Reviewerly/exHarmony huggingface dataset:
Image Hallucination From Attribute Pairs

Recent image-generation methods have demonstrated that realistic images can be produced from captions. Despite the promising results achieved, existing caption-based generation methods confront a dilemma. On the one hand, the image generator should be provided with sufficient details for realistic hallucination, meaning that longer sentences with rich content are preferred, but on the other hand, the generator is meanwhile fragile to long sentences due to their complex semantics and syntax like long-range dependencies and the combinatorial explosion of object visual features. Toward alleviating this dilemma, a novel approach is proposed in this article to hallucinate images from attribute pairs, which can be extracted from natural language processing (NLP) toolsets in the presence of complex semantics and syntax. Attribute pairs, therefore, enable our image generator to tackle long sentences handily and alleviate the combinatorial explosion, and at the same time, allow us to enlarge the training dataset and to produce hallucinations from randomly combined attribute pairs at ease. Experiments on widely used datasets demonstrate that the proposed approach yields results superior to the state of the art.
Overprecision is a property of thinking systems.

Overprecision is the excessive certainty in the accuracy of one's judgment. This article proposes a new theory to explain it. The theory holds that overprecision in judgment results from neglect of all the ways in which one could be wrong. When there are many ways to be wrong, it can be difficult to consider them all. Overprecision is the result of being wrong and not knowing it. This explanation can account for why question formats have such a dramatic influence on the degree of overprecision people report. It also explains the ubiquity of overprecision not only among people but also among artificially intelligent agents. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
TPR, PPV and ROC based Performance Measurement and Optimization of Human Face Recognition of IoT Enabled Physical Location Monitoring

This paper describes the construction of Internet of Things (IoT) enabled system which not only captures the sensors data in textual and numeric form but also performs live human face recognition to monitor physical location effectively. The dataset used in order to apply supervised machine learning algorithms is the combination of automatically captured live sensor data along with name of the human face recognized or unknown and additional manually introduced class label. Performance measurement of face recognition is done with the help of Decision Tree (DT), K-Nearest Neighbors (KNN), Naïve Bayes (NB) and Logistic Regression (LR). The results show that DT gives the best performance with respect to classifier’s accuracy; True Positive Rate, Positive Predictive Value and area under curve of Receiver Operating Characteristics (ROC) for face recognition prediction whether the recognized face is true or false.
An improved model of Agile Kanban method: verification process through experts' review

This paper presents the verification results of the proposed model for improving the software project-monitoring task of the Agile Kanban method (i-KAM). However, i-KAM is still in its initial phases and requires verification regarding the proposed components and associated criteria. Expert review method was used to collect valid and reliable data from experienced persons who have skills and experiences in Agile software development. There were 11 experts participated and their opinions were obtained and quantified by using descriptive analysis. Results confirm that i-KAM is a well-accepted model as it can improve the progress monitoring task in terms of extending its tracking mechanism, controlling the WIP limits, and providing useful insights on the project status. Thereafter, i-KAM was revised according to the experts' suggestions to enhance its feasibility and readability of elements. The implementation of i-KAM would help software house in delivering software projects within prescribe cost and time.
Desktop VR as a Virtual Workspace: a Cognitive Aspect

This paper explores the benefits of using a desktop VR as a virtual workspace.Forty-nine participants data included in this study.With a between-subjects design, we compared the use of extra information between a desktop VR (23 people) and a web browser (26 people).Their tasks were to solve numerical tasks and write the results in a separate spreadsheet.They could follow their performance (solved task / all tasks) on a graph.Then, they filled out a questionnaire where they had to estimate their performance, and indicate the source of this estimation (the only valid source was the provided graph).In the subsample of those who used the graph, the members of the VR group estimated significantly more accurately their performance than the members web browser group.Therefore, the 3D desktop VR workspace can provide benefits to its users by displaying extra information permanently.
Type of Supervised Text Classification System for Unstructured Text Comments using Probability Theory Technique

In this paper, an improved sentimental text analysis system called Probability Based Text Classifier (PBTC) is presented. It aims to train the existing unstructured text command set and to classify the sampled text command belongs into positive or negative polarity based on probability theory and supervised concepts. It consists of three stages pre-processed, training and classification. In the first stage, the proposed (PBTC) system identifies the relevant and irrelevant words in the unstructured text command set based on pre-determined text pattern model. In the second stage it identifies two dissimilar classes over the preprocessed text command set based on predetermined text pattern model and simple probability theory concepts. Next stage, the PBTC identifies the sample test text command without class label belong on which class based on Naive Bayer scheme and trained existing text command set. Experimental result shows that the proposed (PBTC) system is well suitable to train the unstructured text command set and classify the new text command belongs into positive or negative polarity with higher accuracy
Algebraic Construction of Powerful Substitution Box

The development of wireless transmission, day by day contacts the new statures of innovation. Cryptography is one of the procedures used to give security to information streaming over the system by encryption and decryption. Substitution box (S-box) is a one of a kind nonlinear activity in Advanced Encryption Standard (AES), this paper proposed a new algebraic approach to build the multifaceted nature of S-box by changing the affine transformation. This builds the affine change period to end up 102 and expands the security of the S-box against algebraic attacks and interpolation attacks. Further examination has uncovered that the Operational multifaceted nature of the power S-box is higher than the essential S-box. the proposed powerful S-box satisfies the ideal property of Avalanche's impact and has more prominent security towards linear and differential cryptanalysis. New S-box acquires all focal points and efficiency of any current advanced usage of AES S-box
Innovation and growth ambition of female entrepreneurs: a comparison between the MENA region and the rest of the world

The aim of the current paper was to compare the regional differences in innovation and the resulting growth expectation among female entrepreneurs in the MENA region as compared with the rest of the world.Data on innovation and growth aspirations of 162,752 female entrepreneurs in 109 countries was extracted from GEM annual surveys in 2002-2016.A hierarchical linear modelling technique was used to analyse the data.Interestingly, results indicated that female entrepreneurs in the MENA region are more innovative than their peers in the rest of the world; however, there is no difference between the MENA female entrepreneurs and their counterparts in the rest of the world in terms of growth ambitions and benefiting from innovation for growth.Innovation was found to be positively related to shaping growth ambitions in female entrepreneurs.
Secure RGB image steganography based on modified LSB substitution

Different steganography techniques have been presented based on RGB image as the image is considered secure cover for hidden data. Specifically, this paper presents edge-based image steganographic method using parameterised Canny edge detector that relies on embedding the secret message bits into variable LSB length of the blue colour channel of the cover image. The blue colour channel is selected because the steganography-based research showed that the visual perception of blue colour intensity is less distinct than the red and green colours. Secret message bits are embedded up to four bits of LSB which are selected by a random number generator. Additionally, as the less significant information is carried out by the LSB of each pixel so slight changes of LSB will not affect the visual quality of the cover image. The proposed algorithm was tested on a set of RGB colour images, and satisfactory results were demonstrated regarding minimum distortion in the blue colour of a pixel and visually identical original and stego image.
Current Approaches and Applications in Natural Language Processing

Artificial Intelligence has gained a lot of popularity in recent years thanks to the advent of, mainly, Deep Learning techniques [...]
Ghost shuffle lightweight pose network with effective feature representation and learning for human pose estimation

Despite their success, existing human pose estimation approaches mostly have complex architectures, high cost, and lack of lightweight modules. To address this problem, this paper proposes a Ghost Shuffle Lightweight Pose Network (GSLPN) with a more lightweight and efficient network architecture than the popular Lightweight Pose Network. First, in order to condense the scale of the network while maintaining its performance, we stack two lightweight modules, depthwise convolution and the Ghost module, to build our initial prototype bottleneck. Then, we impose a channel shuffle operation on the prototype bottleneck to shuffle the sequence of the feature maps for constructing Ghost Shuffle Bottleneck (Ghost Shuffle Bottleneck) with effective feature representation so as to develop a GSLPN. Second, a lightweight, efficient parallel attention mechanism, Lightweight Pose Parallel Attention, is proposed to improve keypoint locating accuracy. An experiment validating the proposed method showed that GSLPN achieved competitive performance with a smaller model size and less computational complexity than state-of-the-art methods, indicating that the GSLPN is a superior approach for human pose estimation.
Surface Environmental Evolution Monitoring in Coal Mining Subsidence Area Based on Multi-Source Remote Sensing Data

The exploitation of mineral resources is crucial for cost-effective construction but has also led to severe damage to the ecological environment in mining areas. Therefore, it is particularly important to effectively monitor surface environmental problems in the mining subsidence area caused by the exploitation of mineral resources. Herein, the Huinong coal mining subsidence area, Shizuishan city, Ningxia, China, was taken as the study area. The remote sensing image features of various environmental elements were investigated through field investigations, the measured spectra, and image spectrum contrast analysis. On this basis, an object-oriented random forest classification method was used to classify images from different time phases and sources in coal mining subsidence areas. Next, the man–machine interactive interpretation was confirmed by referring to the pre-classification results. By overlaying the interpretation result map and analyzing the land-use class changes, the spatial–temporal evolution monitoring of the surface environment in the coal mining subsidence area from 1979 to 2018 was carried out. The results show that the surface environment in the coal mining subsidence area has undergone significant changes over the past 40 years, among which—from 1979 to 2003—the environment of the coal mining area was severely damaged by the intensive mining activities. The area of cultivated land and vegetation coverage decreased sharply, while the area of other land-use classes, such as coal heaps, water bodies, and coal gangue, exhibited a trend of rapid growth. From 2003 to 2018, after more than 10 years of mine geological environment renovation and management, the surface environment of the coal mining subsidence area greatly improved, among which the vegetation coverage has shown the fastest growth rate, while the area of coal gangue, badlands, and other land-use classes have significantly reduced. The hidden dangers of geological disasters have been drastically mitigated. In addition, the residential area continued to decrease in the early stages and then rebounded to a certain extent, indicating that urbanization was carried out at the same time as the ecological environment began to improve. The surface environment before and after the renovation is consistent with the results from remote sensing monitoring.
SELC: Self-Ensemble Label Correction Improves Learning with Noisy Labels

Deep neural networks are prone to overfitting noisy labels, resulting in poor generalization performance. To overcome this problem, we present a simple and effective method self-ensemble label correction (SELC) to progressively correct noisy labels and refine the model. We look deeper into the memorization behavior in training with noisy labels and observe that the network outputs are reliable in the early stage. To retain this reliable knowledge, SELC uses ensemble predictions formed by an exponential moving average of network outputs to update the original noisy labels. We show that training with SELC refines the model by gradually reducing supervision from noisy labels and increasing supervision from ensemble predictions. Despite its simplicity, compared with many state-of-the-art methods, SELC obtains more promising and stable results in the presence of class-conditional, instance-dependent, and real-world label noise. The code is available at https://github.com/MacLLL/SELC.
Simple continuous-variable quantum key distribution scheme using a Sagnac-based Gaussian modulator

Continuous-variable quantum key distribution (CV-QKD) is a protocol that uses quantum mechanics to ensure that the distribution of an encryption key is secure even in the presence of eavesdroppers. The wide application of CV-QKD requires low cost, system simplicity, and system stability. However, owing to the particularity of Gaussian modulation in CV-QKD, an amplitude modulator (AM) and a bias controller are required, making the system structure complex and unstable. In this Letter, we achieve two-dimensional Gaussian modulation with only one phase modulator (PM) and a Sagnac ring structure, which significantly reduces the complexity of the system. We test the Gaussian modulation stability for 10 h, and the result shows that the expected secure key rate can be maintained at 80 kbit/s under a transmission distance of 50 km. This scheme opens up new, to the best of our knowledge, possibilities for a new generation of highly stable and simple CV-QKD systems.
Dynamic Support Network for Few-shot Class Incremental Learning

Few-shot class-incremental learning (FSCIL) has two main problems: (1) catastrophically forgetting old classes while feature representations drift into new classes, and (2) over-fitting new classes for the few training samples available. Revealed by our analyses, the problems are caused by feature distribution crumbling, which causes class confusion when continuously embedding few samples to a fixed feature space. In this study, we propose a Dynamic Support Network (DSN), which refers to an adaptively updating network with compressive node expansion to ‘support’ the feature space. In each training session, DSN tentatively expands network nodes to enlarge feature representation capacity for incremental classes. It then dynamically compresses the expanded network by node self-activation to pursue compact feature representation which alleviates over-fitting. Simultaneously, DSN selectively recalls old class distributions during incremental learning process to support feature distributions and avoid confusion between classes. DSN with compressive node expansion and class distribution recalling provides a systematic solution for the problems of catastrophically forgetting and overfitting. Experiments on CUB, CIFAR-100, and miniImage datasets show that DSN significantly improves upon the baseline approach, achieving new state-of-the-arts. The code is publicly available.
Intelligent Computer-Aided Model for Efficient Diagnosis of Digital Breast Tomosynthesis 3D Imaging Using Deep Learning

Digital breast tomosynthesis (DBT) is a highly promising 3D imaging modality for breast diagnosis. Tissue overlapping is a challenge with traditional 2D mammograms; however, since digital breast tomosynthesis can obtain three-dimensional images, tissue overlapping is reduced, making it easier for radiologists to detect abnormalities and resulting in improved and more accurate diagnosis. In this study, a new computer-aided multi-class diagnosis system is proposed that integrates DBT augmentation and colour feature map with a modified deep learning architecture (Mod_AlexNet). To the proposed modified deep learning architecture (Mod AlexNet), an optimization layer with multiple high performing optimizers is incorporated so that it can be evaluated and optimised using various optimization techniques. Two experimental scenarios are applied, the first scenario proposed a computer-aided diagnosis (CAD) model that integrated DBT augmentation, image enhancement techniques and colour feature mapping with six deep learning models for feature extraction, including ResNet-18, AlexNet, GoogleNet, MobileNetV2, VGG-16 and DenseNet-201, to efficiently classify DBT slices. The second scenario compared the performance of the newly proposed Mod_AlexNet architecture and traditional AlexNet, using several optimization techniques and different evaluation performance metrics were computed. The optimization techniques included adaptive moment estimation (Adam), root mean squared propagation (RMSProp), and stochastic gradient descent with momentum (SGDM), for different batch sizes, including 32, 64 and 512. Experiments have been conducted on a large benchmark dataset of breast tomography scans. The performance of the first scenario was compared in terms of accuracy, precision, sensitivity, specificity, runtime, and f1-score. While in the second scenario, performance was compared in terms of training accuracy, training loss, and test accuracy. In the first scenario, results demonstrated that AlexNet reported improvement rates of 1.69%, 5.13%, 6.13%, 4.79% and 1.6%, compared to ResNet-18, MobileNetV2, GoogleNet, DenseNet-201 and VGG16, respectively. Experimental analysis with different optimization techniques and batch sizes demonstrated that the proposed Mod_AlexNet architecture outperformed AlexNet in terms of test accuracy with improvement rates of 3.23%, 1.79% and 1.34% when compared using SGDM, Adam, and RMSProp optimizers, respectively.
Extracting entity relations for “problem-solving” knowledge graph of scientific domains using word analogy

Purpose Problem-solving” is the most crucial key insight of scientific research. This study focuses on constructing the “problem-solving” knowledge graph of scientific domains by extracting four entity relation types: problem-solving, problem hierarchy, solution hierarchy and association. Design/methodology/approach This paper presents a low-cost method for identifying these relationships in scientific papers based on word analogy. The problem-solving and hierarchical relations are represented as offset vectors of the head and tail entities and then classified by referencing a small set of predefined entity relations. Findings This paper presents an experiment with artificial intelligence papers from the Web of Science and achieved good performance. The F1 scores of entity relation types problem hierarchy, problem-solving and solution hierarchy, which were 0.823, 0.815 and 0.748, respectively. This paper used computer vision as an example to demonstrate the application of the extracted relations in constructing domain knowledge graphs and revealing historical research trends. Originality/value This paper uses an approach that is highly efficient and has a good generalization ability. Instead of relying on a large-scale manually annotated corpus, it only requires a small set of entity relations that can be easily extracted from external knowledge resources.
Risks and Regulation of Cryptocurrency during Pandemic: A Systematic Literature Review

Cryptocurrencies differ from traditional financial assets as they are not governed by any higher authority, have no physical representation, are indefinitely divisible, and are not based on any tangible assets or country. While their popularity and use have surged over the years, they are still subject to an underlying risk. The purpose of this research is to investigate the regulatory approach for cryptocurrencies adopted around the world. To achieve the purpose of this research, extant literature is examined using a systematic literature review. Using a total of 49 Scopus indexed shortlisted articles, the extant literature on the various risks related to cryptocurrency and the regulatory approach adopted for the same was explored. The prior literature was classified into four thematic clusters of the regulatory approach to risks: pandemic, volatility, money laundering and cyber security. The findings suggest the regulations governing cryptocurrency are still at an infancy stage, and it still suffers from the challenge of limited transparency. The pandemic did not have a drastic impact on cryptocurrency. Cryptocurrencies are volatile in reaction to economic policy uncertainty and macroeconomic variables. To the best of the author’s knowledge, this review paper is one of the few contributing to the gaps in the literature on the various risks and their associated regulatory approach to managing cryptocurrency.
Exploiting Attention-Consistency Loss For Spatial-Temporal Stream Action Recognition

Currently, many action recognition methods mostly consider the information from spatial streams. We propose a new perspective inspired by the human visual system to combine both spatial and temporal streams to measure their attention consistency. Specifically, a branch-independent convolutional neural network (CNN) based algorithm is developed with a novel attention-consistency loss metric, enabling the temporal stream to concentrate on consistent discriminative regions with the spatial stream in the same period. The consistency loss is further combined with the cross-entropy loss to enhance the visual attention consistency. We evaluate the proposed method for action recognition on two benchmark datasets: Kinetics400 and UCF101. Despite its apparent simplicity, our proposed framework with the attention consistency achieves better performance than most of the two-stream networks, i.e., 75.7% top-1 accuracy on Kinetics400 and 95.7% on UCF101, while reducing 7.1% computational cost compared with our baseline. Particularly, our proposed method can attain remarkable improvements on complex action classes, showing that our proposed network can act as a potential benchmark to handle complicated scenarios in industry 4.0 applications.