Reviewerly/exHarmony huggingface dataset:
Analyzing and Predicting Learner Sentiment Toward Specialty Schools Using Machine Learning Techniques

The objective of the research is to use machine learning techniques to evaluate and predict learners' sentiment toward specialty school. The current study used the Yelp website's reviews to obtain data on specialty schools after filtering. Following cleaning, the filtered summary sentences were rated as positive, neutral, or negative sentiments using the AFINN and VADER sentiment algorithms. In addition, to split learner ratings of specialty schools into three sentiment categories, the current study also used four supervised machine learning techniques. The majority of ratings for specialty schools were favorable, according to the findings of the present study. Furthermore, while all of the techniques (decision tree, K-neighbors classifier, logistic regression, and SVM) can accurately classify review text into sentiment class, and SVM outperforms in terms of high accuracy. Specialty educational institutes will be able to better understand learners' psychological sentiments based on the findings of the study, allowing them to improve and adjust their services.
Generation of nonclassical states of light via truncation of mixed states

A possible way of generating nonclassical states of light is via the truncation of a given state in the Fock basis. In recent work, we presented an alternative scheme for such quantum scissors [ Phys. Rev. A 104 , 033715 ( 2021 ) PLRAAN 1050-2947 10.1103/PhysRevA.104.033715 ], employing a nondegenerate parametric amplifier, a beam splitter, and photodetectors. An advantage of this setup is that it does not require the generation of Fock states beforehand, as in previous proposals. Here we extend this treatment to mixed input states. We show the possibilities of generating truncated states with either a maximum Fock number <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>N</mml:mi> </mml:math> or states having a minimum Fock number <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>N</mml:mi> </mml:math> . We discuss two specific examples of states to be truncated: (i) the thermal state and (ii) the phase-diffused coherent state. In either case, the generated field states can present sub-Poissonian statistics as well as non-Gaussian character. These nonclassical properties can be adjusted by changing the parameters of the scissors, e.g., the amplifier strength and the beam-splitter transmittance.
Convolutional neural networks method for analysis of e-commerce customer reviews

Sentiment Analysis (SA) is the most common text classification tool that analyses an incoming message and tells whether the underlying sentiment is positive, negative, or neutral. E-Commerce portals are generating a lot of data every day in the form of customer reviews. It extracts subjective information in source material and helping a business to understand the social sentiment of their brand, product, or service while monitoring online conversations. Many sentiment analysis techniques are available and most of them are done using a single machine learning algorithm. This paper proposes a method of extracting text features using a Convolutional neural network and uses logistic regression to analyze the sentiment polarity of the customer reviews. The result of the experimental result proves that this model effectively improves the performance of the sentimental classification.
Learning Disentangled Representation for Multimodal Cross-Domain Sentiment Analysis

Multimodal cross-domain sentiment analysis aims at transferring domain-invariant sentiment information across datasets to address the insufficiency of labeled data. Existing adaptation methods achieve well performance by remitting the discrepancies in characteristics of multiple modalities. However, the expressive styles of different datasets also contain domain-specific information, which hinders the adaptation performance. In this article, we propose a disentangled sentiment representation adversarial network (DiSRAN) to reduce the domain shift of expressive styles for multimodal cross-domain sentiment analysis. Specifically, we first align the multiple modalities and obtain the joint representation through a cross-modality attention layer. Then, we disentangle sentiment information from the multimodal joint representation that contains domain-specific expressive style by adversarial training. The obtained sentiment representation is domain-invariant, which can better facilitate the sentiment information transfer between different domains. Experimental results on two multimodal cross-domain sentiment analysis tasks demonstrate that the proposed method performs favorably against state-of-the-art approaches.
Modulation Classification of Active Attacks in Internet of Things: Lightweight MCBLDN With Spatial Transformer Network

The Internet of Things (IoT) permeates every aspect of our daily lives as billions of interconnected devices are deployed in the physical world. However, IoT networks operate in an untrusted environment and often suffer from many malicious active attacks. Automatic modulation classification (AMC), which can identify the modulation format of intercepted signals without prior knowledge, is a vital technology in countering physical-layer threats of IoT. However, most of the existing algorithms assume the channel is time invariant, and the AMC in time-varying channels is not been well studied. To deal with this dilemma, a novel AMC algorithm MCBLDN consisting of multiple convolutional neural networks (CNNs), a bidirectional long short-term memory network (BLSTM), and a deep neural network (DNN) is proposed. In MCBLDN, a multislot constellation diagram (CD) method is proposed to extract time-evolution characteristics for generating more discriminative features. Specifically, different grayscale subimages generated by slotted CDs are processed serially by their respective CNNs. Therefore, MCBLDN is overparameterized and time consuming. In addition, the frequency offset and phase offset caused by time-varying channels are neglected in MCBLDN, which is detrimental to the performance of AMC. To address the mentioned disadvantages, a lightweight MCBLDN with a spatial transformer network (SLCBDN) is proposed. First, the multiple CNNs in MCBLDN are pruned into a lightweight classification model, and the input data are rearranged to facilitate parallel processing by the lightweight CNN. Additionally, the spatial transformer network (STN) is utilized to reduce the influence of frequency offset and phase offset. Numerical results verify that the proposed method achieves superior performance and higher speed compared to the baseline algorithm MCBLDN.
Hyper-parameter Tuning of Federated Learning Based on Particle Swarm Optimization

The learning task of federated learning (FL) is solved by a federation of a center server and individual clients. Contrary to traditional deep learning models, federated learning consists of two parts, the global model and individual models. However, since the process is carried out by two parts of equal importance, it is ideal to deal with both parts simultaneously. To achieve superior performance, federated learning requires carefully selected hyper-parameters, which has more hyper-parameters than those of traditional deep learning models. To solve this tuning problem, we propose a method using particle swarm optimization (PSO) algorithm to tune the hyperparameters of federated learning. PSO algorithm is a gradient-free, stochastic optimization method which is better than grid search method when it comes to large search space. It helps locate the optimal combination of multiple parameters. In this article, we focus on applying PSO method on tuning the hyper-parameters of FL models, and prove that it is an efficient way to acquire satisfactory results. Experiments on MNIST dataset with convolution neural networks have proved the superiority of the proposed method.
Safety Helmet Wearing Detection in Aerial Images Using Improved YOLOv4

In construction, it is important to check whether workers wear safety helmets in real time.We proposed using an unmanned aerial vehicle (UAV) to monitor construction workers in real time.As the small target of aerial photography poses challenges to safety-helmet-wearing detection, we proposed an improved YOLOv4 model to detect the helmet-wearing condition in aerial photography: (1) By increasing the dimension of the effective feature layer of the backbone network, the model's receptive field is reduced, and the utilization rate of fine-grained features is improved.(2) By introducing the cross stage partial (CSP) structure into path aggregation network (PANet), the calculation amount of the model is reduced, and the aggregation efficiency of effective features at different scales is improved.(3) The complexity of the YOLOv4 model is reduced by introducing group convolution and the pruning PANet multi-scale detection mode for de-redundancy.Experimental results show that the improved YOLOv4 model achieved the highest performance in the UAV helmet detection task, that the mean average precision (mAP) increased from 83.67% of the original YOLOv4 model to 91.03%, and that the parameter amount of the model is reduced by 24.7%.The results prove that the improved YOLOv4 model can effectively respond to the requirements of real-time detection of helmet wearing by UAV aerial photography.
Improved Prioritization of Software Development Demands in Turkish With Deep Learning-Based NLP

Management of software development demands including bug or defect fixes and new feature or change requests is a crucial part of software maintenance.Failure to prioritize demands correctly might result in inefficient planning and use of resources as well as user or customer dissatisfaction.In order to overcome the difficulty and inefficiency of manual processing, many automated prioritization approaches were proposed in the literature.However, existing body of research generally focused on bug report repositories of open-source software, where textual bug descriptions are in English.Additionally, they proposed solutions to the problem using mostly classical text mining methods and machine learning (ML) algorithms.In this study, we first introduce a demand prioritization dataset in Turkish, which is composed of manually labeled demand records taken from the demand management system of a private insurance company in Turkey.Second, we propose several deep learning (DL) architectures to improve software development demand prioritization.Through an extensive experimentation, we compared the effectiveness of our DL architectures trained with several combinations of different optimizers and activation functions in order to reveal the best combination for demand prioritization in Turkish.We empirically show that DL models can achieve much higher accuracy than classical ML models even with a small amount of training data.
UNIREX: A Unified Learning Framework for Language Model Rationale Extraction

An extractive rationale explains a language model's (LM's) prediction on a given task instance by highlighting the text inputs that most influenced the prediction.Ideally, rationale extraction should be faithful (reflective of LM's actual behavior) and plausible (convincing to humans), without compromising the LM's (i.e., task model's) task performance.Although attribution algorithms and select-predict pipelines are commonly used in rationale extraction, they both rely on certain heuristics that hinder them from satisfying all three desiderata.In light of this, we propose UNIREX, a flexible learning framework which generalizes rationale extractor optimization as follows: (1) specify architecture for a learned rationale extractor; (2) select explainability objectives (i.e., faithfulness and plausibility criteria); and (3) jointly train the task model and rationale extractor on the task using selected objectives.UNIREX enables replacing prior works' heuristic design choices with a generic learned rationale extractor in (1) and optimizing it for all three desiderata in (2)-(3).To facilitate comparison between methods w.r.t.multiple desiderata, we introduce the Normalized Relative Gain (NRG) metric.Across five English text classification datasets, our best UNIREX configuration outperforms the strongest baselines by an average of 32.9% NRG.Plus, we find that UNIREXtrained rationale extractors' faithfulness can even generalize to unseen datasets and tasks.
ASR-Aware End-to-End Neural Diarization

We present a Conformer-based end-to-end neural diarization (EEND) model that uses both acoustic input and features derived from an automatic speech recognition (ASR) model. Two categories of features are explored: features derived directly from ASR output (phones, position-in-word and word boundaries) and features derived from a lexical speaker change detection model, trained by fine-tuning a pretrained BERT model on the ASR output. Three modifications to the Conformer-based EEND architecture are proposed to incorporate the features. First, ASR features are concatenated with acoustic features. Second, we propose a new attention mechanism called contextualized self-attention that utilizes ASR features to build robust speaker representations. Finally, multi-task learning is used to train the model to minimize classification loss for the ASR features along with diarization loss. Experiments on the two-speaker English conversations of Switchboard+SRE data sets show that multi-task learning with position-in-word information is the most effective way of utilizing ASR features, reducing the diarization error rate (DER) by 20% relative to the baseline.
Robust Learning of Recurrent Neural Networks in Presence of Exogenous Noise

Recurrent Neural networks (RNN) have shown promising potential for learning dynamic features of sequential data. However, artificial neural networks are known to exhibit poor robustness in presence of noisy input, where the sequential architecture of RNNs exacerbates the problem. In this paper, we will use ideas from control and estimation theories to propose a tractable robustness analysis for RNN models that are subject to noisy inputs. The variance of the output of the noisy system is adopted as a robustness measure to quantify the impact of noise on learning. It is shown that the robustness measure can be estimated efficiently using linearization techniques. Using these results, we proposed a learning method to enhance robustness of a RNN with respect to exogenous Gaussian noise with known statistics. Several theoretical upper bounds are also presented to show how the robustness measure depends on the trainable parameters. Our extensive simulations on benchmark problems reveal that our proposed methodology significantly improves robustness of recurrent neural networks in a systematic manner.
Flow Transformer: A Novel Anonymity Network Traffic Classifier with Attention Mechanism

Supervising anonymity network is a critical issue in the field of network security, and traditional traffic analysis methods cannot cope with complex anonymity traffic. In recent years, the traffic analysis method based on deep learning has achieved good performance. However, most of the existing studies do not consider the temporal-spatial correlation of the traffic, and only use a single flow for classification. A few works take continuous flows as flow sequence for traffic classification, but they do not distinguish the different importance of each flow. To tackle this issue, we propose a novel flow-based traffic classifier called FLOW TRANSFORMER to classify anonymity network traffic. FLOW TRANSFORMER uses multi-head attention mechanism to set higher weights for important flows, and extracts flow sequence features according to the importance weights. Besides, the RF-based feature selection method is designed to select the optimal feature combination, which can effectively avoid the insignificant features from reducing the performance and efficiency of the classifier. Experimental results on two real-world traffic datasets demonstrate that the proposed method outperforms state-of-the-art methods with a large margin.
Image Classification Based on Sparse Representation in the Quaternion Wavelet Domain

In this study, we propose a novel sparse representation learning method in the Quaternion Wavelet (QW) domain for multi-class image classification. The proposed method takes advantages from: i) the QW decomposition, which promotes sparsity and provides structural information about the image data while allowing approximate shift-invariance, to extract meaningful features from low-frequency QW sub-bands, ii) the dimensionality reduction method using Principal Component Analysis (PCA) for reducing the complexity of the problem, and iii) the sparse representation of the generated QW features to efficiently learn and capture the meaningful and compact information of this data. After the QW decomposition, the features extracted from low-frequency image sub-bands information are projected, by the PCA, into a new feature space with lower dimensionality. The features extracted from the training samples are used to construct a dictionary, while the features of the test samples are sparsely coded for the classification step. The sparse coding problem is formulated in a QW Least Absolute Shrinkage and Selection Operator (QWLasso) model applying quaternion <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula> minimization. A novel Quaternion Fast Iterative Shrinkage-Thresholding Algorithm (QFISTA) is developed to solve the QWLasso model. The experiments conducted on various public image datasets validated that the proposed method possesses higher accuracy, sparsity, and robustness in comparison with several contemporary methods in the field including Neural Networks.
An optimal coverage path plan for an autonomous vehicle based on polygon decomposition and ant colony optimisation

This paper proposes a coverage path planner for the autonomous vehicle used in inspection operation. The proposed coverage path planner is developed based on a back and forth pattern (equivalent to boustrophedon pattern), producing the output path for the inspection operation with the least trajectory length and number of turns covering the entire workspace. Implementing a back and forth pattern on an arbitrary polygon (extracted from workspace boundaries) is not always viable due to the existence of concave vertices in the polygon. So, this work proposes the tree search model based on the polygon width and back and forth pattern’s sweep line, which decomposes the input polygon in numerous ways. The Dijkstra algorithm solves this tree model to obtain back and forth pattern viable polygons with the least number of turns. Later, the vehicle visits these polygons one by one during the inspection operation. The order in which polygon visits are made by the vehicle affects the overall trajectory length. So, this work proposes the ant colony optimisation based trajectory length optimiser, which finds the polygon visiting order that yields minimum trajectory length. The developed coverage path planner is versatile, and it applies to any autonomous vehicle that operates in the planar region. • Proposes tree search model for polygon decomposition, which yields least turns. • Develops ant colony optimisation based trajectory length optimiser. • Trajectory length optimiser solves the model equivalent to the travelling salesman problem. • Applies the proposed coverage path planner on Chennai’s Kasimedu fishing harbour.
Grayscale-Invariant Reversible Data Hiding Based on Multiple Histograms Modification

Grayscale-invariant reversible data hiding (GI-RDH) in color images is a data embedding framework in which the grayscales of a marked color image must be identical to those of the host color image. Recently, some state-of-the-art GI-RDH schemes were proposed. However, their performance in embedding distortion is unsatisfactory. In order to obtain better image quality, a well-known histogram-shifting-based RDH method called multiple histograms modification (MHM) is considered. In this paper, we propose an MHM-based GI-RDH scheme. First, we modified our previous GI-RDH scheme using a multiple-histogram-shifting approach instead of a difference expansion approach. Next, we designed a procedure to select expansion–bin pairs for generated histograms to achieve low embedding distortion through further data embedding. Specifically, we analyzed the expected embedding distortion of our MHM-based GI-RDH scheme given any set of expansion–bin pairs. We then formulated an optimization problem called the GI-MHM minimization problem to identify the optimal expansion–bin pairs for further embedding tasks. Finally, we generated an approximated solution for the GI-MHM minimization problem and conducted the embedding task with these selected expansion–bin pairs. The experimental results revealed that the proposed GI-RDH scheme outperformed previous methods when the embedding capacity was small.
Society 5.0 and Soft Skills in Agile Global Software Development

This paper presents the preliminary results about a systematic mapping of the soft skills suggested/considered important in the agile global software development (AGSD) with an impact on society 5.0. The papers selection method was carried out following a set of research objectives and questions defined from the GQM (Goal-Question-Metric) approach and a suggested systematic protocol for mapping in Software Engineering. Findings show that the existence of research related to soft skills in AGSD up to the date of this review, it is few, however, it is possible to observe that it has gained importance in the last three years.
Cloud computing-enabled IIOT system for neurosurgical simulation using augmented reality data access

In recent years, statistics have indicated that the number of patients with malignant brain tumors has increased sharply. However, most surgeons still perform surgical training using the traditional autopsy and prosthesis model, which encounters many problems, such as insufficient corpse resources, low efficiency, and high cost. With the advent of the 5G era, a wide range of Industrial Internet of Things (IIOT) applications have been developed. Virtual Reality (VR) and Augmented Reality (AR) technologies that emerged with 5G are developing rapidly for intelligent medical training. To address the challenges encountered during neurosurgery training, and combining with cloud computing, in this paper, a highly immersive AR-based brain tumor neurosurgery remote collaborative virtual surgery training system is developed, in which a VR simulator is embedded. The system enables real-time remote surgery training interaction through 5G transmission. Six experts and 18 novices were invited to participate in the experiment to verify the system. Subsequently, the two simulators were evaluated using face and construction validation methods. The results obtained by training the novices 50 times were further analyzed using the Learning Curve-Cumulative Sum (LC-CUSUM) evaluation method to validate the effectiveness of the two simulators. The results of the face and content validation demonstrated that the AR simulator in the system was superior to the VR simulator in terms of vision and scene authenticity, and had a better effect on the improvement of surgical skills. Moreover, the surgical training scheme proposed in this paper is effective, and the remote collaborative training effect of the system is ideal.