Reviewerly/exHarmony huggingface dataset:
Cross-Modality Double Bidirectional Interaction and Fusion Network for RGB-T Salient Object Detection

RGB-T salient object detection (SOD) aims to detect and segment saliency regions on RGB images and the corresponding thermal maps. The ability of alleviating the modality difference between RGB and thermal modality plays a vital role in the development of RGB-T SOD. However, most of the existing methods try to integrate multi-modal information through various fusion strategies, or reduce the modality difference via unidirectional or undifferentiated bidirectional interaction, but failing in some challenging scenes. To deal with the above question, a novel Cross-Modality Double Bidirectional Interaction and Fusion Network (CMDBIF-Net) for RGB-T SOD is proposed. Specifically, we construct an interactive branch to indirectly bridge the RGB and thermal modalities. In addition, we propose a double bidirectional interaction (DBI) module composed of a forward interaction block (FIB) and a backward interaction block (BIB) to reduce the cross-modality differences. Moreover, a multi-scale feature enhancement and fusion (MSFEF) module is introduced to integrate the multi-modal features with considering the internal gap of different modality. Finally, we use a cascaded decoder and a cross-level feature enhancement (CLFE) module to generate high-quality saliency map. Extensive experiments are conducted on three publicly available RGB-T SOD datasets shows that the proposed CMDBIF-Net achieves outstanding performance against the state-of-the-art (SOTA) RGB-T SOD methods.
Attentional Multi-Channel Convolution With Bidirectional LSTM Cell Toward Hate Speech Prediction

Online social networks(OSNs) facilitate their users in real-time communication but also open the door for several challenging problems like hate speech and fake news.This study discusses hate speech on OSNs and presents an automatic method to identify hate messages.We introduce an attentional multi-channel convolutional-BiLSTM network for the classification of hateful content.Our model uses existing word representation techniques in a multi-channel environment having several filters with different kernel sizes to capture semantics relations at various windows.The encoded representation from multiple channels passes through an attention-aware stacked 2-layer BiLSTM network.The output from stacked 2-layer BiLSTM is weighted by an attention layer and further concatenated and passes via a dense layer.Finally, an output layer employing a sigmoid function classifies the text.We investigate the efficacy of the presented model on three Twitter-related benchmark datasets considering four evaluation metrics.In comparative evaluation, our model beats the five state-of-the-art and the same number of baseline models.The ablation study shows that the exclusion of channels and attention mechanism has the highest impact on the performance of the presented model.The empirical analysis analyzing the impact of different word representation techniques, optimization algorithms, activation functions, and batch size on the presented model ascertains the use of their optimal values.
Multi-stage context refinement network for semantic segmentation

Convolutional neural networks have been widely used in image semantic segmentation. However, continuous downsampling operations in convolutional neural networks (such as pooling or convolution with step size) reduce the initial image resolution and lose the spatial details of the image, resulting in blurred image segmentation results. To alleviate this problem, in this paper we propose a multi-stage context refinement network (MCRNet) for semantic segmentation. Specifically, we first construct a Lowest-resolution Chain Context Aggregation (LCCA) module to encode rich semantic information. For obtaining more spatial detail information, we further build a High-resolution Context Attention Refinement (HCAR) module consisting of context feature extraction and context feature refinement. Finally, MCRNet fuses the context information generated by LCCA and HCAR for pixel prediction. Experimental results on three challenging semantic segmentation datasets, namely PASCAL VOC2012, ADE20K and Cityscapes, reveals that our proposed MCRNet is effective.
Blockchain adoption in agri-food supply chain management: an empirical study of the main drivers using extended UTAUT

Purpose Blockchain technology can overcome many complicated problems related to confidentiality, integrity and availability of fast and secure distributed systems in the agri-food supply chain. In emerging economies like India, blockchain application in the agri-food supply chain is still new, and their adoption is underdeveloped. This paper aims to investigate the drivers of blockchain technology adoption and their effect on the behavioral intention of stakeholders in adopting blockchain technology among various stakeholders in the agri-food supply chain. The study also develops a framework to enhance understanding of blockchain adoption in the agri-food supply chain as well as the stakeholders' motivation in seeking blockchain solutions. Design/methodology/approach Considering the most significant aspects of blockchain adoption in the agri-food supply chain, this study attempts to develop an adoption model by using the extended unified theory of acceptance and technology model with interfirm trust and transparency as additional factors. Data was collected from a sample of 200 stakeholders in the North Indian state of Punjab. The empirical analysis was carried out using structural equation modeling in Smart PLS3. Findings The findings supported the developed framework and the results of SEM indicate that all the paths are supported. In particular, the findings of the study reveal that performance expectancy, effort expectancy, social influence, facilitating conditions, interfirm trust and transparency are the drivers of blockchain adoption and have a significant impact on the behavioral intention of stakeholders. Cumulatively, the results positively impact the performance of agri-food supply chain. From this study, it is found that the adoption of blockchain technology in agri-food supply chain enhances their performance. Originality/value The originality of the study lies in the developed framework, technology adoption will help them focus in the right direction by eliminating manual methods and converting the agri-food supply chain into a digitalization system.
DiTing: A large-scale Chinese seismic benchmark dataset for artificial intelligence in seismology

In recent years, artificial intelligence technology has exhibited great potential in seismic signal recognition, setting off a new wave of research. Vast amounts of high-quality labeled data are required to develop and apply artificial intelligence in seismology research. In this study, based on the 2013–2020 seismic cataloging reports of the China Earthquake Networks Center, we constructed an artificial intelligence seismological training dataset (“DiTing”) with the largest known total time length. Data were recorded using broadband and short-period seismometers. The obtained dataset included 2,734,748 three-component waveform traces from 787,010 regional seismic events, the corresponding P- and S-phase arrival time labels, and 641,025 P-wave first-motion polarity labels. All waveforms were sampled at 50 Hz and cut to a time length of 180 s starting from a random number of seconds before the occurrence of an earthquake. Each three-component waveform contained a considerable amount of descriptive information, such as the epicentral distance, back azimuth, and signal-to-noise ratios. The magnitudes of seismic events, epicentral distance, signal-to-noise ratio of P-wave data, and signal-to-noise ratio of S-wave data ranged from 0 to 7.7, 0 to 330 km, –0.05 to 5.31 dB, and –0.05 to 4.73 dB, respectively. The dataset compiled in this study can serve as a high-quality benchmark for machine learning model development and data-driven seismological research on earthquake detection, seismic phase picking, first-motion polarity determination, earthquake magnitude prediction, early warning systems, and strong ground-motion prediction. Such research will further promote the development and application of artificial intelligence in seismology.
Cybersecurity of Autonomous Vehicles: A Systematic Literature Review of Adversarial Attacks and Defense Models

Autonomous driving (AD) has developed tremendously in parallel with the ongoing development and improvement of deep learning (DL) technology.However, the uptake of artificial intelligence (AI) in AD as the core enabling technology raises serious cybersecurity issues.An enhanced attack surface has been spurred on by the rising digitization of vehicles and the integration of AI features.The performance of the autonomous vehicle (AV)-based applications is constrained by the DL models' susceptibility to adversarial attacks despite their great potential.Hence, AI-enabled AVs face numerous security threats, which prevent the large-scale adoption of AVs.Therefore, it becomes crucial to evolve existing cybersecurity practices to deal with risks associated with the increased uptake of AI.Furthermore, putting defense models into practice against adversarial attacks has grown in importance as a field of study amongst researchers.Therefore, this study seeks to provide an overview of the most recent adversarial defensive and attack models developed in the domain of AD.
A comprehensive Examination of the potential application of Chat GPT in Higher Education Institutions

&lt;p&gt;The emergence of large language models such as Chat Generative Pretrained Transformer (GPT) has resulted in a paradigm shift in the education space. GPT, released by OpenAI, can generate suitable responses to human input text in a conversational style, making it attractive for laborious written tasks, such as summarizing and responding to questions in higher education institutions. While several studies reveal the real implementations of GPT in higher education institutions, a comprehensive examination of its potential at the micro and macro levels of higher education is lacking. To address this gap, the researchers of this study examined the potential capabilities of GPT through three lenses: student-facing, teacher-facing, and system-facing tools. The purpose of this study is to provide a robust and nuanced understanding of the appropriateness of GPT's use among stakeholders, such as educators, administrators, and students in higher education. Finally, the study presents a set of applicable recommendations for future researchers.&lt;/p&gt;
AI and Digital Transformation in Higher Education: Vision and Approach of a Specific University in Vietnam

The Fourth Industrial Revolution is opening up new opportunities and challenges for all industries, professions, and fields, aiming to bring humanity more optimal tools and services. During the Fourth Industrial Revolution, digital transformation has been one of the most critical problems. Artificial Intelligence (AI) and the Internet of Things (IoT) are two technologies that have the potential to cause the biggest breakout to evolve in the educational domain. In recent years, digital transformation has seen implementation across all sectors, including education, healthcare, agriculture, transportation, and other smart ecosystems. Among those areas, education, especially higher education, is among the most challenging due to the diversity in training programs, duration, and subjects. The Internet of Things makes it possible to create smart and ubiquitous learning environments, while artificial intelligence can completely transform the way we learn and teach. In this paper, we present the digital transformation process in higher education in Vietnam and internationally and analyze some characteristics of Vietnamese higher education in the digital transformation process. Moreover, we present the vision, approach, and challenges to digital transformation at universities of low- and middle-income countries from the perspective of the Hung Yen University of Technology and Education in Vietnam.
A brief overview of recently launched digital libraries of India

Purpose This paper evaluates the different digital libraries (DLs) in India developed in the past two decades. These DLs help advance scholarship and facilitate the reading habits of their users. Many of these DLs have a rich collection of vernacular literature depicting India’s diverse cultural heritages and traditions. DLs in India also help in outreaching global researchers and knowledge seekers. Many diaspora communities use these DLs frequently and other stakeholders such as the international scholars interested in Indic civilization. This paper finally suggests a way forward to make operational DL initiatives discoverable to humans and machines with the adaptation of FAIR principles that make e-resources findable, accessible, interoperable and reusable for their discovery beyond respective DL portals. Design/methodology/approach This study used a desk survey of DL initiatives in India. Their salient features are obtained from their respective Web portals and social media profiles. Findings This study identified twelve operational DL initiatives in India. Out of them, the newest five DL initiatives are described in this paper. Originality/value This study reflects original findings on the newest five DL initiatives of India. These findings were not earlier reported in a journal article.
Automatic Quality Improvement of Data on the Evolution of 2D Regions

This work deals with data cleaning and quality improvement when representing the evolution of 2D regions extracted from real-world observations. It presents a method that combines quadtrees and time series to identify inconsistencies and poor-quality data in a sequence of 2D regions. Our algorithm splits a 2D space recursively into buckets and creates time series using spatial functions on bucket-delimited subregions. Smaller buckets represent the subregions with a higher number of inconsistencies over time. Then, it uses time series outlier detection methods and consistency metrics to identify polygons that are poor-quality representations and remove them from the original sequence. The proposed method identifies errors and inaccuracies even if they occur in several consecutive observations. We evaluated our strategy using a dataset extracted from real-world videos and compared it with another method from the literature. The results prove the effectiveness of this proposal.
Data-Based Modeling and Control of Dynamical Systems: Parameter Estimation

Parameter estimation methods to provide data-based models to control complex dynamical systems are reviewed. Starting from least square minimization of the equation error, the tutorial provides an overview of how different perspectives of parameter estimation lead to various algorithms that are used in diverse contexts. Both statistical and deterministic approaches are discussed, and the utility of model inferences are explained. The discussions provide a context and review relevant background with respect to three application papers involving recent advances in Gaussian Process Regression (GPR), state estimation approaches and data-driven modeling.
Cloud Data Backup and Recovery Method Based on the DELTA Compression Algorithm

To keep the original storage proportion of cloud data parameters after an information attack, a cloud data backup and recovery method based on a delta compression algorithm is proposed in this paper. First, improve the key file management strategy in the delta compression framework, and then complete the cloud data processing based on the delta compression algorithm by standardizing the transmission form of data information files. Then, the backup processing technology and recovery application mechanism are studied to realize the smooth application of cloud data backup and recovery methods based on the delta compression algorithm. The experimental results show that under the action of the delta compression algorithm, the storage proportion of cloud data parameters will not decrease infinitely with increasing information attack intensity, which meets the practical application requirements of stably storing data and information files.
An Improved Algorithm for Intelligent Identification of Distribution Network Equipment

With the continuous development of the distribution network, the distribution network equipment is gradually diversifying and becoming more complex. The inability of humans to correctly identify the type and model of distribution network equipment has led to frequent safety accidents. Many studies have used deep learning algorithms to identify distribution network equipment in the field, which can effectively reduce equipment mishandling in field operation and maintenance. In the process of real-world photography of distribution network equipment in field operations, there are problems such as complex backgrounds, many co-occurring object objects, different shooting distances and variable angles. The existing methods are difficult to effectively and accurately identify the target equipment. In response to the above problems, this paper proposes an improved algorithm model for intelligent identification of distribution network equipment. The model is based on a VGG16 recognition model with initialized weights to extract image features, combined with data sets for data enhancement operations such as cropping and flipping. At the same time, we introduce an attention mechanism into the model to make it more focused on specific regions in the image. Based on this, the model performs scene discrimination and device prediction tasks. The experimental results show that the model has a high accuracy rate and is practical for the identification of distribution network equipment in complex backgrounds.
Comparison of an in‐house hybrid DIR method to NiftyReg on CBCT and CT images for head and neck cancer

An in-house hybrid deformable image registration (DIR) method, which combines free-form deformation (FFD) and the viscous fluid registration method, is proposed. Its results on the planning computed tomography (CT) and the day 1 treatment cone-beam CT (CBCT) image from 68 head and neck cancer patients are compared with the results of NiftyReg, which uses B-spline FFD alone. Several similarity metrics, the target registration error (TRE) of annotated points, as well as the Dice similarity coefficient (DSC) and Hausdorff distance (HD) of the propagated organs at risk are employed to analyze their registration accuracy. According to quantitative analysis on mutual information, normalized cross-correlation, and the absolute pixel value differences, the results of the proposed DIR are more similar to the CBCT images than the NiftyReg results. Smaller TRE of the annotated points is observed in the proposed method, and the overall mean TRE for the proposed method and NiftyReg was 2.34 and 2.98 mm, respectively (p < 0.001). The mean DSC in the larynx, spinal cord, oral cavity, mandible, and parotid given by the proposed method ranged from 0.78 to 0.91, significantly higher than the NiftyReg results (ranging from 0.77 to 0.90), and the HD was significantly lower compared to NiftyReg. Furthermore, the proposed method did not suffer from unrealistic deformations as the NiftyReg did in the visual evaluation. Meanwhile, the execution time of the proposed method was much higher than NiftyReg (96.98 ± 11.88 s vs. 4.60 ± 0.49 s). In conclusion, the in-house hybrid method gave better accuracy and more stable performance than NiftyReg.
Africa through times

Africa has been portrayed as a continent with poor governance, widespread poverty, economic stagnation, and social services whose standards have lagged behind the rest of the world. Oftentimes these portrayals fail to put the current conditions into context by not examining the underlying circumstances. While the negative characterization may not have fully changed, there is a growing realization that this old view is no longer correct. For example, based on existing development indicators, a number of African countries have been performing well. Income per capita of many countries is growing and more people on the continent are getting educated, implying that the local stock of skilled labour is increasing. Indeed, to obviate the circumstances underlying the current characterization of the African continent, Africa’s development is presented from an historical perspective and information regarding the current situation is provided. This is accomplished through reviewing the existing literature and synthesizing secondary data from various sources. The chapter concludes that addressing the political and economic foundations that form the environment, which enables and supports economic activity, including businesses, is critical.
Non-stationary analysis in electrical power systems by the dual tree wavelet transform

The power theories, which are defined in frequency domain, used the Fourier transform. This means that they are capable to analyze only a periodic stationary processes. Physical phenomena in power systems are in general nonstationary and therefore a change of basis from global to local, is mandatory. The dual tree wavelet transform propose the opportunity for a new power definitions and time-frequency analysis.
Automatic Detection of Hypoglycemic Events From the Electronic Health Record Notes of Diabetes Patients: Empirical Study (Preprint)

<sec> <title>BACKGROUND</title> Hypoglycemic events are common and potentially dangerous conditions among patients being treated for diabetes. Automatic detection of such events could improve patient care and is valuable in population studies. Electronic health records (EHRs) are valuable resources for the detection of such events. </sec> <sec> <title>OBJECTIVE</title> In this study, we aim to develop a deep-learning–based natural language processing (NLP) system to automatically detect hypoglycemic events from EHR notes. Our model is called the High-Performing System for Automatically Detecting Hypoglycemic Events (HYPE). </sec> <sec> <title>METHODS</title> Domain experts reviewed 500 EHR notes of diabetes patients to determine whether each sentence contained a hypoglycemic event or not. We used this annotated corpus to train and evaluate HYPE, the high-performance NLP system for hypoglycemia detection. We built and evaluated both a classical machine learning model (ie, support vector machines [SVMs]) and state-of-the-art neural network models. </sec> <sec> <title>RESULTS</title> We found that neural network models outperformed the SVM model. The convolutional neural network (CNN) model yielded the highest performance in a 10-fold cross-validation setting: mean precision=0.96 (SD 0.03), mean recall=0.86 (SD 0.03), and mean F1=0.91 (SD 0.03). </sec> <sec> <title>CONCLUSIONS</title> Despite the challenges posed by small and highly imbalanced data, our CNN-based HYPE system still achieved a high performance for hypoglycemia detection. HYPE can be used for EHR-based hypoglycemia surveillance and population studies in diabetes patients. </sec>
Analytical Approaches to Electroacoustic Music Improvisation

This article introduces a framework for musical analysis applied to electroacoustic improvisation. After an introduction about the benefits of interdisciplinary research and research-creation practice there is a description of the different domains of the framework: acoustic analysis, music analysis itself and the design of graphic representations. The framework is based on the extraction of information from the audio signal (music information retrieval) to create data representations, some of which are very efficient for music analysis, such as the brightness standard deviation or the self-similarity matrix. The last part of the article demonstrates the use of the framework on extracts of electroacoustic improvisations performed by the Paris-based group Les Phonogénistes, of which the author is a member.