Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis of Floods on Twitter Social Media Using the Naive Bayes Classifier Method with the N-Gram Feature

Indonesia is 6th largest population affected by floods in the world, which is 640.000 people every year. Indonesia areas that often experience floods due to high-intensity rainfall and tropical climate. Recently, there was a flood in South Kalimantan on January 14, 2021. From this incident, few netizen expressed their opinions about the natural flood disaster through Twitter social media. In this study, the author will classify netizen views regarding the natural flood disaster so that the netizen is aware of the incident and they can prevent flood causes. We will divide the tweet into relevant and irrelevant categories to categorize the incident using the Naïve Bayes Classifier. This research implements N-gram features to consider the most efficient method for determining a classification. We use Naïve Bayes because it assumes all variables are unique and provides weight to the text data using N-Gram. The importance of text data could be used to create a Naïve Bayes Classification model to calculate the probability. The naïve Bayes method can be implemented in classifying natural flood disasters. The tweet within the result using bigram will give higher accuracy than unigram or trigram. According this study the goverment can have plan for future mitigation action.
Dialect Password Recognition In Smart Home Based On Convolutional Neural Network

At present, password recognition in smart home has achieved some research results, but due to the relatively large China's regional differences, dialect password recognition rate in smart home is low. Aiming at the defects of dialect password recognition in smart home, a dialect password recognition system based on Convolutional Neural Network was constructed. In this study, Mel Frequency Cepstrum Coefficient is used to extract the features of Anhui northern dialect, and a Convolutional Neural Network model is constructed for training and recognition. Experimental results show that the model has a high recognition rate for dialect passwords in smart home.
Student Attendance using Face Recognition Technology

Among the most important aspects of school is keeping records of students' attendance. Students' college enrollment is affected by a variety of factors, including their attendance record. An accurate attendance system is common in most educational settings. To keep track of the attendance of students throughout class, facial recognition technology is deployed. Students' attendance records may be integrated into the college's academic information management, according to this paper. Students' presence is tracked using a mixture of Computer Vision and Matlab facial recognition software. This research examines the program that requires as well as the elements that impact the device's correctness and effectiveness. Using this paradigm, college organizations may create attendance tracking systems for their students. Face-recognition-based recordings may be used in school institutions, mostly as an overall computer or as a supplement to a current framework, according to this research.
English Long Sentence Segmentation and Translation Optimization of Professional Literature Based on Hierarchical Network of Concepts

The structure of English long sentences is generally complicated, with complicated logical levels, many parallel elements, many modifiers and conjunctions, and long post attributives. Moreover, pronouns in English long sentences often need to be judged according to the context. Complex English long sentences are very common in English and even run through the whole English article. The analysis results of these complex long sentences will seriously affect the quality and readability of machine translation. In this paper, HNC (hierarchical network of concepts) method is improved to realize the segmentation of long sentences, so as to simplify sentence patterns. According to the characteristics of professional literature, this paper puts forward a translation optimization method of professional literature combining HNC with statistics, which greatly improves the recognition efficiency of unknown words by extracting professional terms. The results show that the word segmentation method based on HNC and statistics proposed in this paper has achieved a good word segmentation effect in the open test environment, with an accuracy rate of 93.38% and a recall rate of 94.51%. The conclusion shows that our method can make full use of the knowledge of the source tree database, thereby improving the accuracy of the syntactic model on the target tree database.
Transformer-based Models for Supervised Monocular Depth Estimation

Existing traditional solutions for monocular depth estimation, usually use convolution networks as the backbone of their model architecture. This work presents an encoder-decoder network using a transformer architecture that can perform monocular depth estimation on a single RGB image. For environment perception and autonomous navigation systems, where depth estimation is done on edge devices, there is a need for lightweight and efficient models. It is shown that transformer-based architectures provide comparable results to the currently used convolution networks with significantly fewer parameters. Unlike convolutional networks, transformers don't downsample the input progressively at each layer. Maintaining a similar resolution throughout the encoding process allows for global awareness at each stage. 2 different decoder models are implemented on top of a transformer encoder and their usability is evaluated for depth estimation. On comparing with a comparable convolution network, it is observed that on the KITTI outdoor dataset, the lighter transformer model performs better in terms of robustness and accuracy.
Multi-View Stereo Using Graph Cuts-Based Depth Refinement

Multi-View Stereo (MVS) methods tackle the ill-posed inverse problem of recovering an object&#x0027;s 3D structure from its multi-view calibrated images. High computational cost restricts most MVS methods from using global information for depth estimation. We present a depth map-based MVS method that uses global information to estimate the depths of all pixels in an image simultaneously. To this end, we transform the depth refinement problem into computing max-flow/min-cut on a 3D grid graph with offset vertices. The <inline-formula><tex-math notation="LaTeX">$s{-}t$</tex-math></inline-formula> min-cut of this graph corresponds to the minimization of an energy functional consisting of photo-consistency and smoothness terms. Experimental results on indoor and outdoor datasets validate the efficacy of our method, especially on models with low textured regions where global information is necessary to infer the correct depth.
Lateral Feature Enhancement Network for Page Object Detection

In this paper, lateral feature enhancement (LFE) backbone network is proposed to enrich feature representation effectively for page object detection across various scales. Our LFE backbone network has three feature enhancement modules. Firstly, feature enhancement of large page object is a bottom-up feature pyramid, enhancing features of large page objects which convey more important information to readers. Secondly, lateral feature enhancement includes a top-down feature pyramid propagating representative semantical features to lower layers, and a lateral connection for feature enhancement in each layer. Thirdly, lateral skip connection is designed to retain the original feature details. The stacking strategies of bottom-up, top-down and lateral connections are beneficial for overall object detection. Visualization of feature indicates that the proposed LFE backbone network enhances global semantic information as well as detailed features of small page objects. Comparative experiments on the two state-of-the-art datasets show that it achieves excellent results with 0.950 mAP on PubLayNet and 0.892 mAP on POD with more strict metric IoU=0.8 respectively. Compared with both computer vision (CV) based unimodal detectors and multi-modal detectors, the proposed LFE network performs excellently. Visual effect experiments compare performances of CV-based detectors. The results show our detector outperforms others with strict metric, especially in the detection of small page objects.
Physics-Guided Adversarial Machine Learning for Aircraft Systems Simulation

In the context of aircraft system performance assessment, deep learning technologies allow us to quickly infer models from experimental measurements, with less detailed system knowledge than usually required by physics-based modeling. However, this inexpensive model development also comes with new challenges regarding model trustworthiness. This article presents a novel approach, physics-guided adversarial machine learning (ML), which improves the confidence over the physics consistency of the model. The approach performs, first, a physics-guided adversarial testing phase to search for test inputs revealing behavioral system inconsistencies, while still falling within the range of foreseeable operational conditions. Then, it proceeds with a physics-informed adversarial training to teach the model the system-related physics domain foreknowledge through iteratively reducing the unwanted output deviations on the previously uncovered counterexamples. Empirical evaluation on two aircraft system performance models shows the effectiveness of our adversarial ML approach in exposing physical inconsistencies of both the models and in improving their propensity to be consistent with physics domain knowledge.
Multisensor Collaboration Network for Video Compression Based on Wavelet Decomposition

Multi-sensor data has significant advantages over single sensor data due to the unique property of each sensor. We discover that multi-sensor collaboration can save bits and achieve stable compression performance according to quantization parameter (QP). In this paper, we propose a multi-sensor collaboration network for video compression based on wavelet decomposition, called MSCN. We introduce MSCN into 3D video coding based on color and depth sensors. The images acquired by a color sensor represent color and texture of the scene, while the images obtained by a depth sensor represent 3D geometric shape of the scene objects. Two sensor data are complementary, and color images help to reconstruct their corresponding depth images. First, we perform uniform sampling on the input depth video. Then, we compress the color and downsampled depth videos using 3D-HEVC codec. Finally, we reconstruct the depth video from the decoded color and depth videos by color guided depth super-resolution (SR). Experimental results show that MSCN achieves average BD-rate reductions of {−9.3%, −65.6%, −66.3%} and {−6.2%, −67.7%, and −69.2%} on 3D-HEVC test datasets for sampling factors 1, 2 and 4 in Random Access (RA) and All Intra (AI) configurations, respectively. Moreover, they verify that multi-sensor collaboration remarkably saves bits in video compression.
Automatic Modulation Classification for Cognitive Radio Systems using CNN with Probabilistic Attention Mechanism

This paper studies automatic modulation classification (AMC) for cognitive radio systems. We propose a deep learning neural network approach enhanced with an intelligent attention mechanism to correctly classify, detect, and segment spatially distributed modulation data. AMC is achieved by training the neural network to focus only on specific significant regions learnt using the attention mechanism. The proposed approach is tested for detection efficiency and accuracy to distinguish different modulation data using the publicly available RML2016.10a dataset. The outcome shows the accuracy of the proposed scheme is comparable to other state-of-the-art deep learning algorithms with a reduced complexity. The real-time assessment of the temporal states is achieved based on the spectral characteristics of modulation constellation images at various signal-to-noise ratio (SNR) values. The model performance is evaluated using mean average precision (mAP), F1 score, and speed-accuracy trade-off.
Jarvis: A Voice-based Context-as-a-Service Mobile Tool for a Smart Home Environment

In this paper we introduce Jarvis, a context-as-a-service mobile tool, which enables context-aware data collection, service discovery, and computer-aided situational awareness through a conversational User Interface (UI). At the core of Jarvis are two main components: (i) a voice-based UI to translate speech to Context Definition and Query Language (Speech-to-CDQL), and (ii) an operational component called Context-as-a-Service (CoaaS), which enables smart things and IoT silos to discover, validate and share relevant and dependable context. The UI is based on two machine learning models: a Speech-to-Text model and a Text-to-CDQL model based on an encoder-decoder architecture. Jarvis is developed as a mobile application that allows people with different backgrounds to interact with various IoT devices. Our demo shows how easy Jarvis can be used for context-aware data collection and to interact with diverse objects in a smart home environment through voice.
Structured Sparse Ternary Compression for Convolutional Layers in Federated Learning

In Cross-device Federated Learning, communication efficiency is of paramount importance.Sparse Ternary Compression (STC) is one of the most effective techniques for considerably reducing the per-round communication cost of Federated Learning (FL) without significantly degrading the accuracy of the global model, by using ternary quantization in series to top k sparsification.In this paper, we propose an original variant of STC that is specifically designed and implemented for convolutional layers.Our variant is originally based on the experimental evidence that a pattern exists in the distribution of client updates, namely, the difference between the received global model and the locally trained model.In particular, we have experimentally found that the largest (in absolute value) updates for convolutional layers tend to form clusters in a kernel-wise fashion.Therefore, our primary novel idea is to a-priori restrict the elements of STC updates to lay on such a structured pattern, thus allowing us to further reduce the STC communication cost.We have designed, implemented, and evaluated our novel technique, called Structured Sparse Ternary Compression (SSTC).Reported experimental results show that SSTC shrinks compressed updates by a factor of x3 with respect to traditional STC and with a reduction up to x104 with respect to uncompressed FedAvg, at the expense of negligible degradation of the global model accuracy.
Joint Fine Time Synchronization and Channel Estimation Using Deep Learning for Wireless Communication Systems

This paper presents a joint fine time synchronization and channel estimation scheme based on deep learning (DL) for wireless communication systems. The scheme adopts a specific training sequence structure with both cyclic prefixing and cyclic postfixing. It works excellently without setting a search range and a threshold as required by the conventional method based on the same training sequence structure. Simulation results demonstrate that the proposed DL-based scheme has significant performance gains for most cases as compared with the conventional method. With improved time synchronization, better channel estimation performance is achieved accordingly.
Feature Extraction Techniques for Gender Classification Based on Handwritten Text: A Critical Review

Features of the handwritten text play a vital role in the area of handwriting identification. It became more challenging when one has to identify gender, age, and handedness of the person through handwriting. In last two decade, the use of various feature extraction techniques immerged having advantages one on the other. Ample research is done on writer identification systems by implementing various feature extraction techniques. In this paper, we have shifted the concern toward various features and features extraction techniques implemented on gender identification through handwriting. The objective of this survey is to present the critical review of work done in area feature extraction in gender identification taking only handwriting into consideration. We have categorized all the feature extraction techniques used by the researchers for gender classification into four broad categories: statistical-, transform-, gradient-, and model-based techniques. From the survey, we have identified few techniques that deserve future attention of the researchers for optimal results.
Fuzzy Logic with Superpixel-Based Block Similarity Measures for Secured Data Hiding Scheme

With time, humans have realized, the internet is the best alternative for exchange of messages through multimedia documents, especially images. Thus, its imperceptibility and security are big concerns. Recently, Ashraf et al. (Heliyon 6(5):e03771, [1]) evaluated similarity for image steganography using interval type-2 fuzzy logic, but the visual quality (PSNR) is not as good as current demand. To solve the problem, we have proposed a data hiding scheme by combining distinct and vague characteristics of an image through the techniques of superpixel and fuzzy logic respectively. Superpixel and TSK Fuzzy logic model with rule base is used to identify non-uniform regions of the image where secret bits are embedded in coefficients of quantized Discrete Cosine Transform (DCT). This technique can be used for secret data communications. With an average PSNR of 58 dB, the proposed approach provides excellent visual quality. Finally, in order to illustrate the efficacy of our technique, we compared the proposed system to existing methodologies.
Bengali POS Tagging Using Bi-LSTM with Word Embedding and Character-Level Embedding

Part-of-speech tagging (POS) is an important and very fundamental process in natural language processing (NLP). POS tagging is required as a preprocessing task in many types of linguistic research such as named entity recognition (NER), word sense disambiguation, information extraction, natural language translation, and sentiment analysis. In this paper, we propose a practical Bengali POS tagger, which takes as input a text written in Bengali and gives a POS tagged output. In recent times, Bi-LSTM networks have been proven effective in sequential data processing but not very much tested on resource-poor and inflectional languages like Bengali. This paper addresses the issues of the POS tagging task for the Bengali language using Bi-LSTM with transfer learning by applying pre-trained word embedding information. The POS tagged output from our proposed model can be used directly for other applications of Bengali language processing as our proposed tagger can also handle out-of-vocabulary (OOV) words. Our experiment reveals that Bi-LSTM with transfer learning is effective for tagging Bengali documents.
Gradient Guided Dual-Branch Network for Image Dehazing

Recently, massive deep learning-based image dehazing methods have sprung up. These methods can effectively remove most of the haze and obtain far better results than the traditional methods. With the removal of the haze, however, edge details of the image are also lost, which is usually more noticeable in the gradient space. This paper proposes a gradient guided dual-branch network (GGDB-Net) for image dehazing. Specifically, we explore the hazy image gradient map to guide our model to focus on the hazy regions and edge restoration. We implement two parallel branches with a comprehensive loss function, which collaborate to dehaze and repair the lost edges in haze images. Moreover, the gradient-guided approach can potentially be applied to existing learning-based image dehazing models to boost their performance. Experimental results indicate that our results have good visual perceptions and are comparable to state-of-the-art methods in quantitative metrics.
Expanded and Filtered Features Based ELM Model for Thyroid Disease Classification

Thyroid disorder affects the regulation of various metabolic processes throughout the human body. Structural and functional disorders can affect the body and the brain. The computer-aided diagnosis system can identify the kind of thyroid disease. One such machine learning framework is presented in this paper to recognize disease existence and type. This paper presents a fuzzy adaptive feature filtration and expansion-based model to generate the most relevant and contributing features. This two-level filtration model is processed in a controlled fuzzy-based multi-measure evaluation. At the first level, the composite-fuzzy measures are combined with expert’s recommendations for identifying the ranked and relevant features. At the second level, the statistical computation-based distance measure is applied for expanding the featureset. The fuzzification is applied to the expanded featureset for transiting the continuous values to fuzzy-values. At this level, the fuzzy-based composite-measure is applied for selecting the most contributing and relevant features over the expanded dataset. This processing featureset is processed by the Extreme Learning Machine (ELM) classifier to predict the disease existence and class. Five experiments are conducted on two datasets for validating the performance and reliability of the proposed framework. The comparative analysis is conducted against the Naive Bayes, Decision Tree, Decision Forest, Random Tree, Multilevel Perceptron, and Radial Basis Function (RBF) Networks. The analysis outcome is taken in terms of accuracy, error, and relevancy-based parameters. The proposed framework claims a significant gain in accuracy, relevancy, and reduction in the error rate.
Deep Learning Feature Extraction Using Attention-Based DenseNet 121 for Copy Move Forgery Detection

In modern society, digital images can be far-reaching, and the images are manipulated by various software and hardware technologies. The image forgery activities are undertaken by the attackers mainly for damaging the reputation of people or receiving fiscal gain, etc. Taking this into consideration, many techniques are developed to detect the forged images. In this paper, a new deep learning-based approach is introduced for copy-move forgery detection. The input images are segmented into non-overlapping patches using superpixel-based modified dense peak clustering and the features are extracted from the segmented patches by applying deep learning structure of attention-based DenseNet 121 model. Besides, to compare every block, the depth of each pixel is reconstructed, and eventually matching process is carried out using the adaptive chimp patch matching approach, which detects the suspicious forged regions in an image. Finally, the matched keypoints are merged with the segmented patches using the merged keypoint matching algorithm. As a result, the new deep learning approach has detected the forged regions efficiently from the tampered image with less time even the image is compressed, rotated, or scaled. The performance is evaluated in terms of recall, precision, accuracy, F1-score, computational time, and False Positive Rate (FPR). Moreover, the performance is compared with the other existing approaches, and the outcomes showed that the proposed method has achieved higher accuracy of 97%, recall of 99%, precision of 97.84%, F1-score of 98.81%, FPR of 0.022 and less computational time of 2.5 s.