Hasil untuk "Information technology"

Jing-Yuan Wei

We reinterpret NP witness discovery through an information-theoretic lens. Rather than measuring search solely by Turing-machine time, we treat recovery as an information-acquisition process: the hidden witness is the sole source of uncertainty, and identification requires reducing this uncertainty through a rate-limited access interface in the sense of Shannon. To make this perspective explicit, we analyze an extreme regime, the \emph{psocid model}, in which the witness is accessible only via equality probes $[π= w^\star]$ under a uniform, structureless prior. Each probe reveals at most $O(N/2^N)$ bits of mutual information, so polynomially many probes accumulate only $o(1)$ total information. By Fano's inequality, reliable recovery requires $Ω(N)$ bits, creating a fundamental mismatch between required and obtainable information. The psocid setting thus isolates a fully symmetric search regime in which no intermediate computation yields global eliminative leverage, thereby exposing an informational origin of exponential search complexity.

Gifty Budu, Edward Owusu , Levina N. Abunya et al.

This study examines selected undergraduate students’ learning styles and preferences at a public university in Ghana. These participants were Level 100 students pursuing the Bachelor of Information Technology programme. The study sought to answer three research questions: What are the students’ learning styles? What are the students’ preferred learning styles? What characteristics do students demonstrate to establish their learning styles? Thirty participants (30) were purposefully sampled from the Faculty of Computing and Information Systems of a public university in Accra, Ghana. In this study, the research instruments used for the data collection were Fleming’s questionnaire on learning styles and semi-structured interviews. The outcome of this study revealed that the participants used all the learning styles such as Visual, Auditory, Read and Write, and Kinesthetic (VARK). The most favoured learning style indicated by the questionnaire responses was Auditory. The interview responses indicated that they used different learning styles, but each participant had the most preferred learning style. The study concluded that learners should be allowed to integrate varied learning styles to improve their learning and make learning interesting and relaxed. The study will be beneficial in formulating policies (at the tertiary education level) that seek to provide different opportunities for students regarding learning styles and preferences for studying English as a second language.

Gang Chen, Xin Tang, Baohang Guo et al.

Underwater sensor deployment in military applications requires high precision, yet existing robotic solutions often lack the maneuverability and adaptability required for complex aquatic environments. To address this gap, this study proposes a bio-inspired underwater robot modeled after the marine iguana, which exhibits effective crawling and swimming capabilities. The research aims to develop a compact, multi-functional robot capable of precise sensor deployment and environmental detection. The methodology integrates a biomimetic mechanical design—featuring leg-based crawling, tail-driven swimming, a deployable head mechanism, and buoyancy control—with a multi-sensor control system for navigation and data acquisition. Gait and trajectory planning are optimized using kinematic modeling for both terrestrial and aquatic locomotion. Experimental results demonstrate the robot’s ability to perform accurate underwater sensor deployment, validating its potential for military applications. This work provides a novel approach to underwater deployment robotics, bridging the gap between biological inspiration and functional engineering.

Konrad Miziński, Sławomir Przyłucki

The aim of this study was to investigate the impact of eBPF technology on the performance of network solutions in Kubernetes clusters. Two configurations were compared: a traditional iptables-based setup and eBPF based solution via the Cilium networking plugin. Performance tests were conducted, measuring throughput, latency, CPU usage, and memory consumption under unloaded and loaded conditions. The results indicate that the traditional configuration achieved higher throughput and lower latency in unloaded scenarios. However, under load, the eBPF-enabled cluster demonstrated advantages, including reduced CPU and memory usage and slightly improved latency. This study highlights the potential of eBPF as an efficient technology for Kubernetes environments, particularly in scenarios demanding high performance and resource efficiency.

Sawitree Pipitgool

In the 21st century, digital technology has become integral to daily life, significantly impacting the skills and knowledge of undergraduate students. This research aims to develop a Semantic Web for learning digital technology in the 21st century by employing ontology techniques to enhance the efficiency of information retrieval. The system is designed to offer flexible learning, adaptable to students' needs, and focuses on categorizing content into three main classes and twelve subclasses. These classes define relationships using four object properties to connect main classes, subclasses, and instances, and four data type properties to link instances with data and relationships between digital technologies. This approach clarifies information and makes it more relevant for undergraduate students. Despite the advantages of ontology techniques in improving information retrieval and recommendation processes, challenges remain due to the complexity of constructing data relationships and establishing rules for data storage and retrieval. Effectively managing semantic data requires specialized knowledge to ensure accurate and efficient outcomes. The ontology knowledge base primarily consists of digital technology, innovation, and digital skills. Based on evaluations by three experts, the Semantic Web for digital technology learning in the 21st century, developed using ontology techniques, was rated at a very good level (\bar{x} = 4.52, S.D. = 0.19). The system's performance was also validated, showing precision at 96.25%, recall at 92.08%, and an F-measure of 95.29%, indicating its effectiveness in supporting learning through digital technology.

Lustiyono Prasetyo Nugroho, Rujianto Eko Saputro, Fandy Setyo Utomo

The development of information technology has not been fully utilized in science learning at vocational high schools, where conventional methods still dominate and make it difficult for students to understand concepts. This study aims to develop information technology and create a new product in the form of 2D visualization. The study employed a development approach based on the ADDIE model and focused on developing learning media for Grade 10 science subjects using animated videos. Three aspects were evaluated: feasibility, practicality, and effectiveness. The assessment of these aspects showed that students were able to improve their learning outcomes and conceptual understanding in science subjects. Based on expert evaluations, media experts provided an average score of 87%, while material experts rated it at 80%. Teacher responses reached 94%, and student responses were 94.49%. The results of the post-test stage indicated an average achievement of 92.25%, with the highest score of 100 and the lowest score of 70. These findings suggest that the use of animated videos can effectively enhance students’ learning outcomes and conceptual understanding. It is recommended that future studies expand the method, scope of materials, and sample size to further address the lower range of student scores.

Dhiraj K. Pandey, Antonio R. Nicolosi

We present three progressively refined pseudorandom function (PRF) constructions based on the learning Burnside homomorphisms with noise (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>B</mi><mi>n</mi></msub></semantics></math></inline-formula>-LHN) assumption. A key challenge in this approach is error management, which we address by extracting errors from the secret key. Our first design, a direct pseudorandom generator (PRG), leverages the lower entropy of the error set (<i>E</i>) compared to the Burnside group (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>B</mi><mi>r</mi></msub></semantics></math></inline-formula>). The second, a parameterized PRG, derives its function description from public parameters and the secret key, aligning with the relaxed PRG requirements in the Goldreich–Goldwasser–Micali (GGM) PRF construction. The final indexed PRG introduces public parameters and an index to refine efficiency. To optimize computations in Burnside groups, we enhance concatenation operations and homomorphisms from <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>B</mi><mi>n</mi></msub></semantics></math></inline-formula> to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>B</mi><mi>r</mi></msub></semantics></math></inline-formula> for <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>n</mi><mo>≫</mo><mi>r</mi></mrow></semantics></math></inline-formula>. Additionally, we explore algorithmic improvements and parallel computation strategies to improve efficiency.

Tracey A. Brickell, Tracey A. Brickell, Tracey A. Brickell et al.

This report details a bench to bedside translation of behavioral and social science research into a clinical program as a result of a collaboration between two United States Defense Health Agency Centers of Excellence for warfighter traumatic brain injury (TBI) and brain health. Identifying a gap in health-related quality of life (HRQOL) measures, our team instigated a 7-year multisite effort to validate and develop generic and caregiver specific HRQOL domains for family members of warfighters and civilians with a TBI using state-of-the-science measurement development standards; the Traumatic Brain Injury Caregiver Quality of Life (TBI-CareQOL) measurement system. The TBI-CareQOL was integrated into the Defense and Veterans Brain Injury Center-Traumatic Brain Injury Center of Excellence 15-Year Longitudinal TBI Study designed to address four elements in a Congressional mandate (NDAA FY2007 Sec721 Public Law 109-364). Based on findings from the 15-Year Longitudinal TBI study and larger body of related literature demonstrating the bidirectional associations between warfighter neurobehavioral outcomes and family distress, relevant TBI-CareQOL measures were integrated into the Family Wellness Program (FWP) for intimate partner (IP) beneficiaries of warfighters with TBI in treatment for chronic neurobehavioral symptoms across the Defense Intrepid Network for Traumatic Brain Injury and Brain Health (DIN). The FWP screens IPs for clinically elevated HRQOL symptoms with clinical follow up offered in alignment with operations at each DIN treatment center and military base. In July 2024, the FWP was launched at the National Intrepid Center of Excellence at Walter Reed National Military Medical Center, and is currently expanding across the DIN.

Malik Muhammad Waqar, Hassan Ali, Heng Zhou et al.

In shrimp farming, determining the physical traits of shrimp is vital for assessing their health and growth. One of the critical traits is their size, as it serves as a key indicator of growth rates, biomass, and effective feed management. However, the accurate measurement of shrimp size is challenged by factors such as their naturally curved body posture, frequent overlapping among individuals, and their tendency to blend with the background, all of which hinder precise size estimation. Traditional methods for measuring the size of shrimp involve manual sampling, which is labor-intensive and time consuming. In contrast, image processing and classical computer vision techniques provide some reasonable results but often suffer from inaccuracies, making them unsuitable for large-scale monitoring. To address this problem, this paper proposes a dual-segmentation deep learning-based framework for accurately estimating shrimp size. It integrates instance segmentation using the RTMDet-m model with an enhanced semantic segmentation model to effectively predict the centerline of the shrimp’s body, enabling precise size measurements. The first stage employs the RTMDet-m model for the instance segmentation of shrimp, achieving an average precision (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>AP</mi><mn>50</mn></msub></semantics></math></inline-formula>) of 96% with fewer parameters and the highest frames per second (FPS) count among state-of-the-art models. The second stage utilizes our custom segmentation model for centerline predictive module, attaining the highest FPS and F1-score of 88.3%. The proposed framework achieves the lowest mean absolute error of 1.02 cm and a root mean square error of 1.27 cm in shrimp size estimation compared to the baseline methods discussed in comparative study sections. Our proposed dual-segmentation framework outperforms both traditional and deep learning based methods used for measuring shrimp size.

Kaiwen Zuo, Jing Tang, Hanbing Qin et al.

Recent advancements in Large Language Models (LLMs) have marked significant progress in understanding and responding to medical inquiries. However, their performance still falls short of the standards set by professional consultations. This paper introduces a novel framework for medical consultation, comprising two main modules: Terminology-Enhanced Information Retrieval (TEIR) and Emotional In-Context Learning (EICL). TEIR ensures implicit reasoning through the utilization of inductive knowledge and key terminology retrieval, overcoming the limitations of restricted domain knowledge in public databases. Additionally, this module features capabilities for processing long context. The EICL module aids in generating sentences with high attribute relevance by memorizing semantic and attribute information from unlabelled corpora and applying controlled retrieval for the required information. Furthermore, a dataset comprising 803,564 consultation records was compiled in China, significantly enhancing the model's capability for complex dialogues and proactive inquiry initiation. Comprehensive experiments demonstrate the proposed method's effectiveness in extending the context window length of existing LLMs. The experimental outcomes and extensive data validate the framework's superiority over five baseline models in terms of BLEU and ROUGE performance metrics, with substantial leads in certain capabilities. Notably, ablation studies confirm the significance of the TEIR and EICL components. In addition, our new framework has the potential to significantly improve patient satisfaction in real clinical consulting situations.

Alexander Frummet, Emanuel Slany, Jonas Amling et al.

Conversational agents such as Microsoft Copilot and Google Gemini assist users with complex search tasks but often generate misleading or fabricated references. This undermines trust, particularly in high-stakes domains such as medicine and finance. Explainable information retrieval (XIR) aims to address this by making search results more transparent and interpretable. While most XIR research is domain-agnostic, this paper focuses on auditing -- a critical yet underexplored area. We argue that XIR systems can support auditors in completing their complex task. We outline key challenges and future research directions to advance XIR in this domain.

Takuro Ishida, Tetsuo Furukawa

This paper proposes an embedding method for co-occurrence data aimed at visual information exploration. We consider cases where co-occurrence probabilities are measured between pairs of elements from heterogeneous domains. The proposed method maps these heterogeneous elements into corresponding two-dimensional latent spaces, enabling visualization of asymmetric relationships between the domains. The key idea is to embed the elements in a way that maximizes their mutual information, thereby preserving the original dependency structure as much as possible. This approach can be naturally extended to cases involving three or more domains, using a generalization of mutual information known as total correlation. For inter-domain analysis, we also propose a visualization method that assigns colors to the latent spaces based on conditional probabilities, allowing users to explore asymmetric relationships interactively. We demonstrate the utility of the method through applications to an adjective-noun dataset, the NeurIPS dataset, and a subject-verb-object dataset, showcasing both intra- and inter-domain analysis.

Lev Tauz, Debarnab Mitra, Jayanth Shreekumar et al.

Quantum key distribution (QKD) is a popular protocol that provides information theoretically secure keys to multiple parties. Two important post-processing steps of QKD are 1) the information reconciliation (IR) step, where parties reconcile mismatches in generated keys through classical communication, and 2) the privacy amplification (PA) step, where parties distill their common key into a new secure key that the adversary has little to no information about. In general, these two steps have been abstracted as two distinct problems. In this work, we consider a new technique of performing the IR and PA steps jointly through sampling that relaxes the requirement on the IR step, allowing for more success in key creation. We provide a novel LDPC code construction known as Block-MDS QC-LDPC codes that can utilize the relaxed requirement by creating LDPC codes with pre-defined sub-matrices of full-rank. We demonstrate through simulations that our technique of sampling can provide notable gains in successfully creating secret keys.

Jiuwei Zhao, Mi-Kyung Sung, Jae-Heung Park et al.

Abstract Owing to the significant influence of El Niño-Southern Oscillation (ENSO) on global climate, how ENSO events are initiated is an intriguing issue. The North Pacific Oscillation (NPO), a primary atmospheric variability over the midlatitude, is a well-known trigger for ENSO events, but the physical linkage is not yet fully understood. Based on observational analyses, in Part I, we proposed a new mechanism that the NPO-related wave activity flux (WAF) could directly induce the equatorial wind anomalies in both upper and lower levels. In this study, we substantiate the impacts of the WAF on tropical circulations using climate models participating in the Coupled Model Intercomparison Project phases 5 and 6 (CMIP5/6). We found that the intensity of the southward WAF over the central Pacific is a paramount factor resulting in intermodel diversity in simulating the NPO–ENSO linkage. By classifying the models into two groups of strong and weak meridional WAF (MWAF), we reveal that the strong MWAF models simulate stronger upper- and lower-level equatorial winds and precipitation anomalies that facilitate the ENSO in subsequent winter. We also reveal that the magnitude of the MWAF is closely related to the model’s climatological meridional wind and meridional shear of climatological zonal wind, emphasizing the role of systematic bias on the ENSO simulation. A comparison of the MWAF impact and seasonal footprinting mechanism demonstrates the dominant influence of the MWAF in determining the diversity of NPO–ENSO relationships.

Asma Cherif, Arwa Badhib, Heyfa Ammar et al.

Credit card fraud is becoming a serious and growing problem as a result of the emergence of innovative technologies and communication methods, such as contactless payment. In this article, we present an in-depth review of cutting-edge research on detecting and predicting fraudulent credit card transactions conducted from 2015 to 2021 inclusive. The selection of 40 relevant articles is reviewed and categorized according to the topics covered (class imbalance problem, feature engineering, etc.) and the machine learning technology used (modelling traditional and deep learning). Our study shows a limited investigation to date into deep learning, revealing that more research is required to address the challenges associated with detecting credit card fraud through the use of new technologies such as big data analytics, large-scale machine learning and cloud computing. Raising current research issues and highlighting future research directions, our study provides a useful source to guide academic and industrial researchers in evaluating financial fraud detection systems and designing robust solutions.

Karol Horodecki, Jingfang Zhou, Maciej Stankiewicz et al.

Quantum contextuality is one of the most recognized resources in quantum communication and computing scenarios. We provide a new quantifier of this resource, the rank of contextuality (RC). We define RC as the minimum number of non-contextual behaviors that are needed to simulate a contextual behavior. We show that the logarithm of RC is a natural contextuality measure satisfying several properties considered in the spirit of the resource-theoretic approach. The properties include faithfulness, monotonicity, and additivity under tensor product. We also give examples of how to construct contextual behaviors with an arbitrary value of RC exhibiting a natural connection between this quantifier and the arboricity of an underlying hypergraph. We also discuss exemplary areas of research in which the new measure appears as a natural quantifier.

Tobias Heindel, Je-Hyung Kim, Niels Gregersen et al.

The generation, manipulation, storage, and detection of single photons play a central role in emerging photonic quantum information technology. Individual photons serve as flying qubits and transmit the quantum information at high speed and with low losses, for example between individual nodes of quantum networks. Due to the laws of quantum mechanics, quantum communication is fundamentally tap-proof, which explains the enormous interest in this modern information technology. On the other hand, stationary qubits or photonic states in quantum computers can potentially lead to enormous increases in performance through parallel data processing, to outperform classical computers in specific tasks when quantum advantage is achieved. Here, we discuss in depth the great potential of quantum dots (QDs) in photonic quantum information technology. In this context, QDs form a key resource for the implementation of quantum communication networks and photonic quantum computers because they can generate single photons on-demand. Moreover, QDs are compatible with the mature semiconductor technology, so that they can be integrated comparatively easily into nanophotonic structures, which form the basis for quantum light sources and integrated photonic quantum circuits. After a thematic introduction, we present modern numerical methods and theoretical approaches to device design and the physical description of quantum dot devices. We then present modern methods and technical solutions for the epitaxial growth and for the deterministic nanoprocessing of quantum devices based on QDs. Furthermore, we present the most promising concepts for quantum light sources and photonic quantum circuits that include single QDs as active elements and discuss applications of these novel devices in photonic quantum information technology. We close with an overview of open issues and an outlook on future developments.

Hang Wang, Yunfeng Chen, Rui Min et al.

Distributed acoustic sensing (DAS) is an emerging technology for recording vibration signals via the optical fibers buried in subsurface conduits. Its relatively easy-to-deploy and high spatial and temporal sampling characteristics make DAS an appealing tool to record seismic wavefields at higher quantity and quality than traditional geophones. Considering that the usage of optical fibers in the urban environment has drawn relatively less attention aside from its functionality as a telecommunication cable, we examine its ability to record seismic signals and investigate its preliminary application in city traffic monitoring. To solve the problems that DAS signals are prone to a variety of environmental noise and are generally of weak amplitude compared to noise, we propose a fast workflow for real-time DAS data processing, which can enhance the detection of regular car signals and suppress the other components. We conduct a DAS experiment in Hangzhou, China, a typical metropolitan area that can provide us with a rich data library to validate our DAS data-processing workflow. The well-processed data enable us to extract their slope and coherency attributes that can provide an estimate of real traffic situations. The one-minute (with video validations) and 24 h statistics of these attributes show that the speed and volume of car flow are well correlated demonstrates the robustness of the proposed data processing workflow and great potential of DAS for city traffic monitoring with high precision and convenience. However, challenges also exist in view that all the attributes are statistically analyzed based on the behaviors of a large number of cars, which is meaningful but lacking in precision. Therefore, we suggest developing more quantitative processing and analyzing methods to provide precise information on individual cars in future works.

Lina Souan, Maher A. Sughayer, Maha Abu Alhowr et al.

Background:Aside from the pandemic's negative health effects, the world was confronted with public confusion since proper communication and favorable decisions became an ongoing challenge. As a result, the public's perceptions were influenced by what they knew, the many sources of COVID-19 information, and how they interpreted it. With cancer patients continuing to oppose COVID-19 vaccines, we sought to investigate the COVID-19 pandemic and vaccine sources of this information in adult cancer patients, which either helped or prevented them from taking the vaccine. We also assessed the relevance and impact of their oncologists' recommendations in encouraging them to take the vaccine.MethodsFrom June to October 2021, an online survey was conducted at King Hussein Cancer Center. A total of 441 adult cancer patients took part in the study. Patients who had granted their consent were requested to complete an online questionnaire, which was collected using the SurveyMonkey questionnaire online platform. Descriptive analysis was done for all variables. The association between categorical and continuous variables was assessed using the Pearson Chi-square and Fisher Exact.ResultsOur results showed that 75% of the patients registered for the COVID-19 vaccine, while 12% refused vaccination. The majority of participants acquired their information from news and television shows, whereas (138/441) got their information through World Health Organization websites. Because the SARS-CoV-2 vaccines were made in such a short period, 54.7 % assumed the vaccines were unsafe. Only 49% of the patients said their oncologists had informed them about the benefits of SARS-CoV-2 vaccines.ConclusionsWe found that SARS-CoV-2 vaccine hesitancy in cancer patients might be related to misinformation obtained from social media despite the availability of supportive scientific information on the vaccine's benefits from the physicians. To combat misleading and unreliable social media news, we recommend that physicians use telehealth technology to reach out to their patients in addition to their face-to-face consultation, which delivers comprehensive, clear, and high-quality digital services that guide and help patients to better understand the advantages of COVID-19 vaccines.

Ang Li, Junping Du, Feifei Kou et al.

Cross-media retrieval of scientific and technological information is one of the important tasks in the cross-media study. Cross-media scientific and technological information retrieval obtain target information from massive multi-source and heterogeneous scientific and technological resources, which helps to design applications that meet users' needs, including scientific and technological information recommendation, personalized scientific and technological information retrieval, etc. The core of cross-media retrieval is to learn a common subspace, so that data from different media can be directly compared with each other after being mapped into this subspace. In subspace learning, existing methods often focus on modeling the discrimination of intra-media data and the invariance of inter-media data after mapping; however, they ignore the semantic consistency of inter-media data before and after mapping and media discrimination of intra-semantics data, which limit the result of cross-media retrieval. In light of this, we propose a scientific and technological information oriented Semantics-adversarial and Media-adversarial Cross-media Retrieval method (SMCR) to find an effective common subspace. Specifically, SMCR minimizes the loss of inter-media semantic consistency in addition to modeling intra-media semantic discrimination, to preserve semantic similarity before and after mapping. Furthermore, SMCR constructs a basic feature mapping network and a refined feature mapping network to jointly minimize the media discriminative loss within semantics, so as to enhance the feature mapping network's ability to confuse the media discriminant network. Experimental results on two datasets demonstrate that the proposed SMCR outperforms state-of-the-art methods in cross-media retrieval.