a financial institution to reduce potential financial losses.
Tighter generalized entropic uncertainty relations in multipartite systems

The uncertainty principle, which demonstrates the intrinsic uncertainty of nature from an information-theory perspective, is at the heart of quantum information theory. In the realm of quantum information theory, Shannon entropy is used to depict the uncertainty relation in general. A tighter lower bound for uncertainty relations facilitates more accurate predictions of measurement outcomes and more robust quantum information processing. Interestingly, the tripartite entropic uncertainty relation (EUR) can be further optimized. Renes et al. proposed a tripartite EUR [J. M. Renes and J.-C. Boileau, Phys. Rev. Lett. 103, 020402 (2009)], and subsequently, Ming et al. strengthened its lower bound in [F. Ming, D. Wang, X.-G. Fan, W.-N. Shi, L. Ye, and J.-L. Chen, Phys. Rev. A 102, 012206 (2020)]. Specifically, we derive a tighter lower bound of the tripartite EUR using the Holevo quantity. Furthermore, we generalize the tripartite EUR, that is, the generalized entropic uncertainty relation for multiple measurements in multipartite systems. As illustrations, we provide several typical examples to show that our bound is tight and outperforms the previous bound. Furthermore, our findings pave the way for using the tighter bound for the quantum secret key rate in quantum key distribution protocols and are essential for quantum precision measurements in the framework of genuine multipartite systems. By providing a close peek at the nature of uncertainty, our results may find broad applications in the security analysis of quantum cryptography.
Visualization and Visual Analytics Approaches for Image and Video Datasets: A Survey

Image and video data analysis has become an increasingly important research area with applications in different domains such as security surveillance, healthcare, augmented and virtual reality, video and image editing, activity analysis and recognition, synthetic content generation, distance education, telepresence, remote sensing, sports analytics, art, non-photorealistic rendering, search engines, and social media. Recent advances in Artificial Intelligence (AI) and particularly deep learning have sparked new research challenges and led to significant advancements, especially in image and video analysis. These advancements have also resulted in significant research and development in other areas such as visualization and visual analytics, and have created new opportunities for future lines of research. In this survey article, we present the current state of the art at the intersection of visualization and visual analytics, and image and video data analysis. We categorize the visualization articles included in our survey based on different taxonomies used in visualization and visual analytics research. We review these articles in terms of task requirements, tools, datasets, and application areas. We also discuss insights based on our survey results, trends and patterns, the current focus of visualization research, and opportunities for future research.
Auguring Fake Face Images Using Dual Input Convolution Neural Network

Deepfake technology uses auto-encoders and generative adversarial networks to replace or artificially construct fine-tuned faces, emotions, and sounds. Although there have been significant advancements in the identification of particular fake images, a reliable counterfeit face detector is still lacking, making it difficult to identify fake photos in situations with further compression, blurring, scaling, etc. Deep learning models resolve the research gap to correctly recognize phony images, whose objectionable content might encourage fraudulent activity and cause major problems. To reduce the gap and enlarge the fields of view of the network, we propose a dual input convolutional neural network (DICNN) model with ten-fold cross validation with an average training accuracy of 99.36 ± 0.62, a test accuracy of 99.08 ± 0.64, and a validation accuracy of 99.30 ± 0.94. Additionally, we used 'SHapley Additive exPlanations (SHAP) ' as explainable AI (XAI) Shapely values to explain the results and interoperability visually by imposing the model into SHAP. The proposed model holds significant importance for being accepted by forensics and security experts because of its distinctive features and considerably higher accuracy than state-of-the-art methods.
Grid-Search Integrated Optimized Support Vector Machine Model for Breast Cancer Detection

Breast cancer is a common and highly heterogeneous cancer worldwide. Rapid detection and early diagnosis are essential in its treatment, but it is challenging due to mammogram’s uncertainty. Based on machine learning, operative clinical systems can be implemented to save patients’ lives. This study proposes an optimized support vector machine (SVM) model to predict breast cancer using the grid search method to find the best hyper-parameters. For validation, we offer an in-depth experimental study and we achieve, for Benign|Malignant|Average cases, accuracy of 98.69%|99.72%|99.0%, precision of 96.0%|100.0%|98.0%, recall of 100.0%|98.0%|98.0%, and Fl-score of 98.0%|97.0%|98.0% respectively. Comparison is made between the tuned hyper-parameter and default hyper-parameter performance. SVM performance with default parameters is 96%, while the maximum accuracy is achieved by hyper-parameters tuned. An SVM model is 99%, which can be a better cancer detection system. The results show performance improves when the best hyperparameters are used for SVM training. Thus, the analysis and comparison indicate that our stated system is better than the state-of-the-art ML-based breast cancer detection system.
Coupled retrieval of heavy metal nickel concentration in agricultural soil from spaceborne hyperspectral imagery

Widespread soil contamination endangers public health and undermines global attempts to achieve the United Nations Sustainable Development Goals. Due to the lack of relevant studies and low precision of spaceborne spectroscopy, estimating soil heavy metal concentrations is challenging. In this study, we developed a coupled retrieval to qualify the heavy metal nickel (Ni) concentration in agricultural soil from spaceborne hyperspectral imagery. The retrieval couples spectral feature extraction from multi-scale discrete wavelet transform (DWT) and dimension reduction (DR), optimal band combination algorithm to five machine learning retrieval models using tree-based ensemble learning, neural network-based, and kernel-based. The comparison between the retrievals and Ni measurements shows that the DWT combined with t-distributed stochastic neighbor embedding (tSNE) coupled extreme gradient boosting (XGboost) retrieval model exhibited the best prediction for the validation dataset. Moreover, due to the integration of six statistical indicators of model performance and the fitted slope of the regression line, the retrieval framework can produce more robust and accurate predictions than those that rely on correlation coefficients. The demonstrated potential of spaceborne hyperspectral remote sensing to provide accurate quantitative measurements of soil heavy metal concentrations will serve as a reference for agricultural plot applications worldwide.
Benzene: Scaling Blockchain With Cooperation-Based Sharding

Sharding has been considered as a prominent approach to enhance the limited performance of blockchain. However, most sharding systems leverage a non-cooperative design, which lowers the fault tolerance resilience due to the decreased mining power as the consensus execution is limited to each separated shard. To this end, we present Benzene, a novel sharding system that enhances the performance by cooperation-based sharding while defending the per-shard security. First, we establish a double-chain architecture for function decoupling. This architecture separates transaction-recording functions from consensus-execution functions, thereby enabling the cross-shard cooperation during consensus execution while preserving the concurrency nature of sharding. Second, we design a cross-shard block verification mechanism leveraging Trusted Execution Environment (TEE), via which miners can verify blocks from other shards during the cooperation process with the minimized overheads. Finally, we design a voting-based consensus protocol for cross-shard cooperation. Transactions in each shard are confirmed by all shards that simultaneously cast votes, consequently achieving an enhanced fault tolerance and lowering the confirmation latency. We implement Benzene and conduct both prototype experiments and large-scale simulations to evaluate the performance of Benzene. Results show that Benzene achieves superior performance than existing sharding/non-sharding blockchain protocols. In particular, Benzene achieves a linearly-improved throughput with the increased number of shards (e.g., 32,370 transactions per second with 50 shards) and maintains a lower confirmation latency than Bitcoin (with more than 50 shards). Meanwhile, Benzene maintains a fixed fault tolerance at 1/3 even with the increased number of shards.