Reviewerly/exHarmony huggingface dataset:
Ti:LiNbO<sub>3</sub> Based EO-MZI Count Optimized Design of Reversible Peres Gate

Logical reversibility stands for one-to-one mapping and hence one can trace the inputs by looking at the outputs. This is a significant paradigm these days since it not only helps to preserve data but also minimizes heat dissipation in quantum technology, quantum dot cellular automata, nanotechnology, etc. This paper explores the electro-optic Mach Zehnder Interferometer based implementation of Peres Gate (PG) with the minimum number of MZIs used in it. The design was explored in OptiBPM tool, whereas its mathematical power equation is verified in MATLAB. The system level validation is carried out in order to ensure the proper functioning of the PG as a whole.
Comparison of Hardware Implementations of Cryptographic Algorithms for IoT Applications

This paper compares various algorithms for application in the Internet of things (IoT). This technology contains a vast network of systems, sensors, and products, taking advantage of its low computing power and miniaturization of electronic networks and interconnected networks to provide extended capabilities that were not possible with the previouslyused technology. Many IoT devices per the customer’s use, such as Internet-enabled appliances, automation components, and management devices, are helping to lead the vision of a ‘‘smart life This paper aims to find out and implement cryptographic algorithms that can be used in various IoT applications. For the hardware implementation, a XILINX FPGA ZedBoard xc7z020c1g484-1 was used. It was found that Elliptical curve cryptography using the Diffie-Hellman algorithm (ECCDH) uses approximately 52% and 77% less power as compared with AES and ECC, respectively. Also, the operating frequency in the case of ECCDH is 355% and 9% more than the operating frequency of AES and ECC, respectively. Again, our design uses 43% less power and 374.4% more frequency than the existing ECCDH architecture. Therefore our proposed fast and resource efficient ECCDH algorithm is suitable for IoT applications.
Evaluating the effectiveness of Discriminator network in GAN architecture for phishing URL classification

Phishing attack by illegitimate URLs is of the most common security challenges for both individuals and companies in ensuring the security of their information resources. The user passwords, credit card information, or other sensitive information can be stolen by clicking on the malicious URL links. Recently, machine learning based approach is being popularly applied to detect phishing URLs. The classifiers, such as SVM, Random Forest, LSTM, etc., are built on the standard datasets to make a prediction about a URL sample is malign or benign one. Some recent researches focus on using GAN network for enrichment of malicious URL samples utilized in classifier training based on deep learning models. In this work, we explore the ability of training a standard GAN architecture which consists of two adversarial networks of Discriminator and Generator. The URL samples are generated by the Generator network will be refined and feed backed to the Generator by the Discriminator. This helps the Generator generate URL samples that are more and more similar to the real ones. Accordingly, the Discriminant network also learns the malicious and clean characteristics of the URL patterns. In order to evaluate the effectiveness of this learning, the experiments are conducted on completely new testing datasets beyond the training datasets. The experimental results are promising with the classification accuracy of both malign and benign URLs are about 97%.
Education experiences of primary school children with no or limited ICT during the COVID-19 pandemic in Trinidad and Tobago

The article investigates the education experiences of primary school students with no or limited information and communication technology during the pandemic in Trinidad and Tobago. Data were collected from sixteen students and a parent or guardian via face-to-face interviews and analysed using constant comparison analysis. To ensure equal and quality education in Trinidad and Tobago, revisions to the public assistance grant, a laptop distribution programme, a sustainable psychosocial outreach programme, and strategies to reduce variation in teaching quality among schools are recommended.
Enhancing cross-lingual learning: Optimal transfer language selection with linguistic similarity

The goal of our research is to develop an objective, scientifically backed method, for cross-lingual transfer language selection. We propose a method that relies on linguistic similarity metrics to evaluate the distance between languages and identify the optimal transfer language, rather than solely relying on intuition. Our findings indicate that linguistic similarity is a strong predictor of cross-lingual transfer performance across all Natural Language Processing tasks used, which include abusive language detection, sentiment analysis, named entity recognition, and dependency parsing. Moreover, we observe a statistically significant difference in performance when selecting a transfer source language other than English. This approach facilitates the selection of a more suitable transfer language, which aids in leveraging knowledge from high-resource languages and enhances the performance of low resource language applications. Our study utilized datasets from eight languages belonging to three language families.
Measure phrase modification in &lt;em&gt;name&lt;/em&gt; adjective constructions in Mandarin

Much research has shown that measure phrases elicit an asymmetry in acceptability between positive vs. negative members of gradable adjective antonym pairs (3m tall vs. *3m short) (Kennedy 1999; Schwarzschild 2005, and others). As in other languages, the measure phrase in Mandarin yi mi ‘1 meter’ is accepted as direct modifier of gao ‘tall’ and rejected by the negative antonym ai ‘short’. However, when the adjective combines with name ‘so’, this measure phrase is accepted by both positive and negative adjectives. In addition, name adjective expressions are ambiguous between two readings. Namely, the surprise reading and the equative reading. This paper accounts for the new data with two proposals: (1) name is a presupposition filter; (2) There is a silent morpheme Vec in name adjective expressions, which is in complementary distribution with overt measure phrases.
Omnidirectional 3D-DIC method for determining inner deformation in pipelines

An omnidirectional three-dimensional digital image correlation (DIC) system was developed for full 360-degree inner surface deformation measurements using a pyramid reflector to realize panoramic observation. After the coordinate transformation, the 3D displacement and strain distributions were reconstructed. The basic principles, optical arrangements, and experimental procedures were described in detail. Morphology and rigid-body displacement experiments were conducted to evaluate the efficiency and accuracy of the 3D shape and displacement determination. The experimental results agree well with the actual values and the data obtained by multiple measurements using the traditional 3D-DIC method. Moreover, the proposed method was successfully applied to evaluate the inner deformation distributions of pipes subjected to compression loading. The experimental results further validated the applicability of the proposed method.
Geo Fossils-I: A synthetic dataset of 2D fossil images for computer vision applications on geology

Geo Fossils-I is a synthetic image dataset used as a solution for resolving the limited availability of geological datasets intended for image classification and object detection on 2D images of geological outcrops. The Geo Fossils-I dataset was created to train a custom image classification model for geological fossil identification and inspire additional work in generating synthetic geological data with Stable Diffusion models. The Geo Fossils-I dataset was generated through a custom training process and the fine-tuning of a pre-trained Stable Diffusion model. Stable Diffusion is an advanced text-to-image model that can create highly realistic images based on textual input. An effective technique for instructing Stable Diffusion on novel concepts is the application of Dreambooth, a specialized form of fine-tuning. Dreambooth was used to generate new images of fossils or to modify existing ones per the provided textual description. The Geo Fossils-I dataset contains six different fossil types present in geological outcrops, each one being characteristic of a particular depositional environment. The dataset contains a total of 1200 fossil images equally spread among different fossil types such as ammonites, belemnites, corals, crinoids, leaf fossils, and trilobites. This dataset is the first set within a series to be compiled aiming to enrich the available resources with respect to 2D outcrop images allowing geoscientists to progress in the field of automated interpretation of depositional environments.
Incorporating inconsistent auxiliary images in haze removal of very high resolution images

Haze contamination is a common issue in very high resolution (VHR) remote sensing images, which inevitably hinders their application. The use of auxiliary images has been a key trend for enhancing haze removal. However, auxiliary VHR images are usually inconsistent with the target hazy VHR images, in terms of different imaging viewpoints, misalignment, or land cover changes, which greatly influence the performance of haze removal. To handle this problem, this paper develops a novel solution that incorporates auxiliary VHR images without any preprocessing or manual selection for haze removal. Accordingly, a gradient-based haze removal network (GHRN) is constructed. The gradient maps of both the target hazy images and corresponding auxiliary images are fed to the GHRN architecture, and hierarchical convolutions are applied to assist feature alignment. Seven state-of-the-art haze removal methods were employed as comparisons in ten simulated and six real scenarios. The experiment results demonstrate the reliability of GHRN. Moreover, GHRN is suitable for auxiliary images with different contaminations (e.g., haze or noise). To the best of our knowledge, this is the first study to consider inconsistent auxiliary images for enhancing haze removal.
Classification of unexposed potsherd cavities by using deep learning

The aim of the Deep Research for Akkon project is to use machine-learning techniques, specifically convolutional neural networks (CNNs), to analyze the large corpus of archaeological pottery fragments belonging to the Late Jomon period (ca. 4000 to 3200 aBP) in a non-invasive and non-destructive manner. In Japan, recent research conducted by using the ”impression method” revealed that ceramic vessels belonging to that era contain impressions of plant seeds. Studying these impressions allows archaeologists to better understand the cultural practices of the Jomon people and the path of rice diffusion in Japan. Most of the analysis was conducted by visually inspecting X-ray images. However, results based on using only X-rays are often inconclusive, leading researchers to use other invasive techniques that damage the potsherds. In this paper, we present a method that classifies X-ray images of potsherds by using deep learning. A dataset composed of 1036 images with seven classes was used to evaluate our approach. We generated different models using different CNN architectures and parameters and analyzed them separately. Then, we applied techniques such as ensemble and test-time augmentation to evaluate how such techniques impact our model. The best result was obtained by combining EfficientNetB7 trained with different parameters, achieving a classification rate of 90%.
A Predictive Model That Aligns Admission Offers with Student Enrollment Probability

This study develops a process that helps admission committees of higher education institutions select interested and qualified students. This enables institutions to maintain their financial viability by reaching the quota given by the Education Administration of Taiwan. We aimed to predict the decision-making behavior of students in terms of enrollment. A logistic regression analysis was conducted on publicly and inexpensively accessible data; the selection criteria of the model are based on metrics from a confusion matrix comprising predicted and observed data. The results indicate a matching rate of close to 80% between the training data of a target university from 2018 to 2020 and the testing data from 2021. This system outputs a probability that the student will enroll and thus helps admission committees more effectively select students.
Robust Intrusion Detection for Industrial Control Systems Using Improved Autoencoder and Bayesian Gaussian Mixture Model

Machine learning-based intrusion detection systems are an effective way to cope with the increasing security threats faced by industrial control systems. Considering that it is hard and expensive to obtain attack data, it is more reasonable to develop a model trained with only normal data. However, both high-dimensional data and the presence of outliers in the training set result in efficiency degradation. In this research, we present a hybrid intrusion detection method to overcome these two problems. First, we created an improved autoencoder that incorporates the deep support vector data description (Deep SVDD) loss into the training of the autoencoder. Under the combination of Deep SVDD loss and reconstruction loss, the novel autoencoder learns a more compact latent representation from high-dimensional data. The density-based spatial clustering of applications with noise algorithm is then used to remove potential outliers in the training data. Finally, a Bayesian Gaussian mixture model is used to identify anomalies. It learns the distribution of the filtered training data and uses the probabilities to classify normal and anomalous samples. We conducted a series of experiments on two intrusion detection datasets to assess performance. The proposed model performs better than other baseline methods when dealing with high-dimensional and contaminated data.
Cyber threat intelligence for critical infrastructure security

Summary Cyber‐attacks are considered the most significant threat to organizations from different sectors, including critical infrastructure. Access to critical assets, including industrial control systems, and control over their usage is one of the security approaches implemented to protect those systems from unauthorized access. However, existing implementations do not support the enforcement of fine‐grained authorization policies and do not provide continuous control over data access. Furthermore, existing implementations of the access control paradigm require policy‐makers to perform a manual update of policies that do not consider information about potential or ongoing cyber attacks. In this work, we propose a framework that enables continuous control on the execution of access rights in the industrial domain. Furthermore, the framework relies on cyber incident information shared by trusted entities. This information is used for updating security policies in order to prevent possible incidents within the smart factory infrastructure. We also provide experimental results that show the operability and the efficiency of the proposed framework.
FastAttacker: Semantic Perturbation Functions via Three Classifications

Deep neural networks (DNNs) have achieved great success in tasks such as image classification, speech recognition, and natural language processing.However, they are susceptible to false predictions caused by adversarial exemplars, which are normal inputs with imperceptible perturbations.Adversarial samples have been widely studied in image classification, but not as much in text classification.Current textual attack methods often rely on low-success-rate heuristic replacement strategies at the character or word level, which cannot search for the best solution while maintaining semantic consistency and linguistic fluency.Our framework, FastAttacker, generates natural adversarial text efficiently and effectively by constructing different semantic perturbation functions.It optimizes perturbations constrained in generic semantic spaces, such as the typo space, knowledge space, contextualized semantic space, or a combination.As a result, the generated adversarial texts are semantically close to the original inputs.Experiments show that FastAttacker generates adversarial texts from different levels of spatial constraints, making the problem of finding synonyms an optimal solution problem.Our approach is not only robust in terms of attack generation, but also in terms of adversarial defense.Experiments have shown that state-of-the-art language models and defense strategies are still vulnerable to FastAttack attacks.
Application of Deep Learning for Registration Between SAR and Optical Images

A new matching framework is put forward for registration among optical images along with synthetic aperture radar (SAR). Firstly, picture features are obtained adaptively through convolutional neural networks (CNN). Subsequently, original registration of SAR together with optical pictures is conducted on the grounds of Euclidean distance. In the end, the registration consequence is refined by means of random sample consistent algorithm. The experiment results have illustrated its superior matching execution pertaining to the progressive methods.
Reviving Standard-Dynamic-Range Videos for High-Dynamic-Range Devices: A Learning Paradigm With Hybrid Attention Mechanisms

With the prevalence of high-dynamic-range (HDR) display devices, the demand to convert existing standard-dynamic-range television (SDRTV) video content to its corresponding HDR television (HDRTV) counterpart is growing exponentially. Herein, we propose a two-stage learning paradigm with hybrid attention mechanisms to fully exploit spatial, channelwise, and regional correlations for faithfully driving such conversion. Specifically, in the first domain-mapping stage, the depthwise self-attention and global calibration layer are proposed, which adaptively leverage feature intrarelationships to construct better scene representation and achieve engaging SDRTV-to-HDRTV transformation. In the second highlight-generation stage, considering that the overexposed regions potentially lead to detail loss, which brings enormous challenges to the conversion, we propose a regional self-attention module to specifically restore missing highlights. Extensive experimental results on public databases show that our method outperforms state-of-the-art approaches in terms of different quality evaluation measures.
Gesture Recognition System Resilient to Interdevice Interference Based on Direct Sequence Spread Spectrum

Gesture interaction based on ultrasonic radar is an attractive solution for wearable devices. However, interdevice interference will be a realistic problem, when multiple adjacent devices that use this kind of technology are working simultaneously. In this letter, a direct sequence spread spectrum (DSSS)-based gesture recognition system that is resilient to interdevice interference is proposed. Based on the idea of code division multiplexing, the interference of other transmitters can be suppressed through correlation processing. The range-Doppler map (RDM) is generated by fast Fourier transform on the correlation function, and then features are extracted from the RDM sequence. A random forest classification is employed to complete the gesture classification. A complete prototype DSSS gesture recognition system has been built, which consists of 40-kHz ultrasonic frontends, an field-programmable gate array (FPGA) development board, and a personal computer. It is demonstrated that the system can achieve an average accuracy up to 92% for four defined gestures while the accuracy with an interference transmitter is lowered to 88%, which is 16% higher than that of the classic scheme that does not use the DSSS technique.
Adaptive Modulation for Wireless Federated Edge Learning

Wireless <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">federated edge learning</i> (FEEL) has been recently proposed to support the mobile <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">artificial intelligence</i> (AI) applications. Instead of transmitting local data to the edge server, the local learning updates or model parameters are uploaded over the wireless channels to protect the data privacy and security. However, the unreliable wireless channel will cause random packet error, which eventually has a significant impact on both the convergence rate and the learning latency. To cope with this problem, we propose a novel adaptive modulation scheme to achieve the balance between learning latency and convergence rate caused by stochastic channel error. Different from the conventional one, the modulation scheme in wireless FEEL system can be adjusted according to devices’ computing power, channel conditions, and training data importance. To further improve the FEEL performance, a joint optimization algorithm of spectrum allocation and modulation scheme selection is formulated to maximize the learning efficiency. Finally, we conduct extensive experiments based on different datasets. Test results show that the proposed algorithm can improve the convergence speed of the model training as compared with the conventional adaptive modulation schemes.
Adaptive Five-Axis Scanning of Deformed Surfaces Based on Real-Time Path Planning

Five-axis sweep scanning is an emerging surface inspection technology that has shown great potential to boost inspection efficiency for complex surfaces. The existing five-axis inspection methods rely on computer aided design (CAD) models to plan the entire sweep scanning path prior to measurement. Thus, they cannot be applied to measure deformed surfaces for repair or remanufacturing, since deformed surfaces lack valid CAD models. To solve this problem, an adaptive five-axis inspection approach is proposed for inspecting deformed surfaces based on real-time path planning, with no use of any CAD model. The proposed approach uses an iterative strategy that implements through three steps: surface prediction, sweep scanning path planning on the predicted surface, and conducting the inspection. The surface prediction method is accurate enough and computationally fast, so it is suitable for online applications. The path planning algorithm can improve inspection efficiency by avoiding empty strokes of measurement. Experiments conducted on three complex free-form surfaces verify the feasibility, accuracy, and efficiency of the proposed approach.
Root Cause Location Based on Prophet and Kernel Density Estimation

When an online service, such as an online Web service, encounters an abnormality, operators need to analyse many abnormal monitoring indicators on the affected machine and rapidly determine the root cause indicators, which can quickly locate specific problems. Then, the abnormality is isolated until the cascading effects caused by the anomalies are eliminated. This paper proposes a root cause indicator location algorithm named ProphetKdeRCL. First, the improved Prophet algorithm detects many abnormal time-series indicators. Then, the abnormal deviation degree algorithm based on kernel density estimation is used to measure the fluctuation of each abnormal indicator for sorting. The analyses of the delay causal factor dependence are combined with the time window. Finally, a root cause location list is generated to assist operators in quick troubleshooting operations. This paper uses public datasets to evaluate the overall effectiveness of the algorithm. The results show that compared to other algorithms, the ProphetKdeRCL algorithm has higher accuracy on evaluation indicator AC@1 and superior accuracy on the other commonly used indicators, namely, AC@2 and AC@3.