Reviewerly/exHarmony huggingface dataset:
An unsupervised fusion network for boosting denoising performance

While many efforts have been devoted to addressing image denoising and achieve continuously improving results during the past few decades, it is fair to say that no a stand-alone method is consistently better than others. Nonetheless, many existing denoising methods, each having a different denoising capability, can yield various but complementary denoised images with respect to specific local areas. To effectively exploit the complementarity and diversity among the denoised images obtained with different denoisers, in this work we fuse them to produce an overall better result, which is fundamental to achieve robust and competitive denoising performance especially for complex scenes. A framework called deep fusion network (DFNet) is proposed to generate a consistent estimation about the final denoised image, taking advantage of the complementarity of denoisers and suppressing the bias. Specifically, given a noisy image, we first exploit a set of representative image denoisers to denoise it respectively, and obtain the corresponding initial denoised images. Then these initial denoised images are concatenated and fed into the proposed DFNet, and the proposed DFNet seeks to adjust its network parameters to produce the fused image (as the final denoised image) with an unsupervised training strategy through minimizing the carefully designed loss function. The experimental results show that our approach outperforms the stand-alone methods as well as the ones using combination strategy by large margin both in objective and subjective evaluations. Compared to the those methods that are relatively close to our strategy, the proposed DFNet is extensible and parameter free, which means it can cope with a variable number of different denoisers and avoid the manual intervention during the fusion process. The proposed DFNet has greater flexibility and better practicality.
Improving user experience of color palette extraction by using interactive visualization based on hierarchical color model

Extracting color palettes from an image is a common practice used by artists in different visual domains. In this study, we introduce a novel tool for extracting color palettes from an image. Based on the hierarchical color model (HCM) (Jeong et al. (2019)), we develop a prototype user interface system comprising novel interactions and visualization. Our visualization is a node-link diagram tailored for the HCM; the accompanying interactions originate from the hierarchical structure. We evaluate our prototype system by performing a usability test and comparing it with contemporary alternatives for professional usage. The results from the user study validate the effectiveness of the presented approach. We also find a few user requirements that can be useful in the further development of the related tools. Moreover, we expect that the proposed interactive visualization can facilitate additional studies on the HCM. The prototype is available in the following URL: https://int-vis-hcm.web.app.
ASCL: Adversarial supervised contrastive learning for defense against word substitution attacks

Attacks with adversarial examples can tremendously worsen the performance of deep neural networks (DNNs). Hence, defending against such adversarial attacks is crucial for nearly all DNN-based applications. Adversarial training is an effective and extensively adopted approach for increasing the robustness of DNNs in which benign examples and their adversarial counterparts are considered together in the training stage. However, this may result in a decrease in accuracy on benign examples because it does not account for the inter-class distance of benign examples. To overcome the aforementioned dilemma, we devise a novel defense approach named adversarial supervised contrastive learning (ASCL), which combines adversarial training with supervised contrastive learning to enhance the robustness of DNN-based models while maintaining their clean accuracy. We validate the effectiveness of the proposed ASCL approach in the scenario of defending against word substitution attacks by means of extensive experiments on benchmark tasks and datasets. The experimental results show that ASCL reduces the attack success rate to 20% while maintaining the accuracy for clean inputs within a 2% margin.
The End of Vagueness: Technological Epistemicism, Surveillance Capitalism, and Explainable Artificial Intelligence

Abstract Artificial Intelligence (AI) pervades humanity in 2022, and it is notoriously difficult to understand how certain aspects of it work. There is a movement— Explainable Artificial Intelligence (XAI)—to develop new methods for explaining the behaviours of AI systems. We aim to highlight one important philosophical significance of XAI—it has a role to play in the elimination of vagueness. To show this, consider that the use of AI in what has been labeled surveillance capitalism has resulted in humans quickly gaining the capability to identify and classify most of the occasions in which languages are used. We show that the knowability of this information is incompatible with what a certain theory of vagueness— epistemicism —says about vagueness. We argue that one way the epistemicist could respond to this threat is to claim that this process brought about the end of vagueness. However, we suggest an alternative interpretation, namely that epistemicism is false, but there is a weaker doctrine we dub technological epistemicism , which is the view that vagueness is due to ignorance of linguistic usage, but the ignorance can be overcome. The idea is that knowing more of the relevant data and how to process it enables us to know the semantic values of our words and sentences with higher confidence and precision. Finally, we argue that humans are probably not going to believe what future AI algorithms tell us about the sharp boundaries of our vague words unless the AI involved can be explained in terms understandable by humans. That is, if people are going to accept that AI can tell them about the sharp boundaries of the meanings of their words, then it is going to have to be XAI.
Improved deep generative adversarial network with illuminant invariant local binary pattern features for facial expression recognition

Facial Expression Recognition (FER) is one of the prevailing as well as demanding tasks in social communication. In general, face expressions are usual and straight ways for individuals to converse their intentions as well as emotions. This paper proposes the Taylor-Chicken Swarm Optimization-based Deep Generative Adversarial Network (Taylor-CSO-based Deep GAN) for FER. Here, the facial expressions are predicted through a series of steps, such as video frame extraction, pre-processing, face detection, feature extraction, as well as FER. The major contribution of the proposed work lies in the last step, where Taylor-CSO based Deep GAN is employed for recognizing facial expressions. Initially, video frames are extracted from the input video as well as pre-processing is done to extract the Region of Interest (RoI). Then, the Viola Jones algorithm is employed to detect the face images, and Illuminant Invariant Local Binary Pattern (IILBP) features are extracted. Finally, the FER is performed by the proposed model. The performance of the developed model is analyzed using the CK+ dataset, RAVDESS dataset, and SAVEE dataset. Also, the developed model's performance is evaluated with conventional FER methods. The performance analysis exhibits that the adopted model is precise and valuable.
Gender political inclusion and democracy in Africa: some empirical evidence

The objective of this study is to examine the effect of political inclusion on democracy in Africa. The results of the analyses through the OLS, system GMM, IV-Tobit and IV-2SLS show that gender political inclusion enhances democracy in Africa. This result is robust across alternative specifications of political inclusion and democracy. Besides, the results equally stood when controlled for colonisation and internal conflicts. In terms of policy implications, policymakers in Africa should enhance their fight for political inclusion as one of the gateways to promoting democracy. In this respect, national laws could be put in place, which impose gender quotas in political positions in every country. Equally, the African Union could sign a convention on these quotas for respective countries to ratify.
Impact of Agricultural Finance on Agricultural Output in Nigeria (1983 - 2018)

The study aims to examine the impact of agricultural finance on agricultural output in Nigeria using annual time series data from 1983 to 2018. The total output of the agricultural sector is the dependent variable while government Agricultural Credit Guarantee Scheme Fund is used as a proxy for government finance and credit from commercial banks to the agricultural sector as both independent variables. The methods of analysis are the Vector Autoregressive Model (VAR) for the estimation of the long run relationships and Granger Causality for the determination of causal relationships among variables. The findings of the study reveals that both Government Agricultural finance and financing from commercial banks have significant positive long run impact on Agricultural output and also the have significant causal effects on Agricultural output in Nigeria. Therefore, the study concludes that both independent variables are major determinants of Agricultural finance and recommends the increased financing of the agricultural sector as well as the direction of lower interest bearing loans to the agricultural sector to further improve the output of the sector in Nigeria.
An enhanced reversible image steganography using distributive Tian's method

Abstract In recent years, the concept of image encryption and data concealing has become a major area of research that significantly improves the secure communication of images with hidden data in them. The usage of the cloud has been outstanding in several aspects so as its security to ensure privacy over the data stored in it. The concept of reversible image steganography is one such stream where the cloud users are highly relying upon the privacy of the data, they store in the cloud servers. The reversible image steganography has the potential to recover the original image from the stego‐image once the embedded data is extracted. In this research work, an effective distributive Tian's method is proposed to embed the data onto an encrypted image without degrading the performance of the encrypted image. The cross‐verification of lossless image steganography can be identified when the original image is getting extracted from the encrypted image. The result of the proposed method ensures that the distributive Tian's method outperforms the recent state‐of‐art methods. The proposed method is deployed onto six different images in different aspects to ensure its significance.
The reality of remote extended reality research: Practical case studies and taxonomy

Remote user studies—those where the experimenter and participant are not physically located together—offer challenges and opportunities in HCI research in general, and extended reality (XR) research specifically. The COVID-19 pandemic has forced this form of research to overcome a long period of unprecedented circumstances. However, this experience has produced a lot of lessons learned that should be shared. We propose guidelines based on findings from a set of six remote virtual reality studies, by analyzing participants' and researchers' feedback. These studies ranged from one-session types to longitudinal ones and spanned a variety of subjects such as cybersickness, selection tasks, and visual search. In this paper, we offer a big-picture summary of how we conducted these studies, our research design considerations, our findings in these case studies, and what worked well and what did not in different scenarios. Additionally, we propose a taxonomy for devising such studies in a systematic and easy-to-follow manner. We argue that the XR community should move from theoretical proposals and thought pieces to testing and sharing practical data-informed proposals and guidelines.
Automatic software vulnerability classification by extracting vulnerability triggers

Abstract Vulnerability classification is a significant activity in software development and software maintenance. Natural Language Processing (NLP) techniques, which utilize the descriptions in public repositories, are widely used in automatic software vulnerability classification. However, vulnerability descriptions are ordinarily short and contain many technical terms, making them difficult for machines to automatically comprehend. In this paper, we present an approach based on vulnerability triggers to automatically classify vulnerabilities. First, we extract vulnerability triggers with Bert Question and Answer (Bert Q&amp;A). Then, we use Recurrent Convolutional Neural Networks for Text classification (TextRCNN) to classify vulnerabilities based on Common Weakness Enumeration (CWE). We statistically perform an analysis of vulnerability triggers and comprehensively evaluate the classification performance of our approach on a set of 4769 prelabeled vulnerability entries, as well as compare it with state‐of‐the‐art vulnerability classification approaches. Experiment results show that our approach can achieve a F1‐measure of 95% on extraction and 80.8% on classification.
The effect of chaotic mapping on naked mole-rat algorithm for energy efficient smart city wireless sensor network

Naked mole-rat algorithm (NMRA) is a recently introduced swarm intelligent algorithm based on the matting pattern of naked mole-rats. Though the algorithm is competitive but still shows poor exploration properties and slow convergence. In the present work, chaos theory has been introduced to enhance the exploration properties of NMRA. Apart from that, parametric adaptation has been added to analyse the effect of mating factor ( λ ) using various chaotic maps (such as chebyshev map, circle map, iterative map etc.) on NMRA. Based on these chaotic maps, ten variants of NMRA are proposed, and their performance is evaluated for CEC 2005 test suite subjected to different population and dimension sets. Further, the best variant among the proposed variants is evaluated for CEC 2014 benchmark functions, 100-digit challenge problems (CEC 2019) and three engineering design problems. In addition, to check the effectiveness of proposed best variant, it is further applied to real time problem of energy efficient and prolonged lifetime design of wireless sensor network for smart city framework. The results of best proposed variant are compared with other state-of-the-art algorithms namely differential evolution with adaptive properties (JADE), whale optimization algorithm with opposition and exponential properties (OEWOA), sine cosine crow search algorithm (SCCSA), self-adaptive differential evolution (SaDE), success-history based differential evolution along with parameter adaption (SHADE), equilibrium optimizer (EO), flower pollination algorithm based on fractional order calculus (FA-FPO), Gaussian-salp swarm algorithm (GSSA), variable neighbourhood bat algorithm (VNBA), blended biogeography-based optimization (B-BBO), improved elephant herding optimization (IMEHO), original NMRA and others. Experimental results validate the performance of the proposed NMRA with Chebyshev map (CNMRA) as the best well supported by Wilcoxon rank-sum test and Friedman rank (f-rank) test, concerning other algorithms used for comparison. • Chaotic maps with NMRA to balance exploration and exploitation. • Analysis of breeding probability for five different values. • Performance evaluation for variable population and dimension using CEC test suites. • Real world three engineering design problems. • Develop energy efficient WSN for smart city framework.
Exploring the relationship between hadith narrators in Book of Bukhari through SPADE algorithm

As one of the law resources of Muslim society, hadith is very important to learn. Unlike most hadith-related research, which studies more about content, we examine the relationship pattern between hadith narrators. In the study of hadith science, a series of hadith narrators who narrate a hadith is referred to as a sanad. This hadith sanad must be connected to the Prophet as the primary source of a hadith. Therefore, research related to the relationship between narrators is fundamental because it affects the quality and validity of a hadith. This paper analyzes the pattern of hadith narrators using Sequential Pattern Discovery using Equivalence Classes (SPADE). We separate the data of the narrators from the content, whereas, in the hadith books we use, the two are still mixed. This study, therefore, provides detailed information on the steps in the analysis of the patterns of hadith narrators. Some of the highlights of this paper are:•Algorithm 1 provides the detailed steps in data preprocessing to obtain the "clean data" needed in analyzing the pattern of narrator relationships.•Algorithm 2 provides a detailed description of analyzing the pattern between hadith narrators using SPADE.
Security breach prediction using Artificial Neural Networks

These days, there are many sophisticated and swiftly moving cyberthreats. Artificial Neural Networks (ANN) are used to introduce machine learning models that boost security and add new levels of protection for data storage in the event of a breach. The algorithm has been trained with specific attributes, including biometrics, to help it and improve the accuracy of the breach prediction. Artificially produced hashing algorithms are used to both recover the data from the protection the model has constructed for it and to prevent the breach that is activated by the model if it discovers the breach or confirms that the individual attempting to access the data is not allowed. In the case of virtual attacks, the model analyses the attack before it even starts and counters it by adding more layers of security. If the attack becomes intense, the model then blocks access to the network to stop the attacker (hacker) from continuing the attack.
Extending an Industrial Robot with Image Processing System

Today, industrial robots play a significant role in the automotive industry. There are several workflows in the industry that can only be solved efficiently and productively with robots. These robots are equipped with various accessories, such as grippers, sensors and, if necessary, an image processing system. There are tasks where traditional robot programming with fixed coordinates is not enough; visual information about the workspace and workpiece is needed. The problem, especially in smaller companies, is that the existing robot needs to be upgraded, for example, with a visual sensor and an image processing system, but this is usually difficult and costly for older robots and robot controllers. The aim of my research is to create a solution that allows a robot controller to be economically equipped with an image processing system that is not prepared for this at the factory. All that is required for the controller to be able to communicate with the outside world via some communication interface. An external image processing system, which can be a standard PC with the appropriate camera and software, is required. The image processing software running on the PC communicates with the robot controller, and the robot program receives the necessary information from the image processing system via the communication channel.
Comparative Study of Different Metaheuristics on CEC 2020 Benchmarks

Metaheuristic algorithms have increased in usage in all the scientific fields during the last decades. Since no optimisation algorithm is valid for all optimisation problems, many metaheuristics have been developed for various applications. Accordingly, this paper presents a comparative study on CEC 2020 optimisation problems among different algorithms. The goal is to give an overall sight of selecting a specific metaheuristic algorithm for a particular application. The algorithms in this study are; dynamic differential annealed optimisation, particle swarm optimisation, fertilisation optimisation algorithm, grey wolf optimisation, whale optimisation algorithm, firefly algorithm, artificial bee colony, ant lion optimisation, harris hawks optimisation, and sine cosine optimisation algorithm. The results are discussed in the respective sections with a focus on the convergence behaviour of the algorithms.
LRP2A: Layer-wise Relevance Propagation based Adversarial attacking for Graph Neural Networks

Graph Neural Networks (GNNs) are widely utilized for graph data mining, attributable to their powerful feature representation ability. Yet, they are prone to adversarial attacks with only slight perturbations of input data, limiting their applicability to critical applications. Vulnerability analysis of GNNs is thus essential if more robust models are to be developed. To this end, a Layer-wise Relevance Propagation based Adversarial attacking (LRP2A) model is proposed1. Specifically, to facilitate applying LRP to the “black-box” victim model, we train a surrogate model based on a sophisticated re-weighting network. The LRP algorithm is then leveraged for unraveling “contributions” among the nodes in the downstream classification task. Furthermore, the graph adversarial attacking algorithm is intentionally designed to be both interpretable and efficient. Experimental results prove the effectiveness of the proposed attacking model on GNNs for node classification. Additionally, the adoption of LRP2A allows the choice of the adversarial attacking strategies on the GNN interpretable, which in turn can gain deeper insights on the GNN’s vulnerability.
Cofopose: Conditional 2D Pose Estimation with Transformers

Human pose estimation has long been a fundamental problem in computer vision and artificial intelligence. Prominent among the 2D human pose estimation (HPE) methods are the regression-based approaches, which have been proven to achieve excellent results. However, the ground-truth labels are usually inherently ambiguous in challenging cases such as motion blur, occlusions, and truncation, leading to poor performance measurement and lower levels of accuracy. In this paper, we propose Cofopose, which is a two-stage approach consisting of a person and keypoint detection transformers for 2D human pose estimation. Cofopose is composed of conditional cross-attention, a conditional DEtection TRansformer (conditional DETR), and an encoder-decoder in the transformer framework; this allows it to achieve person and keypoint detection. In a significant departure from other approaches, we use conditional cross-attention and fine-tune conditional DETR for our person detection, and encoder-decoders in the transformers for our keypoint detection. Cofopose was extensively evaluated using two benchmark datasets, MS COCO and MPII, achieving an improved performance with significant margins over the existing state-of-the-art frameworks.