Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis on Twitter Data Using LSTM with Emoji Embedding

Data analysis in today&#x2019;s data-driven world has proved to be the most essential thing. The majority of the data generated online is unstructured and needs to be processed. Social media plays a vital role in sharing data on the internet in large amounts, this data needs to be handled properly and information from this unstructured data must be extracted. By using sentiment analysis, the intentions of textual data can be figured out and can classify properly on the internet. In the proposed approach, the implementation of sentiment analysis is done using the combination of various artificial intelligence technologies and LSTM including some key symbols like emojis with special characters to increase the accuracy of pre-built models. That can make them more practical in today&#x2019;s world.
Designing future scenarios in e-tourism

The rapid appropriation of digital platforms has been transforming the experience of tourism practice in a broad and profound way, its temporality and materiality. When it comes to the experience of interacting in the place you visit, there are numerous kinds of platforms. During software development it is necessary to develop solutions that meet the needs of users.This paper reports the initial design phase of an e-tourism platform to support the relationships between different tourists while in the territory. We adopted an Ethnographic Design method to engage with users in the emerging touristic experiences. We engage distinct techniques personas, scenarios and prototyping to promote a profound understanding about the experience. The joint use of the techniques leaded the formulations of a set of requirements identified throughout the execution of each step of the method. These requirements were prototyped and will serve for the application developed to meet the needs of the identified target audience.
Self-Supervised multi-image Super-Resolution Network for Space Image Restoration

Abstract Space-based space surveillance (SBSS) has the advantage of being flexible and highly interpretable, allowing it to play a strategic role in monitoring space targets. During actual missions, however, bursts of push-frame imaging methods, dynamic noise during observation, large motions and other factors can result in visible degradation, and it isn’t easy to have the corresponding high-resolution (HR) images for the low-resolution (LR) images acquired at the high-speed intersection, which seriously limits the existing multi-image super-resolution (MISR) methods for space-based applications. To overcome these problems, this paper proposes a self-supervised video super-resolution deep learning method that can be trained end-to-end in space-based observations without LR/HR image pairs. We generate extra training pairs using different sizes of sampling factors for LR videos and train the video hyper-resolution network from coarse to fine using a pyramid format. Extensive experiments on the public satellite dataset BUAA-SID-share1.0 show that our approach outperforms traditional video hyper-resolution methods.
Pseudo Wigner-Ville distribution for 3D white light scanning interferometric measurement

White light scanning interferometry is a commonly used optical measurement method for three-dimensional (3D) surface profiles. In the case of large phase errors, accurate height values can generally be obtained indirectly from the interferometric signal envelope information derived using various envelope extraction methods. However, the current envelope extraction algorithms have the disadvantages of low robustness, increasing the half-width of the envelope information, and requiring correct parameter settings in advance. In this study, the pseudo Wigner-Ville distribution is modified and applied to white light scanning interferometric 3D measurement to avoid the above-mentioned drawbacks. Simulations and experiments are performed in a single-frequency mode (only the approximate central wave-number is used to guide both the proposed and wavelet transform methods). The simulation results prove that the proposed method has a 31.7% higher reconstruction accuracy than the wavelet transform method under a 25 dB signal to noise ratio condition. Concurrently, the proposed method is insensitive to the change in the central wavelength with a constant central wave-number parameter and has a good extraction effect for a long coherent length. The experiments measure standard step objects (VLSI standard, 1.761 ± 0.01 µm), and the reconstruction height error of the proposed method is 0.0035 µm. Simulations and experiments show that the proposed method can adaptively provide accurate envelope information after half-width reduction under the condition that only the approximate central wave-number a priori knowledge is used. Simultaneously, the proposed method is shown to be robust and effective.
Modular Federated Learning

Federated learning is an approach to train machine learning models on the edge of the networks, as close as possible where the data is produced, motivated by the emerging problem of the inability to stream and centrally store the large amount of data produced by edge devices as well as by data privacy concerns. This learning paradigm is in need of robust algorithms to device heterogeneity and data heterogeneity. This paper proposes ModFL as a federated learning framework that splits the models into a configuration module and an operation module enabling federated learning of the individual modules. This modular approach makes it possible to extract knowlege from a group of heterogeneous devices as well as from non-IID data produced from its users. This approach can be viewed as an extension of the federated learning with personalisation layers FedPer framework that addresses data heterogeneity. We show that ModFL outperforms FedPer for non-IID data partitions of CIFAR-10 and STL-10 using CNNs. Our results on time-series data with HAPT, RWHAR, and WISDM datasets using RNNs remain inconclusive, we argue that the chosen datasets do not highlight the advantages of ModFL, but in the worst case scenario it performs as well as FedPer.
Natural construction method of ordinal sum implication and its distributivity

Recently, De Lima et al. introduced the left ordinal sum of fuzzy implications so that its natural negation has an ordinal sum representation, which enriches the natural negations of fuzzy implications. In this paper, we propose a natural method to construct the ordinal sum of fuzzy implications without changing its natural negation, and explore the relationship between this class of ordinal sum of fuzzy implications and some classes of ordinal sums of fuzzy implications in the literature. Moreover, we characterize the distributivity equations of such ordinal sum of fuzzy implications over t-norms and t-conorms under some conditions.
A solution technique to cascading link failure prediction

The study of complex network s is a new powerful tool that can provide a profitable skeleton to better elucidate technology-related phenomena and interactions between components of real-world networks However, it is not easy to predict the communal behavior of such systems from their elements and on the other hand, the failure of one or few elements can trigger the failure of other elements throughout the network, resulting in network breakdown and catastrophic events at large scales. Therefore, developing predictive mathematical techniques to examine complex networks is one of the biggest challenges of the present time. Knowing that link failure prediction is less studied in the OR literature, the present study articulates a method to predict link failures in complex networks, which is primarily based on Bayesian Belief Networks (BBN) and TOPSIS . The method aims to predict failures based on the affective factors of failures in networks. To this end, effective factors of failures are first detected, and then the graph of the relationship of factors along with their weight is determined. After all, the method provides the prediction for future damaged components. The functionality of the method is validated by an extensive computational analysis employing simulation in scale-free, random, and actual international aviation networks and its performance is compared with other benchmark algorithms. The results and sensitivity analysis experiments arrive at prominent managerial insights and efficacious implications and show that our method can generate high-quality solutions in many networks.
Unsupervised Hebbian Learning on Point Sets in StarCraft II

Learning the evolution of real-time strategy (RTS) game is a challenging problem in artificial intelligent (AI) system. In this paper, we present a novel Hebbian learning method to extract the global feature of point sets in StarCraft II game units, and its application to predict the movement of the points. Our model includes encoder, LSTM, and decoder, and we train the encoder with the unsupervised learning method. We introduce the concept of neuron activity aware learning combined with k-Winner-Takes-All. The optimal value of neuron activity is mathematically derived, and experiments support the effectiveness of the concept over the downstream task. Our Hebbian learning rule benefits the prediction with lower loss compared to self-supervised learning. Also, our model significantly saves the computational cost such as activations and FLOPs compared to a frame-based approach.
Risk Management Plan For the Hospital Environment

A risk management plan is placed in ISO 14971:2019 standard for mitigating different kinds of risk related to the use of medical electrical equipment including the electromagnetic interference (EMI) risk in the hospital environment.However, EMI accidents in the hospital are still happening indicating that further improvement in the risk management plan is required.Currently, the risk management plan in the standard does not factor in the hospital environment realistically, leading to incomplete risk analysis, evaluation, estimation, and control methods.Due to the dynamic environment of hospitals, the rule-based EMC approach is insufficient and the risk-based EMC approach should be utilized in improving risk management plans.In this paper, we utilized several risk-based EMC approach techniques and measurements such as the foot-printing technique, for properly examining the hospital environment, source-victim matrix tool, to categorize the severity of EMI issue, statistical tools like probability density function, cumulative density function, etc., to calculate probability and predict any future EMI risk.These techniques assist in the zoning of the hospital environment into low-risk, medium-risk, and high-risk for which risk control methods can be established.Overall, we hinted toward improving the risk management plan, in terms of flexibility, accuracy, and reliability, using risk-based EMC approach techniques
FPGA Switching Current Modeling Based on Register Transfer Level Logic Simulation for Power Side-channel Attack Prediction

We investigated the prediction of side-channel leak-age on the power delivery network (PDN) outside a cryptographic IC. To predict the leakage, it is necessary to estimate the switching current caused by the switching activity. So far, high temporal resolution estimation based on gate-level logic simulation has been proposed, but due to the decoupling property of the PDN, the switching current leaking out of the IC chip attenuates signif-icantly. Therefore, this paper calculates the mean of the switching currents over the clock period based on register transfer level (RTL) logic simulation, and then approximates it into a triangle waveform. This approach enables us to simulate the switching current faster than with gate-level simulation. In addition, the simulation time is further shortened by calculating the switching current only to the targeted round of a cryptographic algorithm (AES). Switching currents of the previous rounds were given as statistical models with the same sample mean and variance as the targeted round. As a result of simulating the switching current of an FPGA with the proposed method, we were able to obtain the low-order harmonic components of the current in almost the same magnitude as the gate-level simulation. We also simulated correlation power analysis (CPA) results for the printed circuit board on which the FPGA with the AES circuit was mounted, and found that the proposed method could predict the dynamic power supply voltage observed on the board and CPA results with sufficient accuracy.
Intelligent Evaluation Algorithm of English Writing Based on Semantic Analysis

In order to solve the intelligent evaluation of English writing, this paper proposes a method based on the English semantic neural network algorithm. This paper first briefly analyzes the research background of the English semantic analysis system, then expounds on the relevant technologies of the English distance similarity algorithm, semantic analysis intelligent algorithm structure, word analysis algorithm, sentence part of speech analysis algorithm, sentence semantic analysis algorithm, and neural network algorithm, and finally expounds the database and method implementation of the English semantic analysis system, so as to provide guarantee for the design of the English semantic analysis system. The experimental results show that the recognition accuracy of the BRF network for English characters can reach 96.35%, which is 7.79% higher than that of the BP network; the AUC of the BRF network reaches 0.89, which is closer to 1 compared with 0.72 of the BP network. The test results are in good agreement with the antinoise curve test results of the figure. It is proved that the English semantic neural network algorithm can effectively improve the accuracy of English translation and further improve the efficiency of the system.
Design and Application of Electromechanical Control System Based on Computer Fault Tolerance Technology

In order to improve the stability and control effect of the electromechanical control system, this paper designs the electromechanical control system combined with the computer fault-tolerant technology and proposes an electromechanical control signal processing algorithm. In order to improve the accuracy of the path loss calculation, according to Fresnel’s law, this paper deduces the expression of the reflection coefficient of electromechanical control signal with the distance between the transmitting antenna and the receiving antenna as the main variable and then compares the path loss with the ground and the ceiling as the reflecting surface. In addition, in order to study the path loss, this paper selects different reflective materials to study their path loss under horizontal polarization and vertical polarization. The results show that under the vertical polarization, the path loss fluctuation is not large, and the agreement is higher than that of the horizontal polarization. The research results show that the electromechanical control system based on computer fault-tolerance technology proposed in this paper has stable performance and can play an important role in the control of electromechanical systems.
Speech Attribute Detection to Recognize Arabic Broadcast Speech in Industrial Networks

In this work, we present an alternative model to Arabic speech recognitions to boost classification accuracy of basic speech units in Industrial Internet of Things. This approach integrates both top-down and bottom-up knowledge into the automatic speech attribute transcription (ASAT) framework. ASAT is a new family of lattice-based speech recognition systems grounded on accurate detection of speech attributes. Two state-of-the-art deep neural networks were used for attribute speech feature detection, convolutional neural networks (CNNs) and feed-forward neural network, with pretraining using a stack of restricted Boltzmann machines (DBN-DNN). These attribute detectors are trained using a data-driven approach for Arabic speech data using the Buckwalter code for Arabic transcription. The detectors work by detecting phonologically distinctive articulatory features of Arabic based on manner and place of articulation, which extract characteristics of the human speech production process from the speech dataset. The results of the work are shown in terms of average equal error rate inferring that the attribute model based on the CNN consistently outperforms the DBN-DNN model. Attribute detectors with CNN, versus DBN-DNN model, reduce the AEER producing 10.42% relative improvements for manner of articulation and producing 13.11% relative improvements for place of articulation.
Automatic Question Generation for Hindi and Marathi

Artificial Intelligence has contributed remarkably towards educational purposes in the last few decades. With the rise of online learning, most of the tedious tasks have been transferred from the human hands to machines and programs. Question generation from educational paragraphs has been a challenging activity for educators as it requires significant resources. Although there have been major breakthroughs in the English language towards this goal, there is a need for technology-enabled educational services in Indian regional languages. In this paper, we present automatic question generation for two Indian languages - Hindi and Marathi utilizing Sentence Constituency Parsing with openAI Generative Pre-Trained(GPT) and Text-to-Text Transfer Transformers(T5) models, evaluated by content experts of corresponding languages, generating optimum questions after removing repetitions and inaccuracies in structural constraints on composition.
An Agile Framework for Security Requirements: A Preliminary Investigation

Requirements engineering (RE) is a crucial component in successful software development process. The idea of embedding the non-functional requirements (NFRs) such as performance, maintainability, modifiability, and others into a new software system is often implemented by software engineers. However, security as a crucial NFR is often ignored in the software development process. In this paper we address the importance of security as a NFR in the software development process. To that end, we propose a lightweight novel agile framework to analyze security requirements. We evaluate the proposed framework with a qualitative analysis and determine how it is useful to requirement analysts.
CTSRMV: A Chinese Text Image Super-Resolution Method Based on Mobile ViT

A large number of text images come from natural scenes. However, most of them are taken by non professional cameras such as mobile phones, and their resolution is low, which greatly reduces the readability of the text in the images. The text image super-resolution method aims to enhance the low resolution image to obtain a clearer text image. At present, with the appearance of convolutional neural network, image super-resolution technology has developed rapidly. The traditional super-resolution method is mainly used for natural scene images, and is usually not suitable for text. Many scholars have proposed super-resolution network methods for text images, but most of them are based on English text. Due to the complexity and diversity of Chinese texts, these networks are not suitable for Chinese texts. In order to improve the shortcomings of the above methods, a Chinese text dataset is generated, and a text-image super-resolution lightweight network based on Mobile ViT is also proposed. Experiments show that our proposed dataset CTW is meaningful for the study of Chinese text image super-resolution. And the new method has better performance in PSNR/SSIM metrics and can effectively restore blurred low-resolution images into clear high-resolution images.
Research on Recommendation of College Mental Health Teaching Materials Based on Improved Deep Learning Algorithm

In order to meet the differentiated needs of students and improve the satisfaction of college mental health textbook recommendation, a college mental health textbook recommendation scheme based on improved deep learning algorithm is proposed. Based on the analysis of the principle of deep learning data recommendation system, the data interest is calculated according to the browsing records of students on college mental health textbooks. Combine the deep learning algorithm and collaborative filtering algorithm to collect the demand data of college mental health textbooks, then use the Naive Bayesian classification method to divide the college mental health textbooks into interested and uninterested parts, and recommend the interested college mental health textbooks to the students in need. Experiments show that the longest recommendation time of the college mental health textbook recommendation scheme based on the improved deep learning algorithm proposed in this paper is 4.5 min, the highest recommended recall rate is 95.18%, the average accuracy is 97.2%, the highest content richness is 0.8, the system stability coefficient is 1.06, and the overall average praise rate is 97.79%. It has a good recommendation effect.
SENTIMENT ANALYSIS OF PEOPLE’S ACCEPTANCE TOWARDS THE NEW MALAYSIAN GOVERNMENT USING NAÏVE BAYES METHOD

Sentiment analysis is a field of research that has a significant impact on today’s nations, politics and businesses. It is an algorithmic process to comprehend the opinions of a given subject based on the Natural Language Processing (NLP) methodologies. It has received much attention in recent years and is proven vital in various fields, e.g., online product reviews and social media analysis (Twitter, Facebook, etc.). This paper reports the outcome of sentiment analysis to investigate people’s acceptance of Pakatan Harapan, as the new Malaysian government, spearheaded by Tun Dr. Mahathir Mohamad and Dr. Wan Azizah, with an influence of Dato Seri Anwar Ibrahim. The objective is to classify tweets into three types of sentiments; positive, neutral and negative using Naïve Bayes method which is readily available in Python. The first step is tweets extraction for a month (March to April 2019) using search queries: {Pakatan Harapan, Mahathir, Anwar Ibrahim, Wan Azizah}. It is followed by tweets wrangling using NLP library and lastly output visualization in the form of a word cloud. A word cloud is a visual representation of text data with various font sizes depending on its probabilities. Final results showed that the tweets related to new government consist of neutral sentiment (41%) followed by positive sentiment (30%) and negative sentiment (29%). Malaysians do prefer the new government. However careful mitigation steps must be crafted to overcome controversial issues such as the ‘Rome Statute’ to avoid negative digital footprint, hence winning the Malaysians’ heart.
Optical flow-based angular rate sensor fault detection on UAVs*

The paper first extends the previous work of the authors dealing with optical flow-based angular rate estimation. Extension means consideration of camera position and orientation relative to the UAV body system (non-aligned camera). After the extension the proposed method is evaluated with virtual reality (Unreal-Carla) image sequences considering simulated aircraft flight trajectories and different frames per second (fps) rates. Based-on the results showing high fps requirements for agile flight scenarios a reversed method is proposed for angular rate sensor fault detection considering the integration of system dynamics based-on angular rate and velocity measurements and comparing the predicted image feature positions with the measured ones. The feature position differences are the error measures completed with up-down counters. The results are promising however, only basic evaluation of the proposed methods is done paving the way for detailed evaluation and development outlined in the conclusion.