Reviewerly/exHarmony huggingface dataset:
DFEN: Dual Feature Enhancement Network for Remote Sensing Image Caption

The remote sensing image caption can acquire ground objects and the semantic relationships between different ground objects. Existing remote sensing image caption algorithms do not acquire enough ground object information from remote-sensing images, resulting in inaccurate captions. As a result, this paper proposes a codec-based Dual Feature Enhancement Network (“DFEN”) to enhance ground object information from both image and text levels. We build the Image-Enhancement module at the image level using the multiscale characteristics of remote sensing images. Furthermore, more discriminative image context features are obtained through the Image-Enhancement module. The hierarchical attention mechanism aggregates multi-level features and supplements the ground object information ignored due to large-scale differences. At the text level, we use the image’s potential visual features to guide the Text-Enhance module, resulting in text guidance features that correctly focus on the information of the ground objects. Experiment results show that the DFEN model can enhance ground object information from images and text. Specifically, the BLEU-1 index increased by 8.6% in UCM-caption, 2.3% in Sydney-caption, and 5.1% in RSICD. The DFEN model has promoted the exploration of advanced semantics of remote sensing images and facilitated the development of remote sensing image caption.
System for Estimation of Human Anthropometric Parameters Based on Data from Kinect v2 Depth Camera

Anthropometric measurements of the human body are an important problem that affects many aspects of human life. However, anthropometric measurement often requires the application of an appropriate measurement procedure and the use of specialized, sometimes expensive measurement tools. Sometimes the measurement procedure is complicated, time-consuming, and requires properly trained personnel. This study aimed to develop a system for estimating human anthropometric parameters based on a three-dimensional scan of the complete body made with an inexpensive depth camera in the form of the Kinect v2 sensor. The research included 129 men aged 18 to 28. The developed system consists of a rotating platform, a depth sensor (Kinect v2), and a PC computer that was used to record 3D data, and to estimate individual anthropometric parameters. Experimental studies have shown that the precision of the proposed system for a significant part of the parameters is satisfactory. The largest error was found in the waist circumference parameter. The results obtained confirm that this method can be used in anthropometric measurements.
Sequence Segmentation Attention Network for Skeleton-Based Action Recognition

With skeleton-based action recognition, it is crucial to recognize the dependencies among joints. However, the current methods are not able to capture the relativity of the various joints among the frames, which is extremely helpful because various parts of the body are moving at the same time. In order to solve this problem, a new sequence segmentation attention network (SSAN) is presented. The successive frames are encoded in each of the segments that make up the skeleton sequence. Then, we provide a self-attention block that may record the associated information among various joints in successive frames. In order to better recognize comparable behavior, a model of external segment action attention is employed to acquire the deep interrelation information among parts. Compared with the most advanced approaches, we have shown that the proposed method performs better on NTU RGB+D and NTU RGB+D 120.
Scheduling Framework for Accelerating Multiple Detection-Free Object Trackers

In detection-free tracking, after users freely designate the location of the object to be tracked in the first frame of the video sequence, the location of the object is continuously found in the following video frame sequence. Recently, technologies using a Siamese network and transformer based on DNN modules have been evaluated as very excellent in terms of tracking accuracy. The high computational complexity due to the usage of the DNN module is not a preferred feature in terms of execution speed, and when tracking two or more objects, a bottleneck effect occurs in the DNN accelerator such as the GPU, which inevitably results in a larger delay. To address this problem, we propose a tracker scheduling framework. First, the computation structures of representative trackers are analyzed, and the scheduling unit suitable for the execution characteristics of each tracker is derived. Based on this analysis, the decomposed workloads of trackers are multi-threaded under the control of the scheduling framework. CPU-side multi-threading leads the GPU to a work-conserving state while enabling parallel processing as much as possible even within a single GPU depending on the resource availability of the internal hardware. The proposed framework is a general-purpose system-level software solution that can be applied not only to GPUs but also to other hardware accelerators. As a result of confirmation through various experiments, when tracking two objects, the execution speed was improved by up to 55% while maintaining almost the same accuracy as the existing method.
CLIP-Based Adaptive Graph Attention Network for Large-Scale Unsupervised Multi-Modal Hashing Retrieval

With the proliferation of multi-modal data generated by various sensors, unsupervised multi-modal hashing retrieval has been extensively studied due to its advantages in storage, retrieval efficiency, and label independence. However, there are still two obstacles to existing unsupervised methods: (1) As existing methods cannot fully capture the complementary and co-occurrence information of multi-modal data, existing methods suffer from inaccurate similarity measures. (2) Existing methods suffer from unbalanced multi-modal learning and data semantic structure being corrupted in the process of hash codes binarization. To address these obstacles, we devise an effective CLIP-based Adaptive Graph Attention Network (CAGAN) for large-scale unsupervised multi-modal hashing retrieval. Firstly, we use the multi-modal model CLIP to extract fine-grained semantic features, mine similar information from different perspectives of multi-modal data and perform similarity fusion and enhancement. In addition, this paper proposes an adaptive graph attention network to assist the learning of hash codes, which uses an attention mechanism to learn adaptive graph similarity across modalities. It further aggregates the intrinsic neighborhood information of neighboring data nodes through a graph convolutional network to generate more discriminative hash codes. Finally, this paper employs an iterative approximate optimization strategy to mitigate the information loss in the binarization process. Extensive experiments on three benchmark datasets demonstrate that the proposed method significantly outperforms several representative hashing methods in unsupervised multi-modal retrieval tasks.
Predictive temporal patterns discovery

In recent years, the use of frequent temporal patterns as features for classification has increasingly been used and investigated. In this process, commonly frequent patterns are mined from each class separately. Then the patterns are unified, and feature selection methods may be employed, which are given to induce a classifier. However, this approach is very time consuming since the mining of each class separately takes time. In this paper, we introduce the Saraswati suite that can modify a temporal patterns discovery algorithm into a predictive temporal patterns discovery algorithm, which we demonstrate on Time Intervals Related Patterns. The suite enables predictive patterns to be favored in runtime, while mining both classes simultaneously to discover these patterns. This is through the use of a novel stopping criteria that we call the Saraswati selection criteria and strategies suite. Since the selection criteria are based on the patterns' metrics, such as their frequency in each class or their reoccurrence, and more, it is explainable to domain experts, rather than as a score as happens with common feature selection measures. We modified an existing time intervals related patterns discovery algorithm according to the Saraswati suite, and evaluated it rigorously against the current approach on six real-life datasets. Our results show that the Saraswati-based algorithm is much faster than discovery of the entire set of frequent patterns, and the selection criteria are more effective than existing state-of-the-art feature selection methods when the discovered predictive patterns are used for classification. Additionally, the selection of the patterns is explainable in the domain expert’s terminology based on several meaningful metrics.
Conspiracies, misinformation and resistance to public health measures during COVID-19 in white nationalist online communication

Recent studies documented alarming growth in antiscientific discourse among extremist groups online and especially the relatively high anti-vaccine attitudes among White Nationalists (WN). In light of accelerated politization of COVID-19 containment measures and the expansion of containment to lockdowns, masking, and more, we examine current sentiment, themes and argumentation in white nationalist discourse, regarding the COVID-19 vaccines and other containment measures. We use unsupervised machine learning approaches to analyze all conversations posted in the "Coronavirus (Covid-19)" sub-forum on Stormfront between January 2020 and December 2021 (N = 9642 posts). Additionally, we manually analyze sentiment and argumentation in 300 randomly sampled posts. We identified four discursive themes: Science, Conspiracies, Sociopolitical, and Containment. Negative- sentiment was substantially higher than what was found in prior work done before COVID-19 regarding vaccines and other containment measures. The negativity was driven mostly by arguments adapted from the anti-vaccine movement and not by WN ideology.
Anas platyrhynchos optimizer with deep transfer learning-based gastric cancer classification on endoscopic images

&lt;abstract&gt;&lt;p&gt;Gastric Cancer (GC) has been identified as the world's fifth most general tumor. So, it is important to diagnose the GC at initial stages itself to save the lives. Histopathological analysis remains the gold standard for accurate diagnosis of the disease. Though Computer-Aided Diagnostic approaches are prevalently applied in recent years for the diagnosis of diseases, it is challenging to apply in this case, due to the lack of accessible gastric histopathological image databases. With a rapid progression in the Computer Vision (CV) technologies, particularly, the emergence of medicinal image classifiers, it has become feasible to examine all the types of electron micrographs in a rapid and an effective manner. Therefore, the current research article presents an Anas Platyrhynchos Optimizer with Deep Learning-based Gastric Cancer Classification (APODL-GCC) method for the classification of GC using the endoscopic images. The aim of the proposed APODL-GCC method is to identify the presence of GC with the help of CV and Deep Learning concepts. Primarily, the APODL-GCC technique employs a contrast enhancement technique. Next, the feature extraction process is performed using a neural architectural search network model to generate a collection of feature vectors. For hyperparameter optimization, the Anas Platyrhynchos Optimizer (APO) algorithm is used which enhances the classification performance. Finally, the GC classification process is performed using the Deep Belief Network method. The proposed APODL-GCC technique was simulated using medical images and the experimental results established that the APODL-GCC technique accomplishes enhanced performance over other models.&lt;/p&gt;&lt;/abstract&gt;
Fundamentals of Machine Learning

Machine learning (ML) is a subfield of broader artificial intelligence (AI) that through programming gives computers the ability to learn from data (Alpaydin E, Introduction to machine learning. MIT Press, Cambridge, 2014; Géron A, Hands-on machine learning with Scikit-learn, Keras, and TensorFlow: concepts, tools, and techniques to build intelligent systems. O'Reilly Media, Inc., Sebastapol, 2019; Goodfellow I, Bengio Y, Courville A, Deep learning. MIT Press, Cambridge, 2016). Owning advantages in tackling problems that are too complex for explicitly programmed algorithmsAlgorithms, ML is considered as a powerful technique for making data-driven predictions and decisions. Early applications of ML, launched a few decades ago, were restricted to some specialized tasks, such as pattern recognition. With explosive growths of data and computational power over the succeeding decades, nowadays ML can be found in numerous applications even in our daily lives, from filtering email spam, ranking the search results, recommending videos and posts to language translation. The use of ML also extends widely into scientific domains, among which optics and photonics are, surprisingly or not, actively involved. In this chapter, we discuss the basics of ML in a descriptive manner. In particular, focus will be devoted to its most vibrant subfield of deep learningDeep learning (DL) (Goodfellow I, Bengio Y, Courville A, Deep learning. MIT Press, Cambridge, 2016; Kelleher JD, Deep learning. MIT Press, Cambridge, 2019).
Challenges to agile software project management practices in the context of the COVID-19 pandemic

Abstract: The Agile approach is focused on individuals and customer satisfaction, based on the dynamic and streamlined work of a team that is capable of adapting, and adapting the project to changing scenarios and demands. However, amidst the COVID-19 pandemic, Agile teams accustomed to in-person communication have encountered the challenges of working from home. The main objective of this research is to analyze the challenges generated by the pandemic context, and the consequent social distance, for the development of software projects that use Agile approaches within a large state-owned company and identify the effect it had on the course and results of the projects. The research was qualitative in nature and had two exploratory diagnostic stages. The findings indicate that there were no negative impacts of relevance on project deliveries. On the one hand, results indicated losses in socialization, in spontaneous exchanges of knowledge, and in interaction among teams, as well as a demand for greater engagement of professionals. Additionally, limitations were revealed for more complex discussions and knowledge management. The study highlights solutions that were found and/or suggested for many challenges, contributing to the literature on best practices for the Agile approach, and is focused on the unprecedented context of the COVID-19 pandemic. Our findings can contribute to other companies with a similar profile that work with Agile projects, besides contributing to building a dialogue between the academic and business environments.
Comprehensive and Systematic Review of Various Feature Extraction Techniques for Vernacular Languages

Designing a local language communication system that enables regular people to interact verbally with machines to access information or perform day-to-day operations is a major challenge for India. India is a multilingual country as there are many spoken languages. This paper presents comprehensive literature and a systematic review of low-resource languages like Indian languages for spoken language identification. It has been noticed that conventional feature extraction techniques in ASR systems such as Mel frequency cepstral coefficient (MFCC), and RASTA perceptual linear prediction (RASTA-PLP), perform poorly in loud surroundings. This paper offers an in-depth examination of numerous studies that have been conducted since 2015. The analysis and investigation were performed using the pertinent data from 50 research studies papers selected after the screening process between the years 2015 to 2022. From the research, it has been observed that due to the noisy environment accuracy of speech signal varies with different feature extraction techniques. During this study, it has been seen that LSTM speech signals in a noisy environment show higher precision as compared to other conventional techniques of feature extraction. By critical review of the various papers related to the topic, it was observed that not much work had been done on Long short-term memory (LSTM) and Bidirectional Long short term memory (Bi-LSTM) for low-resource languages like Hindi. By selecting an appropriate feature extraction technique in speech recognition (SR), the results of this review can be utilized to improve voice recognition technology/spoken language identification for noisy environments.
Handwritten Character Recognition Using Deep LSTM Approach

Nowadays, life would not be the same without technology it has become an essential part of our daily lives. One usage of such emerging technology that has altered the way we view the world is image recognition. The use of machine learning for image identification has shown to be one of the most approachable topics in technology, which is increasing fast. Image character recognition can be done using a variety of methods. To improve the accuracy in handwritten character recognition, a novel deep LSTM approach is introduced in this article. It consists of the following steps, feature extraction by CNN, followed by a series of Long Short Term Memory (LSTM) layers, a Connectionist Temporal Classification (CTC) decoder. The multiple long short term memory (LSTM) form of RNN helps to maintain the dependencies among the characters. The experimental results proves that the proposed system outperforms the exiting state-of-art-approaches.
Two Phase Ensemble Learning Based Extractive Summarization for Short Documents

Today, the internet provides us with a wealth of information from a variety of sources, including emails, news articles, the World Wide Web, and e-books. Due to a lack of time, people today do not want to read the content completely. They, therefore, aim to gather the most crucial data from multiple sources. By creating a summary, the automatic text summarizing technique can address this issue. There are primarily two summarizing methods available. One method of text summary is extractive, and the other is abstractive. Without altering the source data, extractive text summarization takes the most important sentences from a document and creates a summary. It’s possible that this approach won’t handle contradictory data correctly. To handle this contradictory data, deep learning models are very helpful. Using these deep learning models, we proposed a new model in this paper. Here, we’ve introduced multi-phase ensemble using the stacking method. In order to obtain the summary, logistic regression is applied after two layers of ensemble techniques. The suggested model is then compared to already existing models for evaluation. When compared to the state-of-the-art, the accuracy of our projected model is highly promising, with high ROUGE scores.
Radar Emitter Identification with Multi-View Adaptive Fusion Network (MAFN)

Radar emitter identification (REI) aims to extract the fingerprint of an emitter and determine the individual to which it belongs. Although many methods have used deep neural networks (DNNs) for an end-to-end REI, most of them only focus on a single view of signals, such as spectrogram, bi-spectrum, signal waveforms, and so on. When the electromagnetic environment varies, the performance of DNN will be significantly degraded. In this paper, a multi-view adaptive fusion network (MAFN) is proposed by simultaneously exploring the signal waveform and ambiguity function (AF). First, the original waveform and ambiguity function of the radar signals are used separately for feature extraction. Then, a multi-scale feature-level fusion module is constructed for the fusion of multi-view features from waveforms and AF, via the Atrous Spatial Pyramid Pooling (ASPP) structure. Next, the class probability is modeled as Dirichlet distribution to perform adaptive decision-level fusion via evidence theory. Extensive experiments are conducted on two datasets, and the results show that the proposed MAFN can achieve accurate classification of radar emitters and is more robust than its counterparts.
The Nonlinear Relationship Between Bitcoin Mining and Carbon Emissions in the Context of Renewable Energy <sup>*</sup>

This chapter presents an overview of the Bitcoin and its impacts on the environment and economics from the viewpoint of carrying out a systematic analysis of the literature related to the environmental and economic effect of digital currency. It is aimed to summarize and critically examine the points of view regarding Bitcoin mining, considering its effects on global warming and the social environment, employing peer-reviewed data associated through literatures. As a result, this study provides the chance to analyze the set of knowledge regarding the effects of the Bitcoin mining procedure on the ecosystem in regard to energy use and CO2 emissions regarding unit root tests and causality test based on nonlinear models. The results show that there exists a nonlinear causal relationship between statistics on Bitcoin mining and the CO2 emissions. The results also imply that Bitcoin remains to be a tool utilized in the economic environment for a range of objectives despite high energy consumption and some negative environmental impact within the scope of renewable energy; hence, authorities would take Bitcoin mining impacts into account to reduce CO2 emissions.
Research on Background Learning Correlation Filtering Algorithm With Multi-Feature Fusion

Aiming at the problems of occlusion, drift, and background change in target tracking, a background learning correlation filtering algorithm based on multi-feature fusion is proposed. In the framework of correlation filtering, multi-feature fusion, multi-template update, and background learning regularization are used to improve the performance of the filter in the problem of template contamination and object occlusion. The fast directional gradient histogram (FHOG), color feature (CN), and texture feature (ULBP) were extracted, and the feature channels were connected in series. Then the depth features of Conv4-4 and Conv5-4 layers were extracted through the VGG-19 network, and the appearance model of the target was constructed. To reduce the sensitivity of the filter to the sudden change of background, a background learning filter is constructed, and the alternate direction multiplier method (ADMM) is used to speed up the calculation of the filter. In the model update stage, aiming at the problem of pollution of the original template caused by target occlusion, a high-confidence multi-template fusion update strategy is proposed by fusing the template with the highest confidence in the current frame, the previous frame, and the history frame. Finally, the proposed algorithm is tested on OTB50, OTB100, UAV123, and TC128 experimental data sets, and some classical and latest algorithms. The experimental results show that the tracking accuracy and robustness of the correlation filtering algorithm are improved.