Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis of Customer Reviews Based Texts Using Classification Algorithms

Nowadays, a large number of text-based sentiments posted by customers about their reviews on products. They provide their sentiments via their reviews. Sentiment analysis is also known as an opinion mining. Businesses frequently do sentiment analysis on textual data to track the perception of their brands and products in customer reviews and to better understand their market. In this research work, it is implemented that the Naive Bayes, Random Forest, Decision Tree and Support Vector Machines classifiers and the results are compared and examined. This work has a performance-based analysis of different classifiers by assessing the accuracy of classification depending on the size of the product data sets of mobile products. The dataset which has been collected from the shopping websites of Amazon, Flipkart, Snapdeal and which is used to find the classification accuracy by its methods used for the analysis. Finally, it compares the performance of four classification methods like Naive Bayes, Random Forest, Decision Tree and Support Vector Machines to identify the best method.
Cloud Computing Resource Allocation Considering Link Switching and Computing Resource Borrowing

This paper investigates the resource sharing strategy used by different cloud computing tenants, which allows tenants to borrow computing resources from one another as well as exchange high and low-speed data transmission links. We provide a theoretical model of the strategy as well as a resource borrowing and recycling scheme, as well as simulation results. The results show that using this resource borrowing scheme can reduce the business's denial of service rate, queuing time, and overall service time, which is especially beneficial for high-load businesses. This article also found that under this strategy, as the service arrival rate increases, the average time that high- and low-traffic services are directly served by the cloud computing center is not monotonously increasing or monotonically increasing, but presents a concave curve or a convex curve, gradually approaching the state of not using resource borrowing, this is of guiding significance for analyzing the resource borrowing and optimal threshold design of actual cloud computing centers.
Using ML to Find the Semantic Region of Interest

One of the most challenging problems in computer vision and image processing is the detection of the semantic regions of interest (SRoI). In this paper, we propose a method using OpenAI's CLIP model to find SRoI by performing semantic search for objects in the image which are detected by an object detection model called Generic RoI Extractor (GRoIE). Finding the semantic regions of interest can be used in different image processing tasks such as image and video compression, enhancement, and reformatting. By knowing the semantic region of interest within images, we can improve the visual quality of images by compressing the more important parts with higher quality and the less important parts, such as the background, with a lower quality. This operation can be achieved without changing the overall compression ratio and the Peak Signal-to-noise Ratio (PSNR) quality metric. Finding the SRoI can make the processes of image enhancement and color correction more accurate by focusing only on the important parts. Moreover, for the image reformatting process, the important parts of the image may be lost. But by using the SRoI, we can reformat the image in a better way by keeping the most important regions in the frame.
ML-Based Indexing of Media Libraries for Insights and Search

Recent advances in Machine Learning (ML) have produced a new form of semantic indexing that lets users enhance searches, as well as gain new insights into their media libraries. Unlike typical search systems that use extracted metadata, semantic indexing allows users to find relevant material without the need to tag the media with selections from a predefined taxonomy. With semantic search, users can simply enter unstructured text, and the system will find the best matching media clips. The paper extends the use of the same technology to gather analytics on the data which can then be further correlated to generate various insights. — This new form of media indexing can be performed with the CLIP model from OpenAI. The model encodes images and text into embeddings that can be searched to find the closest semantic similarity, enhanced with learned cultural knowledge. This type of indexing can be made practical using a database like Elasticsearch. The system has the benefit of finding media based on keywords, synonyms, and summaries. The same system can also be used for analytics and insights, such as clustering, shot detection, and creating a 2-dimensional map to display correlations. — The paper also presents extensions to the semantic search systems. Based on a study of multiple existing models, these extensions provide new capabilities - to handle many media types, using additional languages, search for spoken phrases in audio files, finding both verbatim and semantically similar phrases, extracting semantic information that leverages multiple video frames, and searching for ambient sounds.
Monocular Depth Estimation Network Based on Swin Transformer

Abstract Estimating depth from a single image is challenging because a single 2D image may correspond to many different 3D scenes with the same depth. While most deep learning based depth prediction methods extract depth features using small convolutional kernels with small receptive fields, which results in deformed depth edges and inaccurate depth values of distant objects in the depth estimation results. Aiming at this problem, we propose a depth estimation network based on Swin Transformer and the encoder-decoder structure. We construct the encoder using the Swin Transformer network, which can encode long-range spatial dependency and extract features on various scales and across different channels. The decoder of the proposed network is in charge of fusing the features from the encoder by the operations of interpolation, concatenation, and convolution. Experiments on KITTI and NYUv2 datasets show that our proposed network can get more accurate depth edges and depth values than the state-of-the-art methods.
EKConv: Compressing Convolutional Neural Networks with Evolutionary Kernel Convolution

Abstract Convolutional neural networks (CNNs) have achieved tremendous success in visual recognition tasks but mainly rely on massive learnable parameters. To solve this problem, many effective and efficient convolution operators have been proposed, such as group-wise convolution, point-wise convolution, and depth-wise convolution. However, the above convolution operations model and optimize the weight relationship within the same convolutional layer. To reduce the network parameters, we explicitly construct the relationship between convolution kernels of adjacent layers. Specifically, we propose an evolutionary kernel convolution, namely EKConv, to generate weight parameters by group-wise convolution efficiently. In particular, EKConv makes the kernel parameters of the current convolutional layer inherit from its preceding adjacent kernel, which promotes the information exchange between convolution kernels. More importantly, EKConv is a novel plug-and-play module that can be easily embedded into mainstream CNNs. Extensive experimental results show that EKConv can compress the parameters of CNNs by a large margin yet barely sacrifice image classification performance.
AMSCN: A Novel Dual-Task Model for Automatic Modulation Classification and Specific Emitter Identification

Specific emitter identification (SEI) and automatic modulation classification (AMC) are generally two separate tasks in the field of radio monitoring. Both tasks have similarities in terms of their application scenarios, signal modeling, feature engineering, and classifier design. It is feasible and promising to integrate these two tasks, with the benefit of reducing the overall computational complexity and improving the classification accuracy of each task. In this paper, we propose a dual-task neural network named AMSCN that simultaneously classifies the modulation and the transmitter of the received signal. In the AMSCN, we first use a combination of DenseNet and Transformer as the backbone network to extract the distinguishable features; then, we design a mask-based dual-head classifier (MDHC) to reinforce the joint learning of the two tasks. To train the AMSCN, a multitask cross-entropy loss is proposed, which is the sum of the cross-entropy loss of the AMC and the cross-entropy loss of the SEI. Experimental results show that our method achieves performance gains for the SEI task with the aid of additional information from the AMC task. Compared with the traditional single-task model, our classification accuracy of the AMC is generally consistent with the state-of-the-art performance, while the classification accuracy of the SEI is improved from 52.2% to 54.7%, which demonstrates the effectiveness of the AMSCN.
Hybrid Harmony Search Optimization Algorithm for Continuous Functions

This paper proposes a hybrid harmony search algorithm that incorporates a method of reinitializing harmonies memory using a particle swarm optimization algorithm with an improved opposition-based learning method (IOBL) to solve continuous optimization problems. This method allows the algorithm to obtain better results by increasing the search space of the solutions. This approach has been validated by comparing the performance of the proposed algorithm with that of a state-of-the-art harmony search algorithm, solving fifteen standard mathematical functions, and applying the Wilcoxon parametric test at a 5% significance level. The state-of-the-art algorithm uses an opposition-based improvement method (IOBL). Computational experiments show that the proposed algorithm outperforms the state-of-the-art algorithm. In quality, it is better in fourteen of the fifteen instances, and in efficiency is better in seven of fifteen instances.
Application and Research on Foreign Language Teaching in the Context of Digital Transformation

Digital transformation of education is the focus and hot spot of education reform at present, and digital transformation will bring radical changes to the field of education and teaching. Foreign language teaching, because of its special characteristics, can receive more attention and changes, and can produce a process from quantitative to qualitative changes. How to make a good digital transformation of foreign language teaching is very important to promote foreign language teaching methods, innovation and application as well as to explore new teaching modes under digital conditions. Through the introduction of the current background and the research of the current situation at home and abroad, this paper elaborates the significance of digital transformation of foreign language teaching, and introduces the teaching scenarios, teaching contents and teaching forms under digital transformation respectively, and finally summarizes the current opportunities and challenges.
Adaptive Spatial Transformation Networks for Periocular Recognition

Periocular recognition has emerged as a particularly valuable biometric identification method in challenging scenarios, such as partially occluded faces due to COVID-19 protective masks masks, in which face recognition might not be applicable. This work presents a periocular recognition framework based on deep learning, which automatically localises and analyses the most important areas in the periocular region. The main idea is to derive several parallel local branches from a neural network architecture, which in a semi-supervised manner learn the most discriminative areas in the feature map and solve the identification problem solely upon the corresponding cues. Here, each local branch learns a transformation matrix that allows for basic geometrical transformations (cropping and scaling), which is used to select a region of interest in the feature map, further analysed by a set of shared convolutional layers. Finally, the information extracted by the local branches and the main global branch are fused together for recognition. The experiments carried out on the challenging UBIRIS-v2 benchmark show that by integrating the proposed framework with various ResNet architectures, we consistently obtain an improvement in mAP of more than 4% over the "vanilla" architecture. In addition, extensive ablation studies were performed to better understand the behavior of the network and how the spatial transformation and the local branches influence the overall performance of the model. The proposed method can be easily adapted to other computer vision problems, which is also regarded as one of its strengths.
Development of an Interactive Touchless Technology Based on Static-Electricity-Induced Luminescence

Touchless technology has garnered significant interest in recent years because of its effectiveness in combating infectious diseases such as the novel coronavirus (COVID-19). The goal of this study was to develop an inexpensive and high-precision touchless technology. A base substrate was coated with a luminescent material that emitted static-electricity-induced luminescence (SEL), and it was applied at high voltage. An inexpensive web camera was used to verify the relationship between the non-contact distance to a needle and the applied-voltage-triggered luminescence. The SEL was emitted at 20-200 mm from the luminescent device upon voltage application, and the web camera detected the SEL position with an accuracy of less than 1 mm. We used this developed touchless technology to demonstrate a highly accurate real-time detection of the position of a human finger based on SEL.
DEEDP: Document-Level Event Extraction Model Incorporating Dependency Paths

Document-level event extraction (DEE) aims at extracting event records from given documents. Existing DEE methods handle troublesome challenges by using multiple encoders and casting the task into a multi-step paradigm. However, most of the previous approaches ignore a missing feature by using mean pooling or max pooling operations in different encoding stages and have not explicitly modeled the interdependency features between input tokens, and thus the long-distance problem cannot be solved effectively. In this study, we propose Document-level Event Extraction Model Incorporating Dependency Paths (DEEDP), which introduces a novel multi-granularity encoder framework to tackle the aforementioned problems. Specifically, we first designed a Transformer-based encoder, Transformer-M, by adding a Syntactic Feature Attention mechanism to the Transformer, which can capture more interdependency information between input tokens and help enhance the semantics for sentence-level representations of entities. We then stacked Transformer-M and Transformer to integrate sentence-level and document-level features; we thus obtained semantic enhanced document-aware representations for each entity and model long-distance dependencies between arguments. Experimental results on the benchmarks MUC-4 and ChFinAnn demonstrate that DEEDP achieves superior performance over the baselines, proving the effectiveness of our proposed methods.
Unsupervised Deep Embedded Clustering for High-Dimensional Visual Features of Fashion Images

Fashion image clustering is the key to fashion retrieval, forecasting, and recommendation applications. Manual labeling-based clustering is both time-consuming and less accurate. Currently, popular methods for extracting features from data use deep learning techniques, such as a Convolutional Neural Network (CNN). These methods can generate high-dimensional feature vectors, which are effective for image clustering. However, high dimensions can lead to the curse of dimensionality, which makes subsequent clustering difficult. The fashion images-oriented deep clustering method (FIDC) is proposed in this paper. This method uses CNN to generate a 4096-dimensional feature vector for each fashion image through migration learning, then performs dimensionality reduction through a deep-stacked auto-encoder model, and finally performs clustering on these low-dimensional vectors. High-dimensional vectors can represent images, and dimensionality reduction avoids the curse of dimensionality during clustering tasks. A particular point in the method is the joint learning and optimization of the dimensionality reduction process and the clustering task. The optimization process is performed using two algorithms: back-propagation and stochastic gradient descent. The experimental findings show that the proposed method, called FIDC, has achieved state-of-the-art performance.
Enablers for Growth of Cryptocurrencies: A Fuzzy–ISM Benchmarking

Cryptocurrencies and their market capitalisation have experienced vibrant growth in the last few years. Their total market cap is more than USD 858 billion as of the date of writing and is growing, with nearly 21,984 tradeable cryptos in 530 exchanges. It is emerging as one of the biggest threats to the traditional fundraising market. The issue of the industry’s long-term viability and steady expansion is of paramount importance. Even though unsustainable and uneven growth could help boost economic activity in the short term, it would be detrimental in the long run because of the risk of extinction. This paper is one of the first attempts to identify the factors contributing to the growth of the cryptocurrency market and their effects. This paper is based on the hybrid MCDM methodology of research and uses fuzzy–ISM (interpretive structural modelling). This method is divided into three phases: identification, expert opinion, and interpretation. Sixteen factors were chosen from the previous literature and suggestions from industry professionals. Seven barriers have been framed based on the fuzzy–ISM analysis to better understand the impacts of and interrelationships among the identified barriers. The factors are further classified using fuzzy MICMAC into four major categories based on the drive power and dependence power extracted from the fuzzy matrix. This paper explains the importance of all identified factors as enablers of the acceptance of cryptocurrencies for investment and fundraising.
Hierarchical Motion Excitation Network for Few-Shot Video Recognition

Most of the existing deep learning algorithms are supervised learning and rely on a tremendous number of manually labeled samples. However, in most domains, due to the scarcity of samples or the excessive cost of labeling, it would be impracticable to provide numerous labeled training samples to the network. In this paper, a few-shot video classification network termed Hierarchical Motion Excitation Network (HME-Net) is proposed from the perspective of accumulated feature-level motion information. An HME module composed of Motion Excitation (ME) and Interval Frame Motion Excitation (IFME) is designed to extract feature-level motion patterns from adjacent frames and interval frames. The HME module can discover and enhance the feature-level motion-sensitive information in the original features. The accumulative time window is expanded to four frames in a hierarchical manner, which achieves the purpose of increasing the receptive field. After extensive experimentation, HME-Net is demonstrated to be able to consistently outperform the existing few-shot video classification models. On the UCF101 and HMDB51 datasets, our method is established as a new state-of-the-art technique for the few-shot settings of five-way three-shot and five-way five-shot video recognition.
Evaluation of emotional dynamics in social media conversations: an approach based on structures of opposition and set-theoretic measures

Abstract The paper presents the results related to the definition and adoption of structures of opposition, built with set-theoretic measures, to evaluate emotional dynamics that arise during conversations on social media. Specifically, a graded hexagon of opposition is used to compare the emotional profiles of individuals involved in a dyadic conversation. Set-theoretic measures, based on fuzzy logic, are used to construct the hexagon whose analysis allows us to understand the tendency of the conversation toward empathy or lack of empathy. The results can be useful in the context of the current trend of social media sensing and, in particular, to support social media providers in receiving early warnings related to the analysis of emotional dynamics that could lead to or degenerate into information disorder. The results have been evaluated with conversations extracted from the Empathetic Dialogue dataset.
SSAD: Single-Shot Multi-Scale Attentive Detector for Autonomous Driving

A novel Single-shot Multi-scale detection network with feature fusion and multi-scale attention mechanism is proposed for autonomous driving. The proposed model is referred to as a Single-shot Multi-scale Attentive Detector (SSAD) and it would build feature relations of the feature map in the spatial space. The proposed network highlights pedestrian and vehicle regions on the extracted feature map and also suppresses irrelevant regions, from the global relation information and thereby providing reliable guidance for autonomous driving, while detecting smaller and occluded targets. SSAD design is simple, accurate and computationally efficient. Evaluation results of the proposed network on multiple datasets have achieved considerable and promising results. Experimental results show that the SSAD network when tested on Pascal Voc-2007, INRIA, Caltech, and City Persons datasets, outperforms many state-of-the-art (SOTA) detectors.
Context-aware and co-attention network based image captioning model

ABSTRACTABSTRACTPrevious captioning methods only rely on semantic-level information considering the similarity of features between image regions in visual space and ignoring the linguistic context incorporated in the decoder for caption generation. In this paper, a transformer-based co-attention network is proposed which uses linguistic information to capture the pairwise visual relationships among objects and significant visual features. We infer the entity words from the visual content of objects, during the caption generation process. Also, we infer the interactive words by focusing on the relationship between entity words, based on the relational context between words generated in the course of caption decoding. We use linguistic contextual information as a guiding force to discover the relationships between objects efficiently. Further, we capture both intra-modal and inter-modal interactions using the multilevel co-attention network. Our model attains 44.1/33.6 BLEU@4, 30.8/25.1 METEOR, 61.9/55.1 ROUGE, 132.1/69.8 CIDEr, and 24.1/17.8 SPICE scores on MSCOCO and Flickr30k datasets, respectively.KEYWORDS: Image captioningco-attention networktransformercontext-awarevisual relationshipscomputer visionnatural language processingsemantic Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsHimanshu SharmaHimanshu Sharma is an Associate Professor in the Department of Computer Engineering and Applications, GLA University, Mathura, India. He has done his Ph.D. from GLA University Mathura and M.Tech. from NSIT, New Delhi, India. His area of research is Computer Vision, Image Processing, and Natural Language Processing. He has published many research papers in reputed journals and conferences.Swati SrivastavaSwati Srivastava is currently working as an Assistant Professor in the Department of Computer Engineering and Applications, GLA University, Mathura, India. She has completed her Ph.D. in Computational Intelligence from HBTU Kanpur, India, and M.Tech. from NIT Allahabad, India. Her area of research includes high-dimensional neurocomputing, computational intelligence, machine learning, and computer vision focused on biometrics. She has published many research papers in reputed journals and conferences.