Reviewerly/exHarmony huggingface dataset:
TransMVU: Multi‐view 2D U‐Nets with transformer for brain tumour segmentation

Abstract Medical image segmentation remains particularly challenging for complex and low‐contrast anatomical structures, especially in brain MRI glioma segmentation. Gliomas appear with extensive heterogeneity in appearance and location on brain MR images, making robust tumour segmentation extremely challenging and leads to highly variable even in manual segmentation. U‐Net has become the de facto standard in medical image segmentation tasks with great success. Previous researches have proposed various U‐Net‐based 2D Convolutional Neural Networks (2D‐CNN) and their 3D variants, called 3D‐CNN‐based architectures, for capturing contextual information. However, U‐Net often has limitations in explicitly modelling long‐term dependencies due to the inherent locality of convolution operations. Inspired by the recent success of natural language processing transformers in long‐range sequence learning, a multi‐view 2D U‐Nets with transformer (TransMVU) method is proposed, which combines the advantages of transformer and 2D U‐Net. On the one hand, the transformer encodes the tokenized image patches in the CNN feature map into an input sequence for extracting global context for global feature modelling. On the other hand, multi‐view 2D U‐Nets can provide accurate segmentation with fewer parameters than 3D networks. Experimental results on the BraTS20 dataset demonstrate that our model outperforms state‐of‐the‐art 2D models and classic 3D model.
Low-cost system for analysis pedestrian flow from an aerial view using Near-Infrared, Microwave, and Temperature sensors

The use of IoT systems that support the construction of smart cities is a global trend that directly affects the quality of life of citizens. for vehicular and pedestrian traffic, the detection of living beings and especially of humans, is a way of quantifying different variables pertinent to the improvement of roads, traffic flows, frequency of visits, among others. the implementation of low-cost systems that do not involve high-processing systems makes the solutions more scalable at a global level. The data acquired by this type of device offers advantages to the different entities in statistics and public consultations, thus contributing to their growth. In this article, an assistance system for the task of pedestrian flow detection is designed and constructed. It integrates strategically located arrays of sensors to detect the direction and general location, which include microwave sensors to detect motion, and infrared presence sensors. The results demonstrate that the system manages to establish the direction of flow of the individual and laterally of the displacement and differentiation between humans and objects for assistance to other systems of counting or analysis of pedestrian flow.
A convolutional autoencoder model with weighted multi-scale attention modules for 3D skeleton-based action recognition

The 3D skeleton sequences of action can be recognized based on series of meaningful movements including changes in the direction and geometry features of the body pose. In this paper, we introduce the 3DPo-CDP descriptor, which incorporates the change direction patterns of body joints and pose features in a unified deep structure to learn more adequate features for the action recognition problem. To this end, two types of features are extracted. First, Change Direction Patterns (CDPs) are extracted by following the important points of motion trajectories where a significant change of direction has occurred using two filtering phases. The CDPs capture the global features which are invariant to noise and insignificant temporal dynamics of joints. Second, Pose Features are employed to learn the intrinsic connectivity relationships of adjacent limbs and the variance distances of body joints from representative joints to concentrate on key-frames and informative joints. The complementary features of CDPs and 3D pose, which are transformed into images, are combined in a unified representation and fed into a new convolutional autoencoder. Unlike conventional convolutional autoencoders that focus on frames, high-level discriminative features of spatiotemporal relationships of whole body joints are extracted by introducing weighted multi-scale channel and spatial attention modules. In this paper, we show that adjacent and non-adjacent neighbors can be effectively used to compute different weights for extracting cross-interaction channels and multi-scale spatial relationships of the current pixel. The extracted features are combined with the wavelet representation of statistical body information and then classified with a multi-class SVM classifier. The experimental results demonstrate the effectiveness of the augmented 3DPo-CDP descriptor using an attentional convolution autoencoder structure on five challenging 3D action recognition datasets.
RepSum: A general abstractive summarization framework with dynamic word embedding representation correction

Abstractive summarization is flexible and allows the model to generate new words and phrases. However, the familiar words are more likely to be selected as abstract candidate words in the process of abstractive summarization, causing the generated abstract to diverge from the reference. In our consideration, this is caused by representation degeneration of the pre-trained word embedding. Therefore, this paper proposes a general abstractive summarization framework with dynamic word embedding representation correction (RepSum). The representation correction algorithm identifies the dimension most relevant to word frequency and eliminates the word frequency features. Then the distribution of word embeddings will be more even. As a result, the words will be selected as candidate words without frequency bias to improve the quality of the abstract. The experimental results illustrate that RepSum performs better than the benchmark model in summary quality, demonstrating our method's effectiveness.
Post-trained convolution networks for single image super-resolution

A new method is proposed to increase the accuracy of the state-of-the-art single image super-resolution (SISR) using novel training procedure. The proposed method, named post-trained convolutional neural network (CNN), is carried out stochastic dual simplex algorithm (SDSA) in the last reconstruction layer. The method utilizes contextual information to update the last reconstruction layer of CNN. The extracted contextual information is projected to the last reconstructed layer by optimized weights and the bias is managed through SDSA. Post-trained CNN is applied to the very deep super-resolution (VDSR) method to show its performance. The quantitative and visual results demonstrate that the proposed post-trained VDSR (PTVDSR) exhibits excellent and competitive performance when compared with the VDSR and other super-resolution methods.
The 2022 Ranking List of Citation Analysis Researchers using h-Index

The impact factor of scientific journals and the h-index depend on the citations.Therefore, the citation analysis is a very important part of scientometrics.The paper presents the 2022 ranking list of citation analysis researchers.The ranking is presented primarily according to the h-index of researchers.The advantage the h-index is that it combines both the quantitynumber of articles and qualitycitations to these articles.A researcher cannot have a high h-index without publishing a considerable number of articles.The h-index favors researchers that publish a continuous stream of articles.Researchers with the same h-index are ranked by the number of citations.The minimum h-index of the 10 ranked researchers is 26.h-index can be determined from the following online databases: Web of Science, Scopus, Google Scholar and the Publish or Perish program.The ranking is edited using the Google Scholar database.
Cyber Security Awareness (CSA) and Cyber Crime in Bangladesh: A Statistical Modeling Approach

The need to combat cybercrime is becoming more and more urgent. This effect is crucial for developing nations like Bangladesh, which is currently building out its infrastructure in preparation for fully secure digitization. This study aims to identify the numerous factors that contribute to cybercrime, its challenges, the relationships between different cybersecurity variables, potential solutions to these issues, and various behavioral viewpoints individuals and organizations hold regarding cybercrime victimization. A simple random sampling method has been conducted to collect 200 data from individuals on this topic. Factor analysis based on Principal Component Analysis (PCA) was fitted to the data to analyze cyber behavior, Binary Logistic Regression was fitted to analyze cyber victimization status and the Poisson Regression model was fitted to analyze victimization frequency. The research demonstrates that the dependent variable cybercrime victimization is strongly associated with the independent variables which are password sharing status, using a common password, cyber security knowledge Status, personal information online storage status, downloading free antivirus from an unknown source, disabling antivirus for downloading, download digital media from an unknown source, clicking links unauthorized sites, personal info Sharing with a stranger over online. According to the regression model's findings, women are more likely than men to experience cybercrime. Cybersecurity knowledge is found to be a key factor in preventing cyberattacks. Additional research on this subject can be conducted utilizing large-scale data to gain more trustworthy conclusions on the underlying factors contributing to cybercrime victimization. Overall, developing a digital Bangladesh where our cyber security is robust can be accomplished by learning about cybersecurity and practicing safe online behavior.
A lightweight image splicing tampering localization method based on MobileNetV2 and SRM

Abstract The architectures of many state‐of‐the‐art local tempering detection models are complexity, and the training process of those models is also time‐consuming. Therefore, this paper constructs a lightweight local tampering detection method based on the convolutional network MobileNetV2 and a dual‐stream network. Specifically, the algorithm first improves the MobileNetv2, which not only reduces the multiple of its downsampling operator to retain richer traces of image tampering, but also introduces the dilated convolution in it to expand the receptive field of feature maps. The dual‐stream network uses RGB stream to extract image tampering features such as strong contrast difference and unnatural tampered boundaries, and implements spatial rich model (SRM) stream to extract image tampered area and noise features of real area. Finally, the features extracted from two streams are fused through an improved attention mechanism called parallel convolutional block attention module (CBAM), which can improve the sensitivity of the model to important features in RGB and SRM. The experimental results show that the proposed algorithm still has higher positioning accuracy than some existing algorithms, while achieving lightweight.
Domain adaptation in segmenting historical maps: A weakly supervised approach through spatial co-occurrence

Historical maps depict past states of the Earth’s surface and make it possible to trace the natural or anthropogenic evolution of geographic objects back through time. However, the state of the depicted reality is not the only source of change: maps of varying age can differ in terms of graphical design, and also in terms of storage conditions, physical ageing of pigments, and the scanning process for digitization. Consequently, a computer vision system learned from a specific (source) map series will often not generalize well to older or newer (target) maps, calling for domain adaptation. In the present paper we examine – to our knowledge for the first time – domain adaptation for segmenting historical maps. We argue that for geo-spatial data like maps, which are geo-localized by definition, the spatial co-occurrence of geographical objects provides a supervision signal for domain adaptation. Since only a subset of all mapped objects co-occur, and even those are not perfectly aligned due to both real topographic changes and variations in map generalization/production, they only provide weak supervision — still they can bring a substantial benefit over completely unsupervised domain adaptation methods. The core of our proposed method is a novel self-supervised co-occurrence network that detects co-occurring objects across maps (specifically, domains) with a novel loss function that allows for object changes and spatial misalignment. Experiments show that, for the task of segmenting hydrological objects such as rivers, lakes and wetlands, our system significantly outperforms two state-of-art baselines, even with limited supervision (e.g., 5%). The source code is publicly available at https://github.com/sian-wusidi/spatialcooccurrence.
A Quality, Size and Time Assessment of the Binarization of Documents Photographed by Smartphones

Smartphones with an in-built camera are omnipresent today in the life of over eighty percent of the world's population. They are very often used to photograph documents. Document binarization is a key process in many document processing platforms. This paper assesses the quality, file size and time performance of sixty-eight binarization algorithms using five different versions of the input images. The evaluation dataset is composed of deskjet, laser and offset printed documents, photographed using six widely-used mobile devices with the strobe flash off and on, under two different angles and four shots with small variations in the position. Besides that, this paper also pinpoints the algorithms per device that may provide the best visual quality-time, document transcription accuracy-time, and size-time trade-offs. Furthermore, an indication is also given on the "overall winner" that would be the algorithm of choice if one has to use one algorithm for a smartphone-embedded application.
IFRS adoption and foreign direct investment in Sub-Saharan African countries: Does the levels of Adoption Matter?

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The History, Current Applications and Future of Integrated Circuit

Integrated circuit is a fundamental part of modern electronics, so it can be discovered in almost every electronic product. Therefore, a summary of the development of integrated circuit is very important for the professionals. The history, current applications, and challenges of integrated circuit will be discussed. In order to include all the important events of integrated circuit, in the history section, the discovery of characteristics of semiconductors, the invention of the first transistor, the first bipolar junction transistor (BJT), the first field effect transistor (FET), the first integrated circuit, and the first microprocessor will be demonstrated. Next section is about current applications by functions, including analog, digital and mixed signal IC. Finally, challenges that IC is facing right now, and prospects will be discussed. Understanding the history and current applications will help people to predict the future and find solutions of challenges.
Examining the motivations of sharing political deepfake videos: the role of political brand hate and moral consciousness

Purpose Deepfakes are fabricated content created by replacing an original image or video with someone else. Deepfakes have recently become commonplace in politics, posing serious challenges to democratic integrity. The advancement of AI-enabled technology and machine learning has made creating synthetic videos relatively easy. This study explores the role of political brand hate and individual moral consciousness in influencing electorates' intention to share political deepfake content. Design/methodology/approach The study creates and uses a fictional deepfake video to test the proposed model. Data are collected from N = 310 respondents in India and tested using partial least square–structural equation modelling (PLS-SEM) with SmartPLS v3. Findings The findings support that ideological incompatibility with the political party leads to political brand hate, positively affecting the electorates' intention to share political deepfake videos. This effect is partially mediated by users' reduced intention to verify political deepfake videos. In addition, it is observed that individual moral consciousness positively moderates the effect of political brand hate on the intention to share political deepfake videos. Intention to share political deepfake videos thus becomes a motive to seek revenge on the hated party, an expression of an individual's ideological hate and a means to preserve one's moral self-concept and strengthen their ideologies and moral beliefs. Originality/value The study expands the growing discussion about disseminating political deepfake videos using the theoretical lens of the negative consumer-brand relationship. It validates the effect of political brand hate on irrational behavior that is intended to cause harm to the hated party. Further, it provides a novel perspective that individual moral consciousness may fuel the haters' desire to engage in anti-branding behavior. Political ideological incompatibility reflects ethical reasons for brand hate. Therefore, hate among individuals with high moral consciousness serves to preserve their moral self.
The Use of Machine Learning in Digital Forensics: Review Paper

With the increase of cybercrimes in the current years, digital forensics has become an important matter to study in order achieve quality evidence.Forensic investigators face difficulties with data collection and analysis to reconstruct events.Due to humans' immense interaction on a daily basis, machine learning allows investigators to perform more effective and efficient investigations using various algorithms.Machine learning is a subset of the artificial intelligence field.It is a scientific discipline focusing on developing computer models and algorithms that can perform specific tasks without programming, such as dataset training and testing, and it's potential to aid in investigations.This paper reviews various machine learning techniques that examine and analyze digital evidence during the investigation process.Each machine learning algorithm works on a specific area of digital forensics based on the features, it overcomes complexity, data volume, time-lining, correlation, consistency, etc. moreover, this study compares machine learning algorithms in terms of standard criteria.
An Improved Lightweight Network Based on YOLOv5s for Object Detection in Autonomous Driving

Object detection with high accuracy and fast inference speed based on camera sensors is important for autonomous driving. This paper develops a lightweight object detection network based on YOLOv5s which is one of the most promising object detection networks in the current literature. Our proposed network not only strengthens the object positioning ability in Cartesian coordinates without using space transformation, but also reinforces the reuse of feature maps from different scales of receptive fields. To show its robustness, different driving datasets are used to evaluate the effectiveness of our proposed network. The detection performances on objects with different sizes and in different weather conditions are also examined to show the generalizability of our proposed network. The results show that our proposed network has superior object detection performances in the conducted experiments with a very high running speed (i.e., 75.2 frames per second). This implies that our proposed network can serve as an effective and efficient solution to real-time object detection in autonomous driving.
Unraveling a New Age of Travel in Blockchain Based Metaverse

Recently, there is an upsurge in tourism and hospitality sector using the VR technology with major players like Airbnb and Hilton using it as an instrument of advertisement. Blockchain based networks have opened newer avenues for electronic business and authentication and identity modeling. The article attempts to elucidate how blockchain and metaverse will transform the travel and tourism—and enlist the facilitators and inhibitors of this paradigm change. The article will also shed light on supporting technologies in metaverse and how it will extend the space of virtual tourism.
Randomized Active Learning to Identify Phishing URL

Data analytics is rapidly being employed in cybersecurity concerns, and has been found to be beneficial in situations where large amounts of data and heterogeneity make human assessment by security specialists difficult. Obtaining data with annotations is a tough and well-known restrictive constraint for various supervised security analytics tasks in real-world cyber-security situations using data-driven analytics. Because annotation is largely manual and involves a great deal of expert effort, vast sections of large datasets are frequently left unlabeled. We adopt a randomly ranked feature active learning strategy to create a semi-supervised solution in this research to address this constraint in an applied cyber-security challenge of phishing classification. An early classifier is trained on a slight sample of interpreted data, and then iteratively updated by selecting just relevant samples from a huge pool of unlabeled data that are most likely to effect classifier presentation quickly. Randomly ranked feature Active Learning has a lot of potential in terms of achieving quicker convergence in relationships of classification presentation in a group learning environment, needing even less human annotation labor. Without requiring a significant number of marked training examples to be accessible during training, a helpful feature rank update strategy paired with active learning displays good classification results for labeling phishing/malicious URLs.
Factors Influencing Security Issues in Cloud Computing

Computing is a new computing paradigm that enables firms to do this without making a huge initial investment. Despite the potential advantages of cloud technology, modeling security remains a worry, limiting cloud model adoption. Additional factors such as prototype model, multi-tenancy, adaptability, and the tiered dependency stacking have been introduced to the problem scope beneath the cloud model, increasing the complexity of the security threat. In this work, we examine the cloud security challenge in depth. We investigated the issue from the standpoint of a cloud system, cloud service delivery models, computing stakeholders, and cloud stakeholders.
Real-Time 3D Reconstruction for Mixed Reality Telepresence Using Multiple Depth Sensors

In recent years, there has been a great development in real-time three-dimensional (3D) scene reconstruction from depth sensor data, as well as the study of such data in Virtual Reality (VR) and Augmented Reality (AR) contexts. Although it has been extensively investigated and has attracted the attention of many researchers, the challenge of real-time 3D reconstruction remains a difficult research task. The majority of current techniques, target real-time 3D reconstruction for the single-view-based system rather than multi-view. In order to provide multi-view 3D reconstruction for Mixed Reality (MR) telepresence, this chapter aims to propose a multiple depth sensor capture using a marching square approach to produce a single full 3D reconstruction surface of a moving user in real-time. The chapter explains the design stage that involves setup from multiple depth sensors, surface reconstruction and merging of 3D reconstruction data for MR Telepresence. The chapter ends with results and a conclusion.