Reviewerly/exHarmony huggingface dataset:
Explainable AI: A Review of the Empirical Literature

AI deep learning models have become more capable, but also more complex and less explainable. To address this development, new research on so-called explainable AI (XAI) has proliferated. This paper surveys the empirical literature on XAI based on human-subject experiments. It classifies extant work across different technical and experimental dimensions. Our findings suggest that explainable AI improves self-reported understanding and trust in AI. However, this rarely translates into improved performance of humans in incentivized tasks with AI support. We list several implications of these findings concerning the use of explainable AI in human-computer interaction.
Lung Cancer Detection Using Ensemble Learning

From time to time, lung cancer has appeared in the category of nearly the most lethal maladies since humankind existed. It is even among the most incessant fatalities and major reasons of mortality among all cancers. Lung cancer cases are significantly growing. In India, there are around 70 thousand instances each year. Because the condition is usually asymptomatic in its early stages, it is practically extremely difficult to identify. As a result, early cancer identification is beneficial for preserving lives. Early discovery can improve a patient’s chances of rehabilitation and recovery. Technology has a critical part in accurately identifying cancers. Based on these findings, several researchers have offered various ways. Several Computer-aided diagnostics (CAD) methodologies and systems have been suggested, developed and created in recent years to handle this problem using computer technology. Also, there are some factors like smoking, taking alcohol, anxiety etc. that also helps us to detect if patient is having cancer or not has also been taken into considerations by many researchers. Taking on an assortment of techniques such as machine learning, Ensemble learning and deep learning approaches and numerous ways based on image processing techniques and text information, those systems contribute to a great extent to ascertain the cancer malignancy degree. We had in view integrating or putting together the Ensemble learning algorithms like Stacking, blinding, Max voting, boosting and XGBoost through this so as to build an advanced method to assess and scrutinize the out-turn. On comparing Blinding ensemble learning technique proves to be the most efficient technique based on the performance metrics like accuracy, F1 score, precision and recall.
Wright’s Strict Finitistic Logic in the Classical Metatheory: The Propositional Case

Abstract Crispin Wright in his 1982 paper argues for strict finitism, a constructive standpoint that is more restrictive than intuitionism. In its appendix, he proposes models of strict finitistic arithmetic. They are tree-like structures, formed in his strict finitistic metatheory, of equations between numerals on which concrete arithmetical sentences are evaluated. As a first step towards classical formalisation of strict finitism, we propose their counterparts in the classical metatheory with one additional assumption, and then extract the propositional part of ‘strict finitistic logic’ from it and investigate. We will provide a sound and complete pair of a Kripke-style semantics and a sequent calculus, and compare with other logics. The logic lacks the law of excluded middle and Modus Ponens and is weaker than classical logic, but stronger than any proper intermediate logics in terms of theoremhood. In fact, all the other well-known classical theorems are found to be theorems. Finally, we will make an observation that models of this semantics can be seen as nodes of an intuitionistic model.
RESOLVING SMART HEALTH SECURITY ISSUES USING ONTOLOGIES AND BLOCKCHAIN SERVICES

IoT is one of the most commonly used and emerging technology. It has been used for resolving many issues in daily life. In this paper, we are going to address the security issues in smart health and how we can resolve them by using blockchain technology. Blockchain is evolving rapidly which deals in blocks of data. By using this attribute, we can partially grant access to data to a specific user by identifying their roles in that certain activity. Although there are already many derived solutions by other researchers, we will follow the combination of RFID and EHR techniques of blockchain to propose a better solution to solve this security challenge.
Multitype Label Noise Modeling and Uncertainty-Weighted Label Correction for Concealed Object Detection

Recently, plenty of millimeter-wave image concealed object detection models have achieved superior performance on benchmark datasets. The success of these models heavily depends on a large amount of fully annotated data. However, noisy-label data often exist in a large-scale dataset, which deteriorate the performance of models. In this article, we delve into the training of the concealed object detector with label noise, where we recognize divergent types of noisy annotations. To tackle the multitype label noise problem, we propose a unified framework, including two stages: label noise modeling and correction training. We design the region-level label noise modeling, which analyzes and models label noise via the classification branch loss curves. To correct the labels during training, we design the uncertainty-weighted loss for label correction. The experiments on a millimeter-wave security image dataset demonstrate the effectiveness of our proposed training framework, which can alleviate the negative influence of noisy annotations on network training. By introducing our framework, the object detection network can achieve a prominent performance improvement on mean average precision (0.176 on average) compared to the one trained with noisy labels.
STEAM in Information Technology Education

The article examines the essence of STEAM education and the role of information technology (IT) in it. It shares the implementation of STEAM approach in an information technology lesson. The analysis of the conducted survey among pedagogy students in IT on the applicability of STEAM learning in IT lessons and expected difficulties in the process is presented.
Improving Deep Regression with Ordinal Entropy

In computer vision, it is often observed that formulating regression problems as a classification task often yields better performance. We investigate this curious phenomenon and provide a derivation to show that classification, with the cross-entropy loss, outperforms regression with a mean squared error loss in its ability to learn high-entropy feature representations. Based on the analysis, we propose an ordinal entropy loss to encourage higher-entropy feature spaces while maintaining ordinal relationships to improve the performance of regression tasks. Experiments on synthetic and real-world regression tasks demonstrate the importance and benefits of increasing entropy for regression.
Intelligent Edge Sensing and Control Co-Design for Industrial Cyber-Physical System

In the new generation of industrial cyber-physical system (ICPS), data-driven control is one of the emerging intelligent control methods to realize efficient production adjustment. In most existing works, the perfect sensing process is regarded as the fundamental assumption. However the experienced sensing strategies deployed in advance are increasingly difficult to adapt to the expanding network scale and diversified production demands in the Industry 4.0 era. To tackle the challenges, we propose the novel intelligent edge sensing and control co-design (IESCC) framework under ICPS. The cooperation of five constructed graph convolutional neural networks respectively related to system model, sensing model, estimator, actor and critic is adopted to approximate the coupled optimality conditions of sensing and control strategies. The structure of learning networks is designed in advance for online strategy solving tailored for the real-time industrial requirements and edge computing power. In particular, the representation capabilities of learning networks under different scales are quantitatively analyzed from the perspectives of observability and controllability. Besides, the feasible region of learning rates is explicitly depicted to ensure convergence. Finally, the proposed algorithm is applied into the laminar cooling process in the semi-physical simulation. Compared with the state-of-the-art approaches, our method can always guarantee observability and controllability. And up to 27.9 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\%}$</tex-math></inline-formula> overall performance of sensing and control is improved, and 38 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\%}$</tex-math></inline-formula> execution time reduction is achieved on average.
Vulnerable PQC against Side Channel Analysis - A Case Study on Kyber

The emergence of quantum computing and its impact on current cryptographic algorithms has triggered the migration to post-quantum cryptography (PQC). Among the PQC candidates, CRYSTALS-Kyber is a key encapsulation mechanism (KEM) that stands out from the National Institute of Standards and Technology (NIST) standardization project. While software implementations of Kyber have been developed and evaluated recently, Kyber's hardware implementations, especially designs with parallel architecture, are rarely discussed. To help better understand Kyber hardware designs and their security against side-channel analysis (SCA) attacks, in this paper, we first adapt the two most recent Kyber hardware designs for FPGA implementations. We then perform SCA attacks against these hardware designs with different architectures, i.e., parallelization and pipelining. Our experimental results show that Kyber designs on FPGA boards are vulnerable to SCA attacks including electromagnetic (EM) and power side channels. An attacker only needs 27 ~ 1600 power traces or 60 ~ 2680 EM traces to recover the decryption key successfully.
Extension-based semantics for incomplete argumentation frameworks: properties, complexity and algorithms

Abstract Incomplete Argumentation Frameworks (IAFs) have been defined to incorporate some qualitative uncertainty in abstract argumentation: information such as ‘I am not sure whether this argument exists’ or ‘I am not sure whether this argument attacks that one’ can be expressed. Reasoning with IAFs is classically based on a set of completions, i.e. standard argumentation frameworks (AFs) that represent the possible worlds encoded in the IAF. The number of these completions may be exponential with respect to the number of arguments in the IAF. This leads, in some cases, to an increase of the complexity of reasoning, compared to the complexity of standard AFs. In this paper, we follow an approach that was initiated for Partial Argumentation Frameworks (PAFs) (a subclass of IAFs), which consists in defining new forms of conflict-freeness and defense, the properties that underly the definition of Dung’s semantics for AFs. We generalize these semantics from PAFs to IAFs. We show that, among three possible types of admissibility, only two of them satisfy some desirable properties. We use them to define two new families of extension-based semantics. We study the properties of these semantics, and in particular, we show that their complexity remains the same as in the case of Dung’s AFs. Finally, we propose a logical encoding of these semantics, and we show experimentally that this encoding can be used efficiently to reason with IAFs, thanks to the power of modern SAT solvers.
Surface Object Detection Based on Improved YOLOv5

Surface garbage has a great impact on the ecological environment of the water area. Manual salvage of garbage is costly and time-consuming. Therefore, professional surface salvage robots have attracted extensive attention. The reliability of the fishing robot mainly depends on the object recognition algorithm carried by it. In this paper, a surface object detection algorithm based on improved YOLOv5 is proposed. In order to strengthen the diversity of the dataset and enable the model to identify scenes with multiple overlapping objects, Mixup is used in this paper for data enhancement to improve the generalization ability of the model. At the same time, the S2-MLPv2 attention mechanism is added to the detection part of YOLOv5, and the spatial shift operation is used to improve the image recognition accuracy. The average accuracy of the improved model on the dataset is up to 85.6%, which is 1.8 percentage points higher than that of the original model. The improved algorithm proposed in this paper has certain reference significance for surface garbage salvage.
Kernel-based modeling

The aim of data mining is data classification and regression modeling of real-world problems. Kernel-based approaches are a relatively new important method based on the different kernels type which is based on statistical learning theory initiated. Such models are capable of adapting themselves to predict any variable of interest via sufficient inputs. The kernel trick is one key component that allows the application of kernel-based approaches on a wide variety of problems. There are many kernels, and to solve the kind of problems the right kernel with the right dataset should be used. In the past few decades, kernel-based approaches have been applied for assessing complex phenomena. One of the important and complex phenomena is water resource engineering problems which are generally arisen at every stage of water-related issues ranging from water resources development to field-scale water utilization practices. This chapter aims to provide a basic understanding of the theory behind some kernel-based approaches and a general review of their applications for solving some types of water resources engineering problems.
Social Engineering and Data Privacy

This paper presents the concept of social engineering. The internet has completely changed the mode of operations of modern-day systems. There are billions of internet users and this number is rising every day. Hence, ensuring the security is very important for any cyber physical systems. This paper focuses on one of the very important aspects of cyber security, i.e., social engineering. It is defined as a set of techniques of human manipulation by exploitation of the basic emotions of human beings. Different institutions deploy state-of-the-art systems to protect the data housed in their datacenter. However, it is also essential that an individual must secure their personal information from the social engineers. Hence, this paper discusses the data privacy issues and various relevant techniques under the umbrella of social engineering. It discusses various social engineering techniques and summarizes those techniques, thus concluding the paper.
A Different Kind of Vā: Spiraling through Time and Space

This dialogue is a structured account of an experiment that we, as researchers in the Vā Moana–Pacific Spaces cluster at Auckland University of Technology, carried out during and between lockdowns in Tāmaki Makaurau Auckland during the covid-19 pandemic in 2020–2021. The previous year, Vā Moana had begun to investigate how—without shared physical presence—virtual participation in events can uphold central Māori and Moana (Pacific) traditional values of tikanga (te reo Māori: correct procedure, custom) and teu le vā (gagana Sāmoa: nurturing relational space). Aspects of our research concern practices that continue to emphasize vā—as the attachment and feeling for place and relatedness—outside the Pacific homelands. These nascent practices contribute to an emerging understanding of place as an imaginary space of belonging, in which online environments (the digital vā) play a role. The outbreak of covid-19 gave this general interest unexpected but sharp focus. In this essay, we present, contextualize, and analyze excerpts from three conversations between Vā Moana team members in Aotearoa. Held during, between, and after lockdown periods between March and November 2020, these conversations were conducted either fully online or in a blended format. In the latter case, some members met face-to-face in a "hub," and others used online platforms to participate in reviewing and reorganizing our research relationships under the new conditions, using the challenge thrown before us as an opportunity for experimentation and change.
The Generation of Visually Credible Adversarial Examples with Genetic Algorithms

An adversarial example is an input that a neural network misclassifies although the input differs only slightly from an input that the network classifies correctly. Adversarial examples are used to augment neural network training data, measure the vulnerability of neural networks, and provide intuitive interpretations of neural network output that humans can understand. Although adversarial examples are defined in the literature as similar to authentic input from the perspective of humans, the literature measures similarity with mathematical norms that are not scientifically correlated with human perception. Our main contributions are to construct a genetic algorithm (GA) that generates adversarial examples more similar to authentic input than do existing methods and to demonstrate with a survey that humans perceive those adversarial examples to have greater visual similarity than existing methods. The GA incorporates a neural network, and we test many parameter sets to determine which fitness function, selection operator, mutation operator, and neural network generate adversarial examples most visually similar to authentic input. We establish which mathematical norms are most correlated with human perception, which permits future research to incorporate the human perspective without testing many norms or conducting intensive surveys with human subjects. We also document a tradeoff between speed and quality in adversarial examples generated by GAs and existing methods. Although existing adversarial methods are faster, a GA provides higher-quality adversarial examples in terms of visual similarity and feasibility of adversarial examples. We apply the GA to the Modified National Institute of Standards and Technology (MNIST) and Canadian Institute for Advanced Research (CIFAR-10) datasets.
A Dynamic Programming Algorithm for Finding an Optimal Sequence of Informative Measurements

An informative measurement is the most efficient way to gain information about an unknown state. We present a first-principles derivation of a general-purpose dynamic programming algorithm that returns an optimal sequence of informative measurements by sequentially maximizing the entropy of possible measurement outcomes. This algorithm can be used by an autonomous agent or robot to decide where best to measure next, planning a path corresponding to an optimal sequence of informative measurements. The algorithm is applicable to states and controls that are either continuous or discrete, and agent dynamics that is either stochastic or deterministic; including Markov decision processes and Gaussian processes. Recent results from the fields of approximate dynamic programming and reinforcement learning, including on-line approximations such as rollout and Monte Carlo tree search, allow the measurement task to be solved in real time. The resulting solutions include non-myopic paths and measurement sequences that can generally outperform, sometimes substantially, commonly used greedy approaches. This is demonstrated for a global search task, where on-line planning for a sequence of local searches is found to reduce the number of measurements in the search by approximately half. A variant of the algorithm is derived for Gaussian processes for active sensing.
Competitive Learning to Generate Sparse Representations for Associative Memory

One of the most well established brain principles, hebbian learning, has led to the theoretical concept of neural assemblies. Based on it, many interesting brain theories have spawned.Palm's work implements this concept through binary associative memory, in a model that not only has a wide cognitive explanatory power but also makes neuroscientific predictions.Yet, associative memory can only work with logarithmic sparse representations, which makes it extremely difficult to apply the model to real data.We propose a biologically plausible network that encodes images into codes that are suitable for associative memory. It is organized into groups of neurons that specialize on local receptive fields, and learn through a competitive scheme.After conducting auto- and hetero-association experiments on two visual data sets, we can conclude that our network not only beats sparse coding baselines, but also that it comes close to the performance achieved using optimal random codes.
Integrating Higher-Order Features for Structural Role Discovery

The role of node is able to denote its function and effect in the network, and can represent personal identity or status in real-world complex systems. It is defined on the local connective patterns and structural similarities. Compared to the community detection, the task of role discovery is independent to the node proximity which is generally related to the distance of density in the network. It is more likely to be determined by structural similarity, and the structural node representations have achieved great success in this field. Some existing methods focus on the local structural features to generate role-oriented node representations, but they consider too much on local structures and fail to learn multi-aspects representations of structural roles. More specifically, the local, global, and higher-order structures can denote different type of roles, and there are varying dependencies between them, leading to the difficulty to effectively integrate them. Thus, we propose a novel model HORD to integrate higher-order features into structural role discovery, aiming to learn multi-aspects structural node representations of roles. We leverage higher-order and local features and utilize the unified graph neural network (GNN) framework to organically combine them to generate structural node representations. We conduct extensive experiments on several real-world networks and the results demonstrate that our model is better than state-of-the art methods.
User Interface (UI) Design and User Experience (UX) for Mobile-based Quranic Memorizing using the Lean UX

The interest of the Muslim community in memorizing the Qur'an is increasing today. The appreciation for the memorizers is also getting higher with the many opportunities for scholarships and other achievements from various institutions, especially universities. Memorizing activities are usually taught at Islamic boarding schools using special manuscripts and accompanied by teachers directly. This luxury cannot be enjoyed by the Muslim community, which is increasingly having high mobility. Therefore, it is an opportunity to present digital memorization manuscripts installed on their devices. In addition, the memorization method taught in Islamic boarding schools is also based on the instructions of the Mushaf and the input from the memorizers has shown that the tikrar method is the most widely used. This study will propose a digital mushaf design for memorizing using the tikrar method which makes it easy for the memorizers. The method used is Lean UX by providing a prototype based on the results of the memorization manuscript review and the currently available application of the Qur'an. This prototype was immediately reviewed by the memorizers to get the appropriate design. The results of the design were tested using the Usability Testing and Important Performance Analysis (IPA) methods in order to gain ease of use and user acceptance with the proposed design. The results show positive review results by achieving an easy, intuitive, and efficient design with a usability score of 79.8 which means it is quite good. Keywords: Al Qur’an memorization, Usability testing, Important Performance Analysis, Lean UX, Tikrar method .