Reviewerly/exHarmony huggingface dataset:
Cybersecurity Awareness, Education, and Workplace Training Using Socially Enabled Intelligent Chatbots

Over the past ten years, chatbot technology has become a burgeoning field within AI, finding practical applications in sectors such as banking, customer service, medicine, education, and e-commerce. Nevertheless, the use of intelligent agents in cybersecurity needs to be more extensive. Recently, most research in cybersecurity has focused more and more on detecting malicious activities automatically. Unfortunately, few works focused on how the human factor can result in such attacks. Human-induced attacks and errors are found to be either unintentional or intentional. The first type, our focus, can result from lacking training and cyber awareness. Attackers use different tactics to lure victims into unsafe behaviors, leading to potential personal blackmail, ransomware attacks, or data breaches through phishing emails. Usually, to address the problem mentioned above, universities, employers, and organizations provide cyber awareness education. However, the problem is that the training is a one-time process and does not provide the necessary continuing support and education. As a result, people usually find themselves in a situation where they need specific advice before engaging in a particular activity. Sometimes, they might need someone to alert them to a suspicious email, fishy link, or abnormal attachment. In response, in this study, we discuss the integration of chatbots with social networking for cybersecurity public awareness, education, and workplace training. We focus on utilizing chatbots as social, collaborative agents to protect users against cyber-attacks. The chatbots provide continuous support to users by providing immediate answers and advice on what to do in various threatening situations. In addition, admin users add training material remotely into chatbots, and users can view and answer interactive questions to ensure they are up-to-date regarding cyber awareness.
Interpretable deep learning for hourly solar radiation prediction: A real measured data case study in Tokyo

With their superior performance in capturing complex relationships between input and output variables, deep learning models have gained popularity in the field of solar radiation prediction. However, the use of deep learning models as a predictive tool is often constrained by their inherent complexity, which can limit the interpretability and usability of the model in practical scenarios, such as model predictive control (MPC) in energy systems. In this study, time series are transformed into an image, and the hourly solar radiation prediction is realized through a convolutional neural network (CNN). The experiments demonstrate that the proposed CNN model enhances interpretability at the cost of a minor 6 % reduction in prediction accuracy, compared to the baseline model. Analogous to the deconvolution operation for image classification, feature maps of CNN networks can thus be used for model interpretability, whereby the closer the feature maps are to the input (In the context of forward propagation, this means that the layers of a CNN closer to the input data), the higher is the interpretability. In extracting the feature map using CNN, solar radiation and radiation time played the most important roles in the entire prediction process.
Creating synthetic datasets for collaborative filtering recommender systems using generative adversarial networks

Research and education in machine learning requires diverse, representative, and open datasets that contain sufficient samples to handle the necessary training, validation, and testing tasks. Currently, the Recommender Systems area includes a large number of subfields in which accuracy and beyond-accuracy quality measures are continuously being improved. To feed this research variety, it is both necessary and convenient to reinforce the existing datasets with synthetic ones. This paper proposes a Generative Adversarial Network (GAN)-based method to generate collaborative filtering datasets in a parameterized way by selecting their preferred number of users, items, samples, and stochastic variability. This parameterization cannot be performed using regular GANs. Our GAN model is fed with dense, short, and continuous embedding representations of items and users, instead of sparse, large, and discrete vectors, to ensure fast and accurate learning, as compared to the traditional approach based on large and sparse input vectors. The proposed architecture includes a DeepMF model to extract the dense user and item embeddings and a clustering process to convert the dense GAN generated samples to the discrete and sparse samples necessary to create each required synthetic dataset. The results from three different source datasets show adequate distributions and expected quality values and evolutions in the generated datasets compared to the source datasets. Synthetic datasets and source codes are available to researchers.
Optimal Repairs in the Description Logic $$\mathcal{E}\mathcal{L}$$ Revisited

Ontologies based on Description Logics may contain errors, which are usually detected when reasoning produces consequences that follow from the ontology, but do not hold in the modelled application domain. In previous work, we have introduced repair approaches for $$\mathcal{E}\mathcal{L}$$ ontologies that are optimal in the sense that they preserve a maximal amount of consequences. In this paper, we will, on the one hand, review these approaches, but with an emphasis on motivation rather than on technical details. On the other hand, we will describe new results that address the problems that optimal repairs may become very large or need not even exist unless strong restrictions on the terminological part of the ontology apply. We will show how one can deal with these problems by introducing concise representations of optimal repairs.
A New Class of Explanations for Classifiers with Non-binary Features

Two types of explanations have been receiving increased attention in the literature when analyzing the decisions made by classifiers. The first type explains why a decision was made and is known as a sufficient reason for the decision, also an abductive explanation or a PI-explanation. The second type explains why some other decision was not made and is known as a necessary reason for the decision, also a contrastive or counterfactual explanation. These explanations were defined for classifiers with binary, discrete and, in some cases, continuous features. We show that these explanations can be significantly improved in the presence of non-binary features, leading to a new class of explanations that relay more information about decisions and the underlying classifiers. Necessary and sufficient reasons were also shown to be the prime implicates and implicants of the complete reason for a decision, which can be obtained using a quantification operator. We show that our improved notions of necessary and sufficient reasons are also prime implicates and implicants but for an improved notion of complete reason obtained by a new quantification operator that we also define and study.
Hate Speech in The Context of Digital Violence in Türkiye: Perceptions and Biases on Afghan Identity

According to the data of the Presidency of Migration Management (2021), the largest group of irregular migrants detected in Türkiye in 2021 were Afghans. The arrival of irregular migrants from Afghanistan to Türkiye alongside the Syrian refugees have caused a negatively charged reaction in Turkish society and from the main parties in the opposition. Based on this apparent reaction against the arrival of Afghan&#x0D; refugees, the claim can be made that Afghans are now the new “other” category in Türkiye. We identify two hashtags used on Twitter to protest the arrival of Afghans, which are #sınırnamustur (border is honor) and #hudutnamustur (frontier is honor) having the same meaning in Turkish and are Trend Topics on Twitter. The purpose of this study is to examine the reason why there are two hashtags on Twitter to protest the arrival of Afghan immigrants, what the differences and similarities are between&#x0D; the two in terms of how Afghans are defined and what kinds of hate speech they are subjected to. For this purpose, we applied Craswell’s mixed model in this study using both content analysis and social network analysis techniques. The investigation results reveal that the hashtag #sınırnamustur is a manifestation of a political endeavor, asserting that Afghan refugees are associated with a ‘security challenge’. In contrast, the hashtag #hudutnamustur represents a response from the general populace, embodying elements of cultural superiority.
Selecting the best optimizers for deep learning–based medical image segmentation

The goal of this work is to explore the best optimizers for deep learning in the context of medical image segmentation and to provide guidance on how to design segmentation networks with effective optimization strategies.Most successful deep learning networks are trained using two types of stochastic gradient descent (SGD) algorithms: adaptive learning and accelerated schemes. Adaptive learning helps with fast convergence by starting with a larger learning rate (LR) and gradually decreasing it. Momentum optimizers are particularly effective at quickly optimizing neural networks within the accelerated schemes category. By revealing the potential interplay between these two types of algorithms [LR and momentum optimizers or momentum rate (MR) in short], in this article, we explore the two variants of SGD algorithms in a single setting. We suggest using cyclic learning as the base optimizer and integrating optimal values of learning rate and momentum rate. The new optimization function proposed in this work is based on the Nesterov accelerated gradient optimizer, which is more efficient computationally and has better generalization capabilities compared to other adaptive optimizers.We investigated the relationship of LR and MR under an important problem of medical image segmentation of cardiac structures from MRI and CT scans. We conducted experiments using the cardiac imaging dataset from the ACDC challenge of MICCAI 2017, and four different architectures were shown to be successful for cardiac image segmentation problems. Our comprehensive evaluations demonstrated that the proposed optimizer achieved better results (over a 2% improvement in the dice metric) than other optimizers in the deep learning literature with similar or lower computational cost in both single and multi-object segmentation settings.We hypothesized that the combination of accelerated and adaptive optimization methods can have a drastic effect in medical image segmentation performances. To this end, we proposed a new cyclic optimization method (Cyclic Learning/Momentum Rate) to address the efficiency and accuracy problems in deep learning-based medical image segmentation. The proposed strategy yielded better generalization in comparison to adaptive optimizers.
Explainable survival analysis with uncertainty using convolution-involved vision transformer

Image-based precision medicine research is able to help doctors make better decisions on treatments. Among all kinds of medical images, a special form is called Whole Slide Image (WSI), which is used for diagnosing patients with cancer, aiming to enable more accurate survival prediction with its high resolution. However, One unique challenge of the WSI-based prediction models is processing the gigabyte-size or even terabyte-size WSIs, which would make most models computationally infeasible. Although existing models mostly use a pre-selected subset of key patches or patch clusters as input, they might discard some important morphology information, making the prediction inferior. Another challenge is improving the prediction models' explainability, which is crucial to help doctors understand the predictions given by the models and make faithful decisions with high confidence. To address the above two challenges, in this work, we propose a novel explainable survival prediction model based on Vision Transformer. Specifically, we adopt dual-channel convolutional layers to utilize the complete WSIs for more accurate predictions. We also introduce the aleatoric uncertainty into our model to understand its limitation and avoid overconfidence in using the prediction results. Additionally, we present a post-hoc explainable method to identify the most salient patches and distinct morphology features as supporting evidence for predictions. Evaluations of two large cancer datasets show that our proposed model is able to make survival predictions more effectively and has better explainability for cancer diagnosis.
μPEWFace: Parallel ensemble of weighted deep convolutional neural networks with novel loss functions for face-based authentication

Training Deep Convolutional Neural Networks (DCNN) with large-scale face images takes a significant amount of processing resources and the tuning/optimization time cost for face-based authentication. It must continue to improve its accuracy and speed during the matching phase. In this study, we propose a method, μPEWFace, that inherits not only the benefits of recent DCNNs with adaptive loss functions, such as MagFace, ElasticFace, and AdaFace, for boosting accuracy but also the means to reduce matching time. Consequently, our method expands on the weighted voting mechanism that leverages suboptimal trained models to improve the discriminative capabilities of face-based authentication, as opposed to searching for the best optimal model. In order to boost the efficiency of face-based authentication, we also propose performing the matching phase for each model in parallel. To demonstrate the speed and accuracy of our method, we conduct exhaustive experiments using a variety of well-known benchmarks, including LFW, CFP-FP, AgeDB-30, CALFW, CPLFW, and IJB-B. The experimental findings demonstrate that the proposed method for face-based authentication achieves state-of-the-art results and exhibits remarkable performance. © 2023 Published by Elsevier Ltd.
Truth and Preferences - A Game Approach for Qualitative Choice Logic

In this paper, we introduce game-theoretic semantics (GTS) for Qualitative Choice Logic (QCL), which, in order to express preferences, extends classical propositional logic with an additional connective called ordered disjunction. In particular, we present a new semantics that makes use of GTS negation and, by doing so, avoids contentious behavior of negation in existing QCL-semantics.
Short Boolean Formulas as Explanations in Practice

Abstract We investigate explainability via short Boolean formulas in the data model based on unary relations. As an explanation of length k , we take a Boolean formula of length k that minimizes the error with respect to the target attribute to be explained. We first provide novel quantitative bounds for the expected error in this scenario. We then also demonstrate how the setting works in practice by studying three concrete data sets. In each case, we calculate explanation formulas of different lengths using an encoding in Answer Set Programming. The most accurate formulas we obtain achieve errors similar to other methods on the same data sets. However, due to overfitting, these formulas are not necessarily ideal explanations, so we use cross validation to identify a suitable length for explanations. By limiting to shorter formulas, we obtain explanations that avoid overfitting but are still reasonably accurate and also, importantly, human interpretable.
How Easy it is to Know How: An Upper Bound for the Satisfiability Problem

We investigate the complexity of the satisfiability problem for a modal logic expressing `knowing how' assertions, related to an agent's abilities to achieve a certain goal. We take one of the most standard semantics for this kind of logics based on linear plans. Our main result is a proof that checking satisfiability of a `knowing how' formula can be done in $\Sigma_2^P$. The algorithm we present relies on eliminating nested modalities in a formula, and then performing multiple calls to a satisfiability checking oracle for propositional logic.
Enhancing Temporal Planning by Sequential Macro-Actions

Temporal planning is an extension of classical planning involving concurrent execution of actions and alignment with temporal constraints. Unfortunately, the performance of temporal planning engines tends to sharply deteriorate when the number of agents and objects in a domain gets large. A possible remedy is to use macro-actions that are well-studied in the context of classical planning. In temporal planning settings, however, introducing macro-actions is significantly more challenging when the concurrent execution of actions and shared use of resources, provided the compliance to temporal constraints, should not be suppressed entirely. Our work contributes a general concept of sequential temporal macro-actions that guarantees the applicability of obtained plans, i.e., the sequence of original actions encapsulated by a macro-action is always executable. We apply our approach to several temporal planners and domains, stemming from the International Planning Competition and RoboCup Logistics League. Our experiments yield improvements in terms of obtained satisficing plans as well as plan quality for the majority of tested planners and domains.
Splitting Techniques for Conditional Belief Bases in the Context of c-Representations

Splitting belief bases is fundamental for efficient reasoning and for better understanding interrelationships among the knowledge entities. In this paper, we survey the most important splitting techniques for conditional belief bases in the context of c-representations which constitute a specific class of ranking models with outstanding behavior not only with respect to belief base splitting, as shown in recent papers. We provide a splitting hierarchy, in particular by proving that safe conditional syntax splittings and case splittings are so-called CSP-constraint splittings. We advance the level of knowledge about CSP-constraint splittings and present an algorithm for computing CSP-constraint splittings.
Monetary attribute of stablecoins: A theoretical and empirical test

&lt;abstract&gt; &lt;p&gt;With the continuous expansion of their market size and scope of use, the monetary attribute of stablecoins has become a focal point. The identification of the monetary attribute of stablecoins is a prerequisite for their supervision. Based on the essence and macroeconomic effects of money, this paper analyzes the monetary attribute of stablecoins from theoretical and empirical perspectives. We find that in the traditional financial market, stablecoins are not widely accepted, and their increased supply competes with traditional financial assets. As new types of digital assets, they do not possess a monetary attribute. However, in the digital asset market, stablecoins are widely used. The increase in issuance pushes up asset prices and brings liquidity effects to the market. Therefore, stablecoins possess a monetary attribute in the digital asset market and play the role of "digital fiat currency". This private sector liquidity is not controlled by the government and tends to accumulate risk. Therefore, the government should clarify the legal attribute of stablecoins according to their monetary attribute, strengthen the supervision of stablecoin issuers and prevent the private sector from monopolizing the digital asset market transaction medium.&lt;/p&gt; &lt;/abstract&gt;
Endoscopic image classification algorithm based on Poolformer

Image desmoking is a significant aspect of endoscopic image processing, effectively mitigating visual field obstructions without the need for additional surgical interventions. However, current smoke removal techniques tend to apply comprehensive video enhancement to all frames, encompassing both smoke-free and smoke-affected images, which not only escalates computational costs but also introduces potential noise during the enhancement of smoke-free images. In response to this challenge, this paper introduces an approach for classifying images that contain surgical smoke within endoscopic scenes. This classification method provides crucial target frame information for enhancing surgical smoke removal, improving the scientific robustness, and enhancing the real-time processing capabilities of image-based smoke removal method. The proposed endoscopic smoke image classification algorithm based on the improved Poolformer model, augments the model's capacity for endoscopic image feature extraction. This enhancement is achieved by transforming the Token Mixer within the encoder into a multi-branch structure akin to ConvNeXt, a pure convolutional neural network. Moreover, the conversion to a single-path topology during the prediction phase elevates processing speed. Experiments use the endoscopic dataset sourced from the Hamlyn Centre Laparoscopic/Endoscopic Video Dataset, augmented by Blender software rendering. The dataset comprises 3,800 training images and 1,200 test images, distributed in a 4:1 ratio of smoke-free to smoke-containing images. The outcomes affirm the superior performance of this paper's approach across multiple parameters. Comparative assessments against existing models, such as mobilenet_v3, efficientnet_b7, and ViT-B/16, substantiate that the proposed method excels in accuracy, sensitivity, and inference speed. Notably, when contrasted with the Poolformer_s12 network, the proposed method achieves a 2.3% enhancement in accuracy, an 8.2% boost in sensitivity, while incurring a mere 6.4 frames per second reduction in processing speed, maintaining 87 frames per second. The results authenticate the improved performance of the refined Poolformer model in endoscopic smoke image classification tasks. This advancement presents a lightweight yet effective solution for the automatic detection of smoke-containing images in endoscopy. This approach strikes a balance between the accuracy and real-time processing requirements of endoscopic image analysis, offering valuable insights for targeted desmoking process.
Exploration of Metrics and Datasets to Assess the Fidelity of Images Generated by Generative Adversarial Networks

Advancements in technology have improved human well-being but also enabled new avenues for criminal activities, including digital exploits like deep fakes, online fraud, and cyberbullying. Detecting and preventing such activities, especially for law enforcement agencies needing photo profiles for covert operations, is imperative. Yet, conventional methods relying on authentic images are hindered by data protection laws. To address this, alternatives like generative adversarial networks, stable diffusion, and pixel recurrent neural networks can generate synthetic images. However, evaluating synthetic image quality is complex due to the varied techniques. Metrics are crucial, offering objective measures to compare techniques and identify areas for enhancement. This article underscores metrics’ significance in evaluating synthetic images produced by generative adversarial networks. By analyzing metrics and datasets used, researchers can comprehend the strengths, weaknesses, and areas for further research on generative adversarial networks. The article ultimately enhances image generation precision and control by detailing dataset preprocessing and quality metrics for synthetic images.