Hasil untuk "Electronic computers. Computer science"

Grazia D'Onghia, Antonio Lioy

Trust is the core building block of secure systems, and it is enforced through methods to ensure that a specific system is properly configured and works as expected. In this context, a Root of Trust (RoT) establishes a trusted environment, where both data and code are authenticated via a digital signature based on asymmetric cryptography, which is vulnerable to the threat posed by Quantum Computers (QCs). Firmware, being the first layer of trusted software, faces unique risks due to its longevity and difficult update. The transition of firmware protection to Post-Quantum Cryptography (PQC) is urgent, since it reduces the risk derived from exposing all computing and network devices to quantum-based attacks. This paper offers an analysis of the most common trust techniques and their roadmap towards a Post-Quantum (PQ) world, by investigating the current status of PQC and the challenges posed by such algorithms in existing Trusted Computing (TC) solutions from an integration perspective. Furthermore, this paper proposes an architecture for TC techniques enhanced with PEC, addressing the imperative for immediate adoption of quantum-resistant algorithms.

Roberto Montemanni, Sara Ceschia, Andrea Schaerf

Home healthcare has become more and more central in the last decades, due to the advantages it can bring to both healthcare institutions and patients. Planning activities in this context, however, presents significant challenges related to route planning and mutual synchronization of caregivers.In this paper we propose a new compact model for the combined optimization of scheduling (of the activities) and routing (of the caregivers) characterized by fewer variables and constraints when compared with the models previously available in the literature. The new model is solved by a constraint programming solver and compared experimentally with the exact and metaheuristic approaches available in the literature on the common datasets adopted by the community. The results show that the new model provides improved lower bounds for the vast majority of the instances, while producing at the same time high quality heuristic solutions, comparable to those of tailored metaheuristics, for small/medium size instances.

Uttam U. Deshpande, Supriya Shanbhag, Ramesh Koti et al.

Phone calls are strictly forbidden in certain locations due to the potential security threats. Mobile phones’ growing capabilities have also increased the risk of their misuse in places that are restricted, like manufacturing plants. Unauthorized mobile phone use in these environments can lead to significant safety hazards, operational disruptions, and security breaches. There is an urgent need to develop an intelligent system that can identify the presence of individuals as well as cellphone usage. We propose an advanced Artificial Intelligence and Computer Vision-based real-time cell phone detection system to detect mobile phone usage in restricted zones. Modern deep learning approaches, such as YOLOv8 for real-time object detection to accurately detect cell phone usage, are combined with dense layers of ResNet-50 to perform image classification tasks. We highlight the critical need for such detection systems in manufacturing settings and discuss the specific challenges encountered. To support this research, we have developed a custom dataset of 2,150 images, which features a diverse array of images with varying foreground and background elements to reflect real-world conditions. Our experimental results demonstrate that YOLOv8 achieves a Mean Average Precision (mAP50) of 49.5% at 0.5 IoU for cellphone detection tasks and an accuracy of 96.03% for prediction tasks. These findings underscore the effectiveness of our AI and CV-based system in detecting unauthorized mobile phone usage in restricted zones.

Urslla Uchechi Izuazu, Cosmas Ifeanyi Nwakanma, Dong-Seong Kim et al.

Abstract Deep learning-based intrusion detection systems (DL-IDS) have proven effective in detecting cyber threats. However, their vulnerability to adversarial attacks and environmental noise, particularly in industrial settings, limits practical application. Current IDS models often assume ideal conditions, overlooking noise and adversarial manipulations, leading to degraded performance when deployed in real-world environments. Additionally, the black-box nature of DL model complicates decision-making, especially in industrial control systems (ICS) network, where understanding model behavior is crucial. This paper introduces the eXplainable Cyber-Threat Detection Framework (XC-TDF), a novel solution designed to overcome these challenges. XC-TDF enhances robustness against noise and adversarial attacks using regularization and adversarial training respectively, and also improves transparency through an eXplainable Artificial Intelligence (XAI) module. Simulation results demonstrate its effectiveness, showing resilience to perturbation by achieving commendable accuracy of 100% and 99.4% on the Wustl-IIoT2021 and Edge-IIoT datasets, respectively.

Tahiya Chowdhury

Developing competency in artificial intelligence is becoming increasingly crucial for computer science (CS) students at all levels of the CS curriculum. However, most previous research focuses on advanced CS courses, as traditional introductory courses provide limited opportunities to develop AI skills and knowledge. This paper introduces an introductory CS course where students learn computational thinking through computer vision, a sub-field of AI, as an application context. The course aims to achieve computational thinking outcomes alongside critical thinking outcomes that expose students to AI approaches and their societal implications. Through experiential activities such as individual projects and reading discussions, our course seeks to balance technical learning and critical thinking goals. Our evaluation, based on pre-and post-course surveys, shows an improved sense of belonging, self-efficacy, and AI ethics awareness among students. The results suggest that an AI-focused context can enhance participation and employability, student-selected projects support self-efficacy, and ethically grounded AI instruction can be effective for interdisciplinary audiences. Students' discussions on reading assignments demonstrated deep engagement with the complex challenges in today's AI landscape. Finally, we share insights on scaling such courses for larger cohorts and improving the learning experience for introductory CS students.

Alma Y. Alanis, Gustavo Munoz-Gomez, Nancy F. Ramirez et al.

This work introduces an impulsive neural control algorithm designed to mitigate the spread of epidemic diseases. The objective of this paper is the development of a vaccination strategy based on a PIN-type impulsive controller based on an online-trained neural identifier to control the spread of infectious diseases under a complex network approach with time-varying connections where each node represents a population of individuals whose dynamics are defined by the MSEIR epidemiological model. Considering an unknown model of the system, a neural identifier is designed that provides a nonlinear model for the complex network trained through an extended Kalman filter algorithm. Simulation results are presented by applying the proposed control scheme for a complex network parameterized as infectious diseases.

Thomas J. Misa

A new dataset (N = 7,456) analyzes women's research authorship in the Association for Computing Machinery's founding 13 Special Interest Groups or SIGs, a proxy for computer science. ACM SIGs expanded during 1970-2000; each experienced increasing women's authorship. But diversity abounds. Several SIGs had fewer than 10% women authors while SIGUCCS (university computing centers) exceeded 40%. Three SIGs experienced accelerating growth in women's authorship; most, including a composite ACM, had decelerating growth. This research may encourage reform efforts, often focusing on general education or workforce factors (across the entity of "computer science"), to examine under-studied dynamics within computer science that shaped changes in women's participation.

Aakash Gautam, Anagha Kulkarni, Sarah Hug et al.

Given the potential for technology to inflict harm and injustice on society, it is imperative that we cultivate a sense of social responsibility among our students as they progress through the Computer Science (CS) curriculum. Our students need to be able to examine the social complexities in which technology development and use are situated. Also, aligning students' personal goals and their ability to achieve them in their field of study is important for promoting motivation and a sense of belonging. Promoting communal goals while learning computing can help broaden participation, particularly among groups who have been historically marginalized in computing. Keeping these considerations in mind, we piloted an introductory Java programming course in which activities engaging students in ethical and socially responsible considerations were integrated across modules. Rather than adding social on top of the technical content, our curricular approach seeks to weave them together. The data from the class suggests that the students found the inclusion of the social context in the technical assignments to be more motivating and expressed greater agency in realizing social change. We share our approach to designing this new introductory socially responsible computing course and the students' reflections. We also highlight seven considerations for educators seeking to incorporate socially responsible computing.

Richa Agarwal, Shahid Ali

In order to generalise semi-invariant Riemannian maps, Sahin first introduced the idea of “Generic Riemannian maps”. We extend the idea of generic Riemannian maps to the case in which the total manifold is a nearly Kaehler manifold. We study the integrability conditions for the horizontal distribution although vertical distribution is always integrable. We also study the geometry of foliations of two distributions and obtain the necessary and sufficient condition for generic Riemannian maps to be totally geodesic. Additionally, we study the generic Riemannian map with umbilical fibers.

Kehinde Aruleba, Ismaila Temitayo Sanusi, George Obaido et al.

The integration of artificial intelligence tools such as ChatGPT in the education system has gained attention in recent years. This experience report explores students' perceptions and suggestions for integrating ChatGPT in a computer science course. Following a ChatGPT activity which includes code completion and analysis, seven students participated in in-depth interviews. Findings from the transcribed interviews suggest that ChatGPT has the potential to enhance learning experience including programming. They highlighted the tool's ability to respond immediately to queries and supporting personalised learning. However, they raise concerns that heavy reliance on ChatGPT may adversely affect students' critical thinking and problem-solving skills. These findings show the importance of carefully balancing using ChatGPT in computer science courses. The findings of this research have significant implications for educators, curriculum designers and policymakers as they explore integrating AI tools into educational contexts.

Alvian M. Sroyer, Samuel A. Mandowen, Felix Reba

Malaria adalah penyakit yang disebabkan oleh parasite bernama Plasmodium. Tercatat keseluruhan kasus malaria yang terjadi di Indonesia pada tahun 2019 adalah sebanyak 250.644 kasus. Dan kasus malaria tertinggi terjadi di provinsi Papua, yaitu sebesar 86% atau sebanyak 216.380 kasus. Di Provinsi Papua, penyakit malaria dialami oleh semua usia dan bulan-bulan terjadi peningkatan pasien penderita malaria juga sangat bervariasi. Hal ini mengakibatkan dinas Kesehatan mengalami kesulitan dalam mengelompokan jenis malaria berdasarkan usia pasien dan bulan-bulan kejadian. Sebenarnya sudah ada penelitian yang menjelaskan pengelompokan jenis-jenis malaria, namun belum dijelaskan secara terperinci masing-masing kelompok malaria seperti Malaria Tropika, Malaria Tertiana, Malaria Quartana, Malaria Ovale. Tujuan dari penelitian ini adalah, melakukan analisis cluster terhadap beberapa jenis malaria, usia dan bulan kejadian. Metode cluster yang digunakan dalam penelitian ini adalah metode Single Linkage dan K-Means. Selanjutnya kedua metode akan di evalusi menggunakan standar deviasi. Metode terbaik yang dapat digunakan untuk analisis cluster adalah metode yang memiliki nilai standar deviasi lebih kecil. Data yang digunakan dalam penelitian ini adalah data sekunder yang diperoleh dari Dinas Kesehatan Provinsi Papua. Hasil penelitian menunjukan bahwa, metode Single Linkage lebih akurat dibandingkan dengan K-Means. Dimana dari 50 pasien terdapat 47 pasien lebih dominan terkena penyakit malaria tertiana yaitu pada rentang usia remaja dan dewasa pada bulan juni. Sehingga diharapkan pemerintah Provinsi Papua dapat memberikan sosialisasi kepada masyarakat, khususnya mereka yang pada rentang usia remaja dan dewasa. Karena hampir 94% penyakit malaria tertiana di derita oleh mereka yang berusia remaja dan dewasa.

Andrii Tytarenko

In this paper, we derive a novel method as a generalization over LCEs such as E2C. The method develops the idea of learning a locally linear state space by adding a multi-step prediction, thus allowing for more explicit control over the curvature. We show that the method outperforms E2C without drastic model changes which come with other works, such as PCC and P3C. We discuss the relation between E2C and the presented method and derive update equations. We provide empirical evidence, which suggests that by considering the multi-step prediction, our method – ms-E2C – allows learning much better latent state spaces in terms of curvature and next state predictability. Finally, we also discuss certain stability challenges we encounter with multi-step predictions and how to mitigate them.

Martino Romaniello, the ESO Science Archive operations, development team

The ESO Science Archive is the collection and access point of the data generated at ESO's La Silla Paranal Observatory, both raw and processed. It is a major contributor to ESO's science output, being used in about 4 out of 10 refereed articles with ESO data. In this paper, which is presented on behalf of the operations and development teams, we review its contents, policies, us interfaces and impact.

Gabriela OPAIT

The victorious spirit, which predominates in all the provinces of the United States of America, penetrates the scale of values in rise concerning the real G.D.P. The symbiosis of the progresses witnessed by the American nation along of the time, as a result of the management organized in “American smart style”, have reflection in the values regarding the real G.D.P. of the United States of America. The aim of this original research pursues to display the permanent increase regarding the real G.D.P. of the United States of America, between 2021-2030

Uyanga Dorjsembe, Ju Hong Lee, Bumghi Choi et al.

Deep neural networks have achieved almost human-level results in various tasks and have become popular in the broad artificial intelligence domains. Uncertainty estimation is an on-demand task caused by the black-box point estimation behavior of deep learning. The deep ensemble provides increased accuracy and estimated uncertainty; however, linearly increasing the size makes the deep ensemble unfeasible for memory-intensive tasks. To address this problem, we used model pruning and quantization with a deep ensemble and analyzed the effect in the context of uncertainty metrics. We empirically showed that the ensemble members’ disagreement increases with pruning, making models sparser by zeroing irrelevant parameters. Increased disagreement im-plies increased uncertainty, which helps in making more robust predictions. Accordingly, an energy-efficient compressed deep ensemble is appropriate for memory-intensive and uncertainty-aware tasks.

Ivan Sekaj, Ivan Kénický, Filip Zúbek

Artificial neural networks are means which are, among several other approaches, effectively usable for modelling and control of non-linear dynamic systems. In case of modelling systems input and output signals are a-priori known, supervised learning methods can be used. But in case of controller design of dynamic systems the required (optimal) controller output is a-priori unknown, supervised learning cannot be used. In such case we only can define some criterion function, which represents the required control performance of the closed-loop system. We present a neuro-evolution design for control of a continuous-time controller of non-linear dynamic systems. The controller is represented by an MLP-type artificial neural network. The learning algorithm of the neural network is based on an evolutionary approach with genetic algorithm. An integral-type performance index representing control quality, which is based on closed-loop simulation, is minimised. The results are demonstrated on selected experiments with controller reference value changes as well as with noisy system outputs.

Orit Hazzan

We live in exceptional times in which the entire world is witnessing the exponential spread of a pandemic, which requires to adopt new habits of mind and behaviors. In this paper, I introduce the term exponential competence, which encompasses these cognitive and social skills, and describe a course for computer science and software engineering students in which emphasis is placed on exponential competence. I argue that exponential competence is especially important for computer science and software engineering students, since many of them will, most likely, be required to deal with exponential phenomena in their future professional development.

Aurélie Breidenbach, Caroline Mahlow, Andreas Schreiber

Gender diversity in the tech sector is - not yet? - sufficient to create a balanced ratio of men and women. For many women, access to computer science is hampered by socialization-related, social, cultural and structural obstacles. The so-called implicit gender bias has a great influence in this respect. The lack of contact in areas of computer science makes it difficult to develop or expand potential interests. Female role models as well as more transparency of the job description should help women to promote their - possible - interest in the job description. However, gender diversity can also be promoted and fostered through adapted measures by leaders.

Khizar Hayat, Mobeen Nazar, Taimoor Khalid et al.

Vaccinations are very essential for the prevention of harmful diseases. However, the implementation rate of vaccination varies in different parts of the world. Many countries struggle to achieve the maximum immunization ratio due to their vaccination practices and methodologies. However, the authors have developed a solution to strengthen the vaccination procedure. SHIFAYAAB, Proposed Methodology in this paper provides a centralized platform for different healthcare organizations and hospitals, working on various vaccination programs. The idea is to collectively provide a centralized database for the vaccination programs by integrating the platform with the healthcare organizations and hospitals, to enhance and improve the vaccination procedure for the workers as well as the public. SHIFAYAAB proposes automation of the vaccination procedure by replacing the old school vaccine schedule card-reports with autonomous system[1]generated microplans. It will assemble the vaccination records and provide a user-friendly platform for the vaccinators to carry out the vaccination process. It will also provide children parents a platform to keep track of their vaccination progress by monitoring their microplan along with regular notification reminders from the platform.

Zhongfu SUN, Yongli LI, Feixiang ZHENG et al.

At present time,China is entering an important period of upgradation and transitionin agriculture,therefore,promoting smart agriculture development will be becoming the prior way with integration of modern information technology.Firstly,the current status of agriculture in China and the necessity of smart agriculture were analyzed,including possible blockchain application.Secondly,the connotation and its main trend for blockchain were briefly introduced.Thirdly,the main fields of blockchain application were summed up for smart agriculture.Finally,the prospects were set forth and some important suggestions were proposed for agricultural blockchain in future.