Reviewerly/exHarmony huggingface dataset:
Semantical investigations on non-classical logics with recovery operators: negation

Abstract We investigate mathematical structures that provide natural semantics for families of (quantified) non-classical logics featuring special unary connectives, known as recovery operators, that allow us to ‘recover’ the properties of classical logic in a controlled manner. These structures are known as topological Boolean algebras, which are Boolean algebras extended with additional operations subject to specific conditions of a topological nature. In this study, we focus on the paradigmatic case of negation. We demonstrate how these algebras are well-suited to provide a semantics for some families of paraconsistent Logics of Formal Inconsistency and paracomplete Logics of Formal Undeterminedness. These logics feature recovery operators used to earmark propositions that behave ‘classically’ when interacting with non-classical negations. Unlike traditional semantical investigations, which are carried out in natural language (extended with mathematical shorthand), our formal meta-language is a system of higher-order logic (HOL) for which automated reasoning tools exist. In our approach, topological Boolean algebras are encoded as algebras of sets via their Stone-type representation. We use our higher-order meta-logic to define and interrelate several transformations on unary set operations, which naturally give rise to a topological cube of opposition. Additionally, our approach enables a uniform characterization of propositional, first-order and higher-order quantification, including restrictions to constant and varying domains. With this work, we aim to make a case for the utilization of automated theorem proving technology for conducting computer-supported research in non-classical logics. All the results presented in this paper have been formally verified, and in many cases obtained, using the Isabelle/HOL proof assistant.
VIDEO CAMERA TECHNOLOGY FOR VEHICLE COUNTING IN TRAFFIC CENSUS: ISSUES, STRATEGIES AND OPPORTUNITIES

This study provides an overview of the sensor technologies commonly used for automated vehicle classification and counting, with a focus on non-intrusive sensors. Video cameras are found to be the most feasible solution for data collection in traffic census as it can operate in portable mode and used at any location. Several factors must be considered to ensure accurate counting. These involve optimum placement of the camera to ensure that all vehicles can be observed, and the lighting conditions must be considered to ensure good video quality. These further contributes to accurate classification and counting of vehicles by dedicated deep learning algorithm. As the data collection may involve location with poor access to cloud computing and storage, offline processing is therefore recommended. The study also revealed opportunities for solving issues related to strategic placement of video cameras, and development of dedicated deep learning algorithms.
Unifying Gradients to Improve Real-World Robustness for Deep Networks

The wide application of deep neural networks (DNNs) demands an increasing amount of attention to their real-world robustness, i.e., whether a DNN resists black-box adversarial attacks, among which score-based query attacks (SQAs) are the most threatening since they can effectively hurt a victim network with only access to model outputs. Defending against SQAs requires a slight but artful variation of outputs due to the service purpose for users, who share the same output information with SQAs. In this article, we propose a real-world defense by Unifying Gradients (UniG) of different data so that SQAs could only probe a much weaker attack direction that is similar for different samples. Since such universal attack perturbations have been validated as less aggressive than the input-specific perturbations, UniG protects real-world DNNs by indicating to attackers a twisted and less informative attack direction. We implement UniG efficiently by a Hadamard product module, which is plug-and-play. According to extensive experiments on 5 SQAs, 2 adaptive attacks and 7 defense baselines, UniG significantly improves real-world robustness without hurting clean accuracy on CIFAR10 and ImageNet. For instance, UniG maintains a model of 77.80% accuracy under a 2500-query Square attack while the state-of-the-art adversarially trained model only has 67.34% on CIFAR10. Simultaneously, UniG outperforms all compared baselines in terms of clean accuracy and achieves the smallest modification of the model output. The code is released at https://github.com/snowien/UniG-pytorch .
The Importance of Social Media for People with Disabilities in Poland During the COVID-19 Epidemic

People with disabilities during the COVID-19 epidemic were particularly vulnerable to information exclusion, e.g. press conferences were not translated into PJM, there was no information in plain language, etc. In addition, restrictions were introduced on social contacts and the possibility of carrying out previous activities and using the support of volunteers or assistants. This made social media an important tool during the COVID-19 pandemic for people with disabilities as it often provided a way to connect with a wider community, maintain social relationships, and obtain information about the epidemic and the restrictions being imposed. The aim of the article is to demonstrate the significance of social media during the COVID-19 pandemic in Poland from 2020 to 2021 for people with disabilities. This will be facilitated by netnographic research, which will be based on the analysis of content published in 10 Facebook groups for people with disabilities between March–April 2020 and December–February 2021.&#x0D; The results of the research indicate that social media played an important role for people with disabilities during the crisis situation of the pandemic. Analysis of the content of social media group profiles showed that they provide a space for seeking and exchanging information, sharing concerns, seeking support, and fighting for their rights by people with disabilities.
Authentication, access, and monitoring system for critical areas with the use of artificial intelligence integrated into perimeter security in a data center

Perimeter security in data centers helps protect systems and the data they store by preventing unauthorized access and protecting critical resources from potential threats. According to the report of the information security company SonicWall, in 2021, there was a 66% increase in the number of ransomware attacks. In addition, the message from the same company indicates that the total number of cyber threats detected in 2021 increased by 24% compared to 2019. Among these attacks, the infrastructure of data centers was compromised; for this reason, organizations include elements Physical such as security cameras, movement detection systems, authentication systems, etc., as an additional measure that contributes to perimeter security. This work proposes using artificial intelligence in the perimeter security of data centers. It allows the automation and optimization of security processes, which translates into greater efficiency and reliability in the operations that prevent intrusions through authentication, permit verification, and monitoring critical areas. It is crucial to ensure that AI-based perimeter security systems are designed to protect and respect user privacy. In addition, it is essential to regularly monitor the effectiveness and integrity of these systems to ensure that they function correctly and meet security standards.
Integrating Sustainability Metrics into Project and Portfolio Performance Assessment in Agile Software Development: A Data-Driven Scoring Model

In today’s rapidly evolving information technology sectors, agile methodologies have been employed by software development organizations to facilitate the large-scale, efficient, and swift development of digital products. Notably, it is a widely accepted principle that an increase in project delivery predictability results from more effective portfolio management. Despite the abundant resources within software engineering that address project management and agile development performance, the measurement of portfolio delivery performance integrating sustainability principles is under-researched. This paper aims to underline a solution to bridge this gap by proposing a data-driven scoring model explicitly designed for software firms to integrate sustainability metrics into their project and portfolio performance assessment. The model is primarily aimed at monitoring and enhancing delivery performance while also reinforcing the sustainability of the software development lifecycle. A thorough literature review was conducted to discern gaps in existing practices, followed by development of a scoring model melded with delivery and sustainability metrics seamlessly. Validated through a case study, the findings reveal that the model influences the performance and sustainability dynamics within software development entities. The insights gained from this study underscore the pivotal role of a harmonized delivery and sustainability metrics system in enhancing the sustainability and efficiency of software development undertakings.
Does a name make a difference? The neo-documentalist movement, where does it stand after 27 years 1996–2023

Purpose The purpose of the paper is to analyze how the Neo-documentalist movement, initiated in 1996 by Michael Buckland, Boyd Rayward and Niels Lund, has evolved in its 27 years history, how the choice of documentation as name of the new program in Tromsø has made a difference in the LIS field and how different documentation scholars around the world has participated and approached the movement until now. Design/methodology/approach The paper has approached the “Neo-documentalist movement” in a historical perspective from 1996 to 2023 discussing what difference does the choice of a concept make, when the concept of documentation is chosen instead of information in the name of a program and for the general discussion of the object of an academic field like Library and Information Science. Findings The analysis shows that it did make a difference to choose the concept of documentation as name of the program in Tromsø and the Neo-documentalist movement contributed to a new focus and discussion of the informative objects, the documents and their creation, not only in Tromsø, but in different parts of the world across linguistic borders. Originality/value The paper is original by the fact that it is the first time that the neo documentalist movement has been reviewed on a global scale across linguistic barriers. It has value by a discussion of the ways in which a choice of concept matter in relation to defining a field and the research agenda.
On Provable White-Box Security in the Strong Incompressibility Model

Incompressibility is a popular security notion for white-box cryptography and captures that a large encryption program cannot be compressed without losing functionality. Fouque, Karpman, Kirchner and Minaud (FKKM) defined strong incompressibility, where a compressed program should not even help to distinguish encryptions of two messages of equal length. Equivalently, the notion can be phrased as indistinguishability under chosen-plaintext attacks and key-leakage (LK-IND-CPA), where the leakage rate is high.In this paper, we show that LK-IND-CPA security with superlogarithmic-length leakage, and thus strong incompressibility, cannot be proven under standard (i.e. single-stage) assumptions, if the encryption scheme is key-fixing, i.e. a polynomial number of message-ciphertext pairs uniquely determine the key with high probability. Our impossibility result refutes a claim by FKKM that their big-key generation mechanism achieves strong incompressibility when combined with any PRG or any conventional encryption scheme, since the claim is not true for encryption schemes which are key-fixing (or for PRGs which are injective). In particular, we prove that the cipher block chaining (CBC) block cipher mode is key-fixing when modelling the cipher as a truly random permutation for each key. Subsequent to and inspired by our work, FKKM prove that their original big-key generation mechanism can be combined with a random oracle into an LK-IND-CPA-secure encryption scheme, circumventing the impossibility result by the use of an idealised model.Along the way, our work also helps clarifying the relations between incompressible white-box cryptography, big-key symmetric encryption, and general leakage resilient cryptography, and their limitations.
INVESTIGATING FACTORS AFFECTING CUSTOMER REPEATED PURCHASING INTENTION IN ONLINE SHOPPING: THE MEDIATING ROLE OF SATISFACTION

This research examines the relationship between website quality, customer perceived value, customer satisfaction, and repeated online purchasing intention in Lebanon. A quantitative approach was used, and data from 385 online shoppers were collected through an online survey. The results indicate that website quality significantly predicts customer perceived value, satisfaction, and repeated online purchasing intention. Customer perceived value also predicts satisfaction and purchase intention, while satisfaction predicts repeated online purchasing intention. These findings highlight the importance of website quality in shaping customer perceptions, satisfaction, and repeated online purchasing behavior.
Mineral Chemistry of Pyroxene Gneiss in Obudu, SE Nigeria, and Its Petrological Significance

Abstract The pyroxene gneiss which forms part of the basement cover in southeast Nigeria is a coarse-grained weakly foliated rock that has experienced high-grade metamorphism and anatexis. Electron microprobe data obtained from samples of this pyroxene-bearing gneiss confirm that the essential minerals are plagioclase (andesine, An 30 – 37 ), orthopyroxene (hypersthene, En 55.3 – 61.2 , Wo 1.0 – 2.6 , Fs 36.3 – 43.7 ), and clinopyroxene (augite, En 39.7 –42.3 , Wo 42.0 – 45.1 , Fs 14.2 – 17.0 ). This assemblage is a typical granulite facies mineralogy produced by igneous rocks with intermediate to mafic composition that have been metamorphosed at medium pressure. Other minerals are calcic amphibole (X Mg = 0.56–0.59), biotite (X Mg = 0.58–0.69), orthoclase, and quartz. Orthoclase occurs mainly in leucocratic bands and clinopyroxene absent samples and may have resulted from dehydration reaction and thus dissolved in the melt phase. Fe-Ti oxides of ilmenite, hematite, and magnetite occur as accessory minerals, giving the imprint of metamorphism under oxidizing conditions. The presence of exsolved titanohematite in ilmenite indicates retrogressive metamorphism.
An Optimal Server Selection Model for Dynamic Task Allocation in Cloud

In cloud data centers, the aggregated performance of multiple heterogeneous server processors can be optimized by proper identification of cloud services from opted cloud service providers. Quality of service (QoS)-based ranking provides efficient information from a set of functionally equivalent customer requests for optimal cloud service selection. Real-world cloud service requirements must obtain QoS values by taking the advantage of customers’ past service usage experiences. This study proposed a QoS-based ranking prediction methodology for cloud services. The proposed system helps in optimal cloud server selection when making a QoS ranking prediction. To predict the optimal server model, a customized QoS ranking prediction approach is proposed in which cloud servers can be effectively and efficiently identified and used in a cloud environments. Ultimately, this reduces the power consumption of unranked or unused servers.
Development of a Software Tool for Assisting the Patients Having Amyotrophic Lateral Sclerosis

We have developed a software tool to help and assist ALS patients in their various non-locomotive day-to-day activities. We want to reduce their depression and anxiety by making them more independent. Our complete application uses the user’s iris movement direction and eyes. Using them it can use most of the features available in our application. Our application continuously monitors the patient to provide him with immediate medical attention whenever needed as of during a paralysis attack ...
Data Hiding Using Steganography

Consequently, numerous strategies have been attained over the last couple of years to secure the transfer of secret information over the Internet. Cryptography and Steganography techniques are used to boost the protection of the data. First off, the basic AES formula has been modified and used to encipher the important message. Second, a mistreatment methodology has been used to conceal the encrypted message. Therefore, two levels of security area units provided mistreatment of the projected hybrid technique. A few challenges encountered in data hiding were database and storage security, privacy protections, and information flow control. The proposed model in this paper helps in overcoming these challenges effectively. The data entered can be text (txt), audio (mp3), or video (mp4). If the input is an image, he or she must provide a cover image to hide the main input image, or else the image will be chosen at random from the dataset. Following this, the output image is converted to a blurred image using Gaussian blur.
A Framework for Embracing Mobile Gadgets to Nurture the Reading Habits of Rural Learners in Southern Africa

AbstractAccess to mobile gadgets has increased exponentially, altering social and educational conditions in Africa. This also applies to the reading habits of rural learners in secondary schools. This paper investigates how mobile gadgets could be embraced to nurture the reading habits of rural secondary school learners in Southern Africa. This qualitative study employed a multiple case study design in five rural secondary schools in Beitbridge District, Matabeleland South, Zimbabwe, and five rural secondary schools in Vhembe District, Limpopo, South Africa. Focus group interviews were adopted as a data collection tool for this study. Data was then analysed thematically according to the objectives of the study. The findings reveal the benefits of mobile reading, which include sharing reading materials, easy access, and use by rural learners. The barriers to mobile gadget reading include a strong emphasis on academic, as opposed to leisure, reading and internet connectivity problems. The findings further reveal that some rural learners in Beitbridge did not have access to mobile gadgets. In addition, we found that some rural learners in Limpopo were using their mobile gadgets to read e-news and e-novels. Some of them spend five or more hours accessing social media for entertainment purposes only, and this has a negative effect on their reading. The study concludes that some rural learners in Southern Africa use their mobile gadgets to nurture their reading habits, although some challenges were identified. In response, this study presents an integrated framework that could be adopted to embrace mobile gadgets to improve the reading habits of rural learners.Keywords: mobile gadgetsmobile readingreading habitsrural learnersSouthern Africa
Evolution and innovations in animation: A comprehensive review and future directions

Summary In this article, we offer an in‐depth exploration of the history, advancements, and innovations in the field of animation. From the roots of traditional hand‐drawn animation to the contemporary realm of computer‐generated imagery, the article outlines the progression of techniques and technologies that have revolutionized the industry. This study meticulously examines the transformative impact of emerging trends such as virtual reality, augmented reality, and artificial intelligence on the development and application of animation. A significant focus is placed on the societal implications of animation, investigating its pervasive influence in fields as diverse as education, advertising, and entertainment. The use of animation as a pedagogical tool, a marketing strategy, and a medium for storytelling has not only redefined these sectors but has also reshaped audience expectations and experiences. In forecasting the future of animation, the article identifies and analyzes potential technologies and trends poised to push the boundaries of the field. This includes examining challenges such as the ethical considerations of AI and the technical constraints of VR and AR. By providing a comprehensive overview of past developments and future possibilities, this review contributes to the ongoing discourse on the continually evolving landscape of animation.
Merit: multi-level graph embedding refinement framework for large-scale graph

Abstract The development of the Internet and big data has led to the emergence of graphs as an important data representation structure in various real-world scenarios. However, as data size increases, computational complexity and memory requirements pose significant challenges for graph embedding. To address this challenge, this paper proposes a multilevel embedding refinement framework (MERIT) based on large-scale graphs, using spectral distance-constrained graph coarsening algorithms and an improved graph convolutional neural network model that addresses the over-smoothing problem by incorporating initial values and identity mapping. Experimental results on large-scale datasets demonstrate the effectiveness of MERIT, with an average AUROC score 8% higher than other baseline methods. Moreover, in a node classification task on a large-scale graph with 126,825 nodes and 22,412,658 edges, the framework improves embedding quality while enhancing the runtime by 25 times. The experimental findings highlight the superior efficiency and accuracy of the proposed approach compared to other graph embedding methods.
Bionic visual navigation model for enhanced template matching and loop closing in challenging lighting environments

Abstract In recent years, various computational models have been proposed in neuroscience, but only a few have been utilized in robot visual navigation through engineering approaches. Besides, these engineering methods lack environmental adaptability, especially in the aspect of visual place recognition in weak or uncontrollable light fluctuations. To address this issue and enhance the performance of visual template matching and map loop closure detection in challenging lighting environments, this paper proposed a bionic visual navigation model that combines two neural network architectures, the Pulse Coupled Neural Network (PCNN) and Continuous Attractor Neural Network (CANN). In our navigation model, the visual features of the environments are encoded as temporal information by the spiking model and input into the local view cells for visual template matching. Additionally, we utilize the pose cells network, which incorporates the similarity between current and previous templates, along with odometry data, to encode spatial information and store it as an experience map. To validate the effectiveness of the proposed model, we conducted evaluations on datasets collected within our library, campus, and an open-source office dataset. The experimental results reveal that our algorithm increases the F 1-score of template matching by approximately 10.5 $$ \% $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>%</mml:mo> </mml:math> , 35.8 $$ \% $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>%</mml:mo> </mml:math> , 61.7 $$ \% $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>%</mml:mo> </mml:math> , and 1.9 $$ \% $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>%</mml:mo> </mml:math> in each dataset, compared to the conventional RatSLAM method. Furthermore, our algorithm generates a more accurate map that closely correlates with the real-world trajectory without compromising on computation time. The results suggest that our bionic visual navigation model is reliable for both standard and extreme lighting conditions.