Reviewerly/exHarmony huggingface dataset:
Two-qubit gate using conditional driving for highly detuned Kerr nonlinear parametric oscillators

A Kerr nonlinear parametric oscillator (KPO) is one of the promising devices used to realize qubits for universal quantum computing. The KPO can stabilize two coherent states with opposite phases, yielding a quantum superposition called a Schr\"odinger cat state. Universal quantum computing with KPOs requires three kinds of quantum gates: ${R}_{z},{R}_{x}$, and ${R}_{zz}$ gates. We theoretically propose a two-qubit gate ${R}_{zz}$ for highly detuned KPOs. In the proposed scheme, we add a parametric drive for the first KPO. This leads to the ${R}_{zz}$ gate based on the driving of the second KPO depending on the first-KPO state, which we call ``conditional driving.'' First, we perform simulations using a conventional KPO Hamiltonian derived from a superconducting-circuit model under some approximations and evaluate the gate fidelity. Next, we also perform numerical simulations of the two-qubit gate using the superconducting-circuit model without the approximations. The simulation results indicate that the conditional-driving gates can be implemented with high fidelity ($&gt;99.9%$) for rotation angles required for universality.
Coalition based utility and efficiency optimization for multi-task federated learning in Internet of Vehicles

With the emergence of the sixth generation (6G) communication technologies, massive infrastructures will be densely deployed and the number of data will be generated exponentially. Massive data collected by vehicles can be used to train a machine learning model, which is an effective way of implementing intelligent services in the Internet of Vehicles (IoV). However, centralized training leads to communication congestion and user privacy leakage. To address these problems and improve efficiency, federated learning has been proposed. Most current studies on federated learning consider all users performing only a single task at a certain time, which results in low network utility and long task execution time. Because some vehicles may have similar data, we propose a multi-task federated learning model where vehicles simultaneously execute tasks. To jointly maximize the network utility and efficiency of the multi-task federated learning in the 6G-enabled IoV, we design a task-driven vehicular coalition algorithm considering vehicle selection and wireless resource allocation. Moreover, we derive a convex objective function from network utility function and loss function with constraints through a series of mathematical analyses. Finally, we verify that our proposed algorithm with low complexity can improve the utility and efficiency of the multi-task federated learning in the 6G-enabled IoV.
Application of Generative Adversarial Networks and Shapley Algorithm Based on Easy Data Augmentation for Imbalanced Text Data

Imbalanced data constitute an extensively studied problem in the field of machine learning classification because they result in poor training outcomes. Data augmentation is a method for increasing minority class diversity. In the field of text data augmentation, easy data augmentation (EDA) is used to generate additional data that would otherwise lack diversity and exhibit monotonic sentence patterns. Generative adversarial network (GAN) models can generate diverse sentence patterns by using the probability corresponding to each word in a language model. Therefore, hybrid EDA and GAN models can generate highly diverse and appropriate sentence patterns. This study proposes a hybrid framework that employs a generative adversarial network and Shapley algorithm based on easy data augmentation (HEGS) to improve classification performance. The experimental results reveal that the HEGS framework can generate highly diverse training sentences to form balanced text data and improve text classification performance for minority classes.
IoT Device Identification Using Directional Packet Length Sequences and 1D-CNN

With the large-scale application of the Internet of Things (IoT), security issues have become increasingly prominent. Device identification is an effective way to secure IoT environment by quickly identifying the category or model of devices in the network. Currently, the passive fingerprinting method used for IoT device identification based on network traffic flow mostly focuses on protocol features in packet headers but does not consider the direction and length of packet sequences. This paper proposes a device identification method for the IoT based on directional packet length sequences in network flows and a deep convolutional neural network. Each value in a packet length sequence represents the size and transmission direction of the corresponding packet. This method constructs device fingerprints from packet length sequences and uses convolutional layers to extract deep features from the device fingerprints. Experimental results show that this method can effectively recognize device identity with accuracy, recall, precision, and f1-score over 99%. Compared with methods using traditional machine learning and feature extraction techniques, our feature representation is more intuitive, and the classification model is effective.
Defining numeral classifiers and identifying classifier languages of the world

Abstract This paper presents a precise definition of numeral classifiers, steps to identify a numeral classifier language, and a database of 3,338 languages, of which 723 languages have been identified as having a numeral classifier system. The database, named World Atlas of Classifier Languages (WACL), has been systematically constructed over the last 10 years via a manual survey of relevant literature and also an automatic scan of digitized grammars followed by manual checking. The open-access release of WACL is thus a significant contribution to linguistic research in providing (i) a precise definition and examples of how to identify numeral classifiers in language data and (ii) the largest dataset of numeral classifier languages in the world. As such it offers researchers a rich and stable data source for conducting typological, quantitative, and phylogenetic analyses on numeral classifiers. The database will also be expanded with additional features relating to numeral classifiers in the future in order to allow more fine-grained analyses.
User Experience of Wearable Technologies: A Comparative Analysis of Textile-Based and Accessory-Based Wearable Products

Wearable technology components have been integrated into accessories and then clothes over the evolutionary process. In the future, it is predicted that wearable technologies will be patchable and implantable. Despite progress in wearable technologies, there is an adaptation problem in wearable technologies. One of the most important reasons for this problem is the negative product experiences of the users. In this study, we investigated the user experiences of accessory-based and textile-based wearable technologies. Due to the temporality of the user experience, a mixed method was developed, including a survey for the expectations before use, a diary-interview for during use, and a semi-structured interview for after use. The data obtained with these methods were analyzed with SPSS and NVivo programs and the two different wearing-style experiences were compared. As a result of these analyses, it has been revealed that textile-based wearable technologies give a more positive experience during and after use and may be preferred over accessory-based wearable technologies in terms of wearability.
Enhancement of the CAST Block Algorithm Based on Novel S-Box for Image Encryption

Background and Aim: due to the rapid growth of data communication and multimedia system applications, security becomes a critical issue in the communication and storage of images. This study aims to improve encryption and decryption for various types of images by decreasing time consumption and strengthening security. Methodology: An algorithm is proposed for encrypting images based on the Carlisle Adams and Stafford Tavares CAST block cipher algorithm with 3D and 2D logistic maps. A chaotic function that increases the randomness in the encrypted data and images, thereby breaking the relation sequence through the encryption procedure, is introduced. The time is decreased by using three secure and private S-Boxes rather than using six S-Boxes, as in the traditional method. Moreover, the CAST encryption algorithm was modified to be used on the private keys and substitution stage (S-Boxes), with the keys and S-Boxes of the encryption algorithm being generated according to the 2D and 3D chaotic map functions. The proposed system passed all evaluation criteria, including (MSE, PSNR, EQ, MD, SC, NC, AD, SNR, SIM, MAE, Time, CC, Entropy, and histograms). Results: Moreover, the results also illustrate that the created S-Boxes passed all evaluation criteria; compared with the results of the traditional method that was used in creating S-Box, the proposed method achieved better results than other methods used in the other works. The proposed solution improves the entropy which is between (7.991–7.999), reduces the processing time which is between (0.5–11 s/Images), and improves NCPR, which is between (0.991–1). Conclusions: The proposed solution focuses on reducing the total processing time for encryption and decryption and improving transmission security. Finally, this solution provides a fast security system for surgical telepresence with secure real-time communication. The complexity of this work needs to know the S-Box creation method used, the chaotic method, the values of the chaotic parameters, and which of these methods was used in the encryption process.
Stratified K-fold cross validation optimization on machine learning for prediction

Cervical is the second most common malignant tumor in women, with 341,000 deaths worldwide in 2020, almost 80% of which occur in developing countries. One of the causes is infection with Human papillomavirus (HPV) types 16 and 18. The increasing incidence of cervical cancer in Indonesia makes this disease must be treated seriously because it is one of the main causes of death. In addition to the virus, external factors can be one of the causes. The high mortality rate in patients is caused by the patient's awareness of the emergence of cervical cancer which is only seen when it enters the final stage. One of the efforts to reduce the number of sufferers is to implement cervical cancer detection. Early detection of cervical cancer can also be identified by looking at external factors, such as behavioral factors, intentions, attitudes, norms, perceptions, motivations, social support, and empowerment. However, the data used has an imbalance in the distribution of the target class, namely more negative samples than positive ones. To overcome this, a technique called Stratified K-Fold Cross-Validation (SKCV) is used. Evaluation of the accuracy value using the Confusion matrix to determine the performance of each model. The best performance of the five classification algorithms used is 96 percent (RF), 94 percent (LR), 92 percent (XGBoost), 90 percent (KNN), and 88 percent (NB). The results show that the model formed by RF-based SKCV has the highest accuracy of other models.
Cascaded Reinforcement Learning Agents for Large Action Spaces in Autonomous Penetration Testing

Organised attacks on a computer system to test existing defences, i.e., penetration testing, have been used extensively to evaluate network security. However, penetration testing is a time-consuming process. Additionally, establishing a strategy that resembles a real cyber-attack typically requires in-depth knowledge of the cybersecurity domain. This paper presents a novel architecture, named deep cascaded reinforcement learning agents, or CRLA, that addresses large discrete action spaces in an autonomous penetration testing simulator, where the number of actions exponentially increases with the complexity of the designed cybersecurity network. Employing an algebraic action decomposition strategy, CRLA is shown to find the optimal attack policy in scenarios with large action spaces faster and more stably than a conventional deep Q-learning agent, which is commonly used as a method for applying artificial intelligence to autonomous penetration testing.
Human Activity Recognition System For Moderate Performance Microcontroller Using Accelerometer Data And Random Forest Algorithm

There has been increasing interest in the application of artificial intelligence technologies to improve the quality of support services in healthcare. Some constraints, such as space, infrastructure, and environmental conditions, present challenges with assistive devices for humans. This paper proposed a wearable-based real-time human activity recognition system to monitor daily activities. The classification was done directly on the device, and the results could be checked over the internet. The accelerometer data collection application was developed on the device with a sampling frequency of 20Hz, and the random forest algorithm was embedded in the hardware. To improve the accuracy of the recognition system, a feature vector of 31 dimensions was calculated and used as an input per time window. Besides, the dynamic window method applied by the proposed model allowed us to change the data sampling time (1-3 seconds) and increase the performance of activity classification. The experiment results showed that the proposed system could classify 13 activities with a high accuracy of 99.4%. The rate of correctly classified activities was 96.1%. This work is promising for healthcare because of the convenience and simplicity of wearables.
Analysis of Thermal Imaging Performance under Extreme Foggy Conditions: Applications to Autonomous Driving

Object detection is recognized as one of the most critical research areas for the perception of self-driving cars. Current vision systems combine visible imaging, LIDAR, and/or RADAR technology, allowing perception of the vehicle's surroundings. However, harsh weather conditions mitigate the performances of these systems. Under these circumstances, thermal imaging becomes the complementary solution to current systems not only because it makes it possible to detect and recognize the environment in the most extreme conditions, but also because thermal images are compatible with detection and recognition algorithms, such as those based on artificial neural networks. In this paper, an analysis of the resilience of thermal sensors in very unfavorable fog conditions is presented. The goal was to study the operational limits, i.e., the very degraded fog situation beyond which a thermal camera becomes unreliable. For the analysis, the mean pixel intensity and the contrast were used as indicators. Results showed that the angle of view (AOV) of a thermal camera is a determining parameter for object detection in foggy conditions. Additionally, results show that cameras with AOVs 18° and 30° are suitable for object detection, even under thick fog conditions (from 13 m meteorological optical range). These results were extended using object detection software, with which it is shown that, for the pedestrian, a detection rate ≥90% was achieved using the images from the 18° and 30° cameras.
Human–machine interface-based wheelchair control using piezoelectric sensors based on face and tongue movements

Hand-free control of assistive mobility devices has been developed to serve people with movement disabilities at all levels. In this study, we demonstrate a human–machine interface (HMI) system that uses piezoelectric sensors to translate face and tongue movements. This study addresses two issues. First, we used six piezoelectric sensors to acquire muscular facial signals to observe the sensor positions and features during winking and tongue movements. Second, we verified the proposed HMI for online simulated wheelchair control. Twelve volunteers participated in the experiment. A maximum classification accuracy of 98.0% from the maximum and mean parameters could be achieved using the linear discriminant analysis and K-nearest neighbors classification algorithms. Using the proposed algorithm, command translation patterns for command translation reached more than 95% of the average classification accuracy with 0.5 s of the window for command creation. For online control of the simulated wheelchair, the results showed high efficiency based on the time condition. The combination of winking and tongue actions results in a steering time of the same magnitude as that of joystick-based control, which is less than twice the time of a joystick. Hence, the proposed system can be further implemented in a powered wheelchair for quadriplegic patients who retain control in the face or tongue muscles.
B-YOLOX-S: A Lightweight Method for Underwater Object Detection Based on Data Augmentation and Multiscale Feature Fusion

With the increasing maturity of underwater agents-related technologies, underwater object recognition algorithms based on underwater robots have become a current hotspot for academic and applied research. However, the existing underwater imaging conditions are poor, the images are blurry, and the underwater robot visual jitter and other factors lead to lower recognition precision and inaccurate positioning in underwater target detection. A YOLOX-based underwater object detection model, B-YOLOX-S, is proposed to detect marine organisms such as echinus, holothurians, starfish, and scallops. First, Poisson fusion is used for data amplification at the input to balance the number of detected targets. Then, wavelet transform is used to perform Style Transfer on the enhanced images to achieve image restoration. The clarity of the images and detection targets is further increased and the generalization of the model is enhanced. Second, a combination of BIFPN-S and FPN is proposed to fuse the effective feature layer obtained by the Backbone layer to enhance the detection precision and accelerate model detection. Finally, the localization loss function of the prediction layer in the network is replaced by EIoU_Loss to heighten the localization precision in detection. Experimental results comparing the B-YOLOX-S algorithm model with mainstream algorithms such as FasterRCNN, YOLOV3, YOLOV4, YOLOV5, and YOLOX on the URPC2020 dataset show that the detection precision and detection speed of the algorithm model have obvious advantages over other algorithm networks. The average detection accuracy mAP value is 82.69%, which is 5.05% higher than the benchmark model (YOLOX-s), and the recall rate is 8.03% higher. Thus, the validity of the algorithmic model proposed in this paper is demonstrated.
A Decentralized Architecture for Trusted Dataset Sharing Using Smart Contracts and Distributed Storage

The data economy is based on data and information sharing and tremendously impacts society as it facilitates innovative collaborations and decision-making strategies. Nonetheless, most dataset-sharing solutions rely on a centralized authority that rules data ownership, availability, and accessibility. Recent works have explored the integration of distributed storage and blockchain to enhance decentralization, data access, and smart contracts for automating the interactions between actors and data. However, current solutions propose a smart contract design limiting the system's scalability in terms of actors and shared datasets. Furthermore, little is known about the performance of these architectures when using distributed storage instead of centralized storage approaches. This paper proposes a scalable architecture called DeBlock for data sharing in a trusted way among unreliable actors. The architecture integrates a public blockchain that provides a transparent record of datasets and interactions, with a distributed storage for data storage in a completely decentralized way. Furthermore, the architecture provides a smart-contract design for a transparent catalog of datasets, actors, and interactions with efficient search and retrieval capabilities. To assess the system's feasibility, robustness, and scalability, we implement a prototype using the Ethereum blockchain and leveraging two decentralized storage protocols, Swarm and IPFS. We evaluate the performance of our proposed system in different scenarios (e.g., varying the amount and size of the shared datasets). Our results demonstrate that our proposal outperforms benchmarks in gas consumption, latency, and resource requirements, especially when increasing the number of actors and shared datasets.
Probit Cryptographic Blockchain for Secure Data Transmission in Intelligent Transportation Systems

&lt;p&gt;To address the current security challenges, Digital Twin (DT) models and strategies are to be applied to improve security, privacy, and safety in intelligent transportation systems. However, the existing algorithm was failed to address the security and privacy issues for data between the vehicles. To enhance the security of data transmission, a novel Probit Regressive Davis Mayer Kupyna Cryptographic Hash Blockchain (PRDMKCHB) technique is proposed. Initially, the source vehicle finds the nearest vehicle to transmit the data based on the trust value using the probit regression function. After finding the neighboring nodes, the Blockchain is constructed to improve the security of data transmission from the source to the destination. The Blockchain uses the Kupyna Cryptography to generate the hash value for each data. Davis Mayer compression function is to improve the security of data delivery and to minimize packet loss. The comprehensive simulation is carried out to validate the performance of the proposed PRDMKCHB technique and existing Blockchain technology in terms of packet delivery ratio, packet loss rate, and execution time. Simulation results show the performance improvement of the proposed PRDMKCHB technique compared to the previous Blockchain technology in terms of higher packet delivery ratio, minimum packet loss, and execution time with respect to the number of data packets.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
On the Relative Impact of Optimizers on Convolutional Neural Networks with Varying Depth and Width for Image Classification

The continued increase in computing resources is one key factor that is allowing deep learning researchers to scale, design and train new and complex convolutional neural network (CNN) architectures in terms of varying width, depth, or both width and depth to improve performance for a variety of problems. The contributions of this study include an uncovering of how different optimization algorithms impact CNN architectural setups with variations in width, depth, and both width/depth. Specifically in this study, three different CNN architectural setups in combination with nine different optimization algorithms—namely SGD vanilla, with momentum, and with Nesterov momentum, RMSProp, ADAM, ADAGrad, ADADelta, ADAMax, and NADAM—are trained and evaluated using three publicly available benchmark image classification datasets. Through extensive experimentation, we analyze the output predictions of the different optimizers with the CNN architectures using accuracy, convergence speed, and loss function as performance metrics. Findings based on the overall results obtained across the three image classification datasets show that ADAM and NADAM achieved superior performances with wider and deeper/wider setups, respectively, while ADADelta was the worst performer, especially with the deeper CNN architectural setup.