Reviewerly/exHarmony huggingface dataset:
Belief Contraction in Non-classical logics as Hyperintensional Belief Change

AGM's belief revision is one of the main paradigms in the study of belief change operations. Despite its popularity and importance to the area, it is well recognised that AGM's work relies on a strong idealisation of the agent's capabilities and the nature of beliefs themselves. Particularly, it is recognised in the literature that Belief and Knowledge are hyperintensional attitudes, i.e. they can differentiate between contents that are necessarily equivalent, but to our knowledge, only a few works have explicitly considered how hyperintensionality affects belief change. This work investigates abstract operations of hyperintensional belief change and their connection to belief change in non-classical logics, such as belief contraction operations for Horn Logics and Description Logics. Our work points to hyperintensional belief change as a general framework to unify results in belief change for non-classical logics.
Content Analysis of Voluntary Disclosures on Cybersecurity in Malaysia

Digitalisation is an important driver of the digital economy.The COVID-19 pandemic further accelerates the adoption of digital technology.Having huge data of clients, suppliers, investors, collaborators, and other business contacts expose companies to cyber threats and attacks.Several regulators such as the Security Exchange Commission (SEC) in the US requires public listed companies to disclose information related to cybersecurity risks.This study aims to explore how Malaysian listed companies reveal information related to cybersecurity in their annual reports.In addition, it also attempts to determine the themes or nature of these disclosures and to assess the disclosure levels.Using a content analysis, this study examined forty-nine annual reports from four sectors.It discovers that most companies disclose information related to cybersecurity under eight (8) common sections: governance and leadership, risk management and internal control, key risks and opportunities, management report, sustainability statement, organisational capital, external environment, and performance review.Furthermore, it suggests that these voluntary disclosures are mostly related to eight (8) themes: Data Protection Act, Data Security and Data Integrity, Cyber Threats and Risks, Cybersecurity Assessment, Focus and Priority, Optimisation, Enhanced and Investment, Policies and Artificial Intelligence.Even though most of these disclosures are at the basic level, there are a few companies with comprehensive disclosure on cybersecurity.The research findings provide valuable insights into voluntary disclosures related to cybersecurity among Malaysian listed companies.
Deep Learning Methods with Pre-Trained Word Embeddings and Pre-Trained Transformers for Extreme Multi-Label Text Classification

In recent years, there has been a considerable increase in textual documents online. This increase requires the creation of highly improved machine learning methods to classify text in many different domains. The effectiveness of these machine learning methods depends on the model capacity to understand the complex nature of the unstructured data and the relations of features that exist. Many different machine learning methods were proposed for a long time to solve text classification problems, such as SVM, kNN, and Rocchio classification. These shallow learning methods have achieved doubtless success in many different domains. For big and unstructured data like text, deep learning methods which can learn representations and features from the input data wtihout using any feature extraction methods have shown to be one of the major solutions. In this study, we explore the accuracy of recent recommended deep learning methods for multi-label text classification starting with simple RNN, CNN models to pretrained transformer models. We evaluated these methods’ performances by computing multi-label evaluation metrics and compared the results with the previous studies.
Exploring Heterogeneous Characteristics of Layers in ASR Models for More Efficient Training

Transformer-based architectures have been the subject of research aimed at understanding their overparameterization and the non-uniform importance of their layers. Applying these approaches to Automatic Speech Recognition, we demonstrate that the state-of-the-art Conformer models generally have multiple ambient layers. We study the stability of these layers across runs and model sizes, propose that group normalization may be used without disrupting their formation, and examine their correlation with model weight updates in each layer. Finally, we apply these findings to Federated Learning in order to improve the training procedure, by targeting Federated Dropout to layers by importance. This allows us to reduce the model size optimized by clients without quality degradation, and shows potential for future exploration.
Deep Reinforcement Learning-based Spectrum Assignment with Multi-metric Reward Function and Assignable Boundary Slot Mask

We propose a deep reinforcement learning-based spectrum assignment with a multi-metric reward function and assignable boundary slot mask. The mask restricts options to assignable ones and multi-metric function provides informative clues for training, outperforming first-fit.
Criminal Trend Analysis of Illegal Use of Personal Data for a Period of 2015-2019

This paper presents an analysis of the criminal offence of the unauthorised use of personal data in the Criminal code of the Republic of Croatia, described in article 146 (including the incidence of these crimes over a five-year period). The paper analyses the impact and efficiency of criminal offences under the code for a five-year period from 2015-2019 by reviewing data related to reported criminal offences with detailed statistics about reported, resolved and subsequently discovered offences of illegal use of personal data in the Republic of Croatia. Authors have chosen this specific period of time due to the fact that the European General Data Protection Regulative (GDPR) became active in 2018 and it is directly obliging all the member states to pursue consistent implementation regarding personal data. Inter alia, an analysis was performed on victims as well as perpetrators of relevant criminal offences and shown also by the gender. The objectives of the research were to discover how efficient were Croatian institutions in solving criminal offences that are related to illegal use of personal data in the Republic of Croatia 2015-2019. The paper also describes descriptive statistical data concerning these offences and give us prediction of a number of possible offences for a following year.
Grouped Pointwise Convolutions Significantly Reduces Parameters in EfficientNet

EfficientNet is a recent Deep Convolutional Neural Network (DCNN) architecture intended to be proportionally extendible in depth, width and resolution. Through its variants, it can achieve state of the art accuracy on the ImageNet classification task as well as on other classical challenges. Although its name refers to its efficiency with respect to the ratio between outcome (accuracy) and needed resources (number of parameters, flops), we are studying a method to reduce the original number of trainable parameters by more than 84% while keeping a very similar degree of accuracy. Our proposal is to improve the pointwise (1x1) convolutions, whose number of parameters rapidly grows due to the multiplication of the number of filters by the number of input channels that come from the previous layer. Basically, our tweak consists in grouping filters into parallel branches, where each branch processes a fraction of the input channels. However, by doing so, the learning capability of the DCNN is degraded. To avoid this effect, we suggest interleaving the output of filters from different branches at intermediate layers of consecutive pointwise convolutions. Our experiments with the CIFAR-10 dataset show that our optimized EfficientNet has similar learning capacity to the original layout when training from scratch.
Authentication of a User Using a Combination of Hand Gesture and Online Signature

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Convergence analysis of adaptive tuning parameter for robust stabilizing control of $N$ -level quantum systems

This paper analyzes how the weight function affects the properties of convergence: where and how fast the estimate converges. Stabilization control of quantum systems has been getting much attention as research in quantum information technology progresses, and of course, it is necessary to consider uncertainties in practice. In order to deal with a large uncertain quantum system, we recently proposed an adaptive parameter tuning algorithm based on the study of robust stabilization of $N$ -level quantum systems by Liang, Amini, and Mason (SIAM J. Control Optim., 59(1), 669–692, 2021). In this paper, we briefly review our proposed method and analyze its convergence performance of the estimated parameter. The convergence analysis shows that certain conditions on the weight function are necessary for proper tuning, and the asymptotic convergence rate depends on only the choice of the function.
Design of Digital Filter Based on Artificial Bee Population Algorithm

With the development of digital signal processing technology, higher requirements are put forward for the accuracy of digital filters. The accuracy of the digital filter has an important influence on the digital signal processing effect, which shows that the optimal design of the filter has practical significance. This paper studies the design of digital FIR filter based on artificial bee colony algorithm. The mathematical optimization model of the digital filter is constructed, in which the unit impulse response amplitude of the filter is used as the optimization variable, and the mean square error of the designed low-pass filter and the ideal low-pass filter is the optimization goal. The artificial bee colony algorithm is used to solve the optimization objective function of the filter, the flow chart and implementation steps are given, and the influence of the artificial bee colony algorithm parameters and the filter parameters on the design results is compared and analysed. The experimental results show that the artificial bee colony algorithm can effectively design the digital filter, and the optimized filter basically agrees with the amplitude-frequency response curve of the ideal filter.
Algorithmic Apophenia and Aestheticization of Data

The article suggests taking a look at the artistic development of “artificial intelligence” not in the aspect of its humanization and challenge to humanism, but as a tool for processing data, the number of which and the features technically exceed the capabilities of human understanding. The influence of dataism on culture is considered. The article analyzes the works of contemporary artists (Mario Klingemann, Zach Blas, etc.) working with the phenomenon of “algorithmic apophenia”. The thesis is considered that the transformation of data into information in computer neural networks is methodologically comparable to the creative approaches of modernist artists, which allows us to reach a new level of artis-r tic reflection in the culture that conditioned by machine civilization. Critical and reflexive models of apophenia in contemporary art are collided.
An Overview of Image Steganography and Steganalysis based on Least Significant Bit (LSB) Algorithm

Since the last one decade, the era of information like the new thing in IT is increasing day by day; hence, secret communication over the device has become the most important issue in today's communication systems.  Due to increasing mode of digital communication and enhancing digital communication, security has become very important subject for researchers. There are so many security techniques are exist, but among them, steganography is a method to hide the existing secret message of the end-users in a carrier without being apprehended by the intruders. So now it is very important to develop a mechanism to hide communicator's message in digital media, so the intruders apprehend that passing message.  There are so many mechanisms are available for hiding digital media data, but the image steganography method with the least significant bit substitution is a new approach. So now, this methodology can be used to put into LSB pictures where every pixel is the stand-in for four bytes to demonstrate the potency of that pixel. This effort exhibits more productive consequences, mainly the volume of that data- bit to be concealed regarding the connection to the LSB figure pixels.Under this plank, the Most Significant Bits (MSB) of the confidential information to be concealed in the form of an image is reserved in the Least Significant Bits (LSB) of the portrayal utilized as in the cover picture. It is also familiar that the above pixels in a portrayal are kept in the shape of the bits. In the monochrome portrayal, the potency of every pixel is reserved in eight bits (1-byte). Correspondingly, for a color (R-Red, G-Green, B-Blue) picture, every pixel needs twenty-four bits, eight bits for each surface
Experiments with randomisation in research funding: scoping and workshop report

Worldwide, there is growing interest in applications of randomisation, or lottery-type mechanisms, within the grant funding system, and a small but growing number of research funders are now undertaking trials on various scales. This is part of wider moves in research systems towards experimentation and testing of methods of prioritisation, allocation and evaluation.A number of funders involved in such experiments are partners in the Research on Research Institute (RoRI), and through its work on randomisation, RoRI aims to support these and other partners as they plan, design, undertake and evaluate further trials of randomisation.RoRI hopes to facilitate closer alignment and learning from experiments that are underway or planned, enabling its partners—and the broader research community—to build a richer collective and comparative picture of the pros, cons and possibilities of these and related approaches designed to test and improve methods of evaluation, assessment and allocation.The aim of this short working paper is to summarise recent work in this area, supplemented by partners’ experiences, which can support the RoRI consortium in clarifying the priorities of its work—and laying the foundations for further funder experiments beyond RoRI’s pilot phase.
Bringing templates to life: overcoming obstacles to the organizational implementation of Agile methods

Agile software development methods have become accepted as a template for organizations to create new products. Though generally viewed as an aid to productivity, there are a number of barriers to experiencing their full benefit. One such barrier pertains to the implementation of agile methods across the range of organizational levels from the use of tools to culture, norms, and policies creating the context within which projects are performed. This essay examines in detail the experiences of one expert at integrating agile technique, approach, and philosophy into the broader organizational setting. Numerous particular lessons and prescriptions result from this discussion. Turning around the grounded theory approach where numerous individuals are interrogated mildly in regard to a particular phenomenon, the discussion surfaced in this paper results from repeated interviews with one domain expert. Lessons and comments are organized into four sections: individual team member, organization, transitioning, and tools and techniques.
Persistence of Real Effective Exchange Rate Misalignments in Tunisia

This article's aim is to assess misalignments in Tunisia's real effective exchange rate and to draw attention to their persistence. Thus, over the period 1980-2019, the Autoregressive and Distributed Lag (ARDL) model was used to estimate the relationship between the real effective exchange rate and its fundamental determinants, which are the following: productivity, government expenditure, investment, net foreign assets and trade openness. According to empirical evidence, the key fundamental determinants of the Tunisian real effective exchange rate have a significant long-run association with the Tunisian real effective exchange rate. The findings have revealed the persistence of the series of misalignments in the real effective exchange rate. This property has emerged as a result of either idiosyncratic shocks resulting from internal economic/political problems or structural global forces conveyed through the nominal exchange rate, capital flows, and policy-induced actions.
Am I Allergic to This? Assisting Sight Impaired People in the Kitchen

Sight-Impaired People (SIP) need assistance with food packaging - which can contain safety-critical information. Therefore, Textual Visual Question Answering (VQA) could prove critical in increasing the independence of SIP [26, 35], yet has only seen recent attention. For instance, comprehending text within an image is necessary to determine: what type of soup is in a can, how long to cook a microwave meal, when a box of eggs will expire, and whether a meal contains an ingredient they are allergic to. This handful of examples relate to a kitchen setting — a particularly challenging area for SIP. We extended the existing Aye-saacvoice assistant prototype with this task and setting in mind. We developed textual VQA components to accurately understand what a user is asking, extract relevant text from images in an intelligent manner, and to provide Natural Language answers that build upon the context of previous questions. As our system is created to be assistive, we designed it with a particular focus on privacy, transparency, and controllability. These are vital objectives that existing systems do not cover. We found that our system outperformed other VQA systems on real food packaging questions asked by SIP from the VizWiz dataset [19].
Promising Depth Map Prediction Method from a Single Image Based on Conditional Generative Adversarial Network

Pose estimation is typically performed through 3D images. In contrast, estimating the pose from a single RGB image is still a difficult task. RGB images do not only represent objects’ shape, but also represent the intensity that is relative to the viewpoint, texture, and lighting condition. While the 3D pose estimation from depth images is considered a promising approach since the depth image only represents objects’ shape. Thus, it is necessary to know what is the appropriate method that can be used for predicting the depth image from a 2D RGB image and then to use for getting the 3D pose estimation. In this paper, we propose a promising approach based on a deep learning model for depth estimation in order to improve the 3D pose estimation. The proposed model consists of two successive networks. The first network is an autoencoder network that maps from the RGB domain to the depth domain. The second network is a discriminator network that compares a real depth image to a generated depth image to support the first network to generate an accurate depth image. In this work, we do not use real depth images corresponding to the input color images. Our contribution is to use 3D CAD models corresponding to objects appearing in color images to render depth images from different viewpoints. These rendered images are then used as ground truth and to guide the autoencoder network to learn the mapping from the image domain to the depth domain. The proposed model outperforms state-of-the-art models on the publicly PASCAL 3D+ dataset.
MCMC Sampling Estimation of Poisson-Dirichlet Process Mixture Models

In this article, we aim to estimate the parameters of Poisson-Dirichlet mixture model with multigroup data structure by empirical Bayes. The number of mixture components with Bayesian nonparametric process priors is not fixed in advance and it can grow with the increase of data. Empirical Bayes is the useful method to estimate the mixture components without information on them in advance. We give the procedure to construct smooth estimates of base distribution <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msub> <mrow> <mi>G</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </math> and estimates of the two parameters <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mfenced open="(" close=")" separators="|"> <mrow> <mi>α</mi> <mo>,</mo> <mi>θ</mi> </mrow> </mfenced> </math> . The performances of estimations for parameters under multigroup data are better than those of the single-group data with the same total size of individuals in the perspectives of bias, standard deviations, and mean squared errors by numerical simulation. Also, we applied Poisson-Dirichlet mixture models to well-known real datasets.
Mutual superimposing of SAR and ground-level shooting images mediated by intermediate multi-altitude images

When satellite-based SAR (Synthetic Aperture Radar) images and images acquired from the ground are registered, they offer a wealth of information such as topographic, vegetation or water surface to be extracted from the ground-level shooting images. Simultaneously, high temporal-resolution and high spatial-resolution information obtained by the ground-level shooting images can be superimposed on satellite images. However, due to the differences in imaging modality, spatial resolutions, and observation angle, it was not easy to directly extract the corresponding points between them. This paper proposes an image registration method to estimate the correspondence between SAR images and ground-level shooting images through a set of multi-altitude images taken at different heights.
Denoising X-Ray Image Using Discrete Wavelet Transform and Thresholding

Medical imaging helps in acquiring structure of internal organs for diagnosing and analysis of the diseases. There is wide variety of medical image acquiring technologies. Despite the advancements in medical image acquiring technologies, the possibility of the presence of noise in the image is inevitable. Image denoising plays crucial role in the removal of such noise. This paper gives a clear insight into removal of noises present in X-ray images using discrete wavelet transformation with thresholding. The image quality assessment metrics PSNR and SNR are used to ascertain the performance of the denoising techniques.
Analisa Klasifikasi menggunakan Algoritma Decision Tree pada Data Log Firewall

With the increasing number of attacks on computer networks, an attack detection security system is needed that can classify attacks quickly, and can process data on a large scale. One method for processing data on a large scale is data mining. Data mining is a process that uses one or more Machine Learning techniques to analyze and extract knowledge automatically. In the case of research, there are many techniques in data mining that can be used, including classification techniques. Classification is the basic form of data analysis. Decision Tree is one of the well-known techniques in data mining and is one of the most popular methods in the decision-making process of a case where the criteria for entropy, information gain and profit ratio are obtained from the method. This study will discuss data classification using the Decision Tree algorithm, where the data contains logs on firewall and internet traffic control devices used at Firat University Turkey. The data contains 65532 data records with 12 attributes and 4 classes in the action attribute then the data is classified using the Decision Tree algorithm, then processed through RapidMiner version 9.9 and the highest recall results are 100% and the accuracy rate is 99.82% so that the classification model by using the Decision Tree algorithm is better when applied to the dataset.