Hasil untuk "Information technology"

Zhengquan Zhang, Yue Xiao, Zheng Ma et al.

A key enabler for the intelligent information society of 2030, 6G networks are expected to provide performance superior to 5G and satisfy emerging services and applications. In this article, we present our vision of what 6G will be and describe usage scenarios and requirements for multi-terabyte per second (Tb/s) and intelligent 6G networks. We present a large-dimensional and autonomous network architecture that integrates space, air, ground, and underwater networks to provide ubiquitous and unlimited wireless connectivity. We also discuss artificial intelligence (AI) and machine learning [1], [2] for autonomous networks and innovative air-interface design. Finally, we identify several promising technologies for the 6G ecosystem, including terahertz (THz) communications, very-large-scale antenna arrays [i.e., supermassive (SM) multiple-input, multiple-output (MIMO)], large intelligent surfaces (LISs) and holographic beamforming (HBF), orbital angular momentum (OAM) multiplexing, laser and visible-light communications (VLC), blockchain-based spectrum sharing, quantum communications and computing, molecular communications, and the Internet of Nano-Things.

Yang Lu

L. Floridi

V. C. Gungor, D. Sahin, T. Koçak et al.

M. Vouk

ldquoCloudrdquo computing - a relatively recent term, builds on decades of research in virtualization, distributed computing, utility computing, and more recently networking, web and software services. It implies a service oriented architecture, reduced information technology overhead for the end-user, great flexibility, reduced total cost of ownership, on-demand services and many other things. This paper discusses the concept of ldquocloudrdquo computing, issues it tries to address, related research topics, and a ldquocloudrdquo implementation available today.

P. Evans, T. Wurster

P. K. Mehta, P. Monteiro

J. Lyotard, G. Bennington, B. Massumi

M. Prensky

Part one of this paper highlights how students today think and process information fundamentally differently from their predecessors, as a result of being surrounded by new technology. The author compares these “digital natives” with the older generation who are learning and adopting new technology naming them “digital immigrants”.

S. Shane

A. Wensley

J. Dowling, G. Milburn

We are currently in the midst of a second quantum revolution The first quantum revolution gave us new rules that govern physical reality. The second quantum revolution will take these rules and use them to develop new technologies. In this review we discuss the principles upon which quantum technology is based and the tools required to develop it. We discuss a number of examples of research programs that could deliver quantum technologies in coming decades including: quantum information technology, quantum electromechanical systems, coherent quantum electronics, quantum optics and coherent matter technology.

Sarv Devaraj, L. Krajewski, Jerry C. Wei

Melanie A. Kilian, David Elsweiler

Understanding and classifying query intents can improve retrieval effectiveness by helping align search results with the motivations behind user queries. However, existing intent taxonomies are typically derived from system log data and capture mostly isolated information needs, while the broader task context often remains unaddressed. This limitation becomes increasingly relevant as interactions with Large Language Models (LLMs) expand user expectations from simple query answering toward comprehensive task support, for example, with purchasing decisions or in travel planning. At the same time, current LLMs still struggle to fully interpret complex and multifaceted tasks. To address this gap, we argue for a stronger task-based perspective on query intent. Drawing on a grounded-theory-based interview study with airport information clerks, we present a taxonomy of task-based information request intents that bridges the gap between traditional query-focused approaches and the emerging demands of AI-driven task-oriented search.

S. Boell, D. Cecez-Kecmanovic

Philipp Schaer, Christin Katharina Kreutz, Krisztian Balog et al.

Simulations in information access (IA) have recently gained interest, as shown by various tutorials and workshops around that topic. Simulations can be key contributors to central IA research and evaluation questions, especially around interactive settings when real users are unavailable, or their participation is impossible due to ethical reasons. In addition, simulations in IA can help contribute to a better understanding of users, reduce complexity of evaluation experiments, and improve reproducibility. Building on recent developments in methods and toolkits, the second iteration of our Sim4IA workshop aims to again bring together researchers and practitioners to form an interactive and engaging forum for discussions on the future perspectives of the field. An additional aim is to plan an upcoming TREC/CLEF campaign.

Fengxiang Xu, Nan Zhang, Zhenxiang Chen et al.

Hyperspectral remote sensing images provide rich spectral information about land surface features and are widely used in fields such as environmental monitoring, disaster assessment, and land classification. However, effectively leveraging the spectral information in hyperspectral images remains a significant challenge. In this paper, we propose a hyperspectral pansharpening method based on ratio transformation and residual networks, which significantly enhances both spatial details and spectral fidelity. The method generates an initial image through ratio transformation and refines it using a residual attention network. Additionally, specialized loss functions are designed to preserve both spatial and spectral details. Experimental results demonstrate that, when evaluated on the EO-1 and Chikusei datasets, the proposed method outperforms other methods in terms of both visual quality and quantitative metrics, particularly in spatial detail clarity and spectral fidelity. This approach effectively addresses the limitations of existing technologies and shows great potential for high-resolution remote sensing image processing applications.

Emil Mihai Pavelescu, Saroj Kumar Patra, Cosmin Romaniţan et al.

Intermediate band solar cells (IBSCs) have the potential to overcome the efficiency limit of single-bandgap solar cells. Dilute nitride III–V alloys, with splitting of the conduction band due to band anticrossing, can be used as the intermediate-band material for solar cell applications. In this work, we report on the introduction of engineered GaInNAs alloy, with low (dilute) In and N contents, as IBSC material and the comparison of the performances of GaInNAs-based and the corresponding In-free GaNAs-based IBSCs grown on GaAs (100) substrate. Introduction of a small amount of In (3%) in a GaN _0.011 As _0.989 -based IBSC was found to noticeably increase the short-circuit current, I _sc , at the expense of a small decrease in the open-circuit voltage, V _oc . When annealed at 750 °C for 90 s, significant enhancements in I _sc and V _oc are seen, especially in the In-containing solar cell. The observed In-related enhancement in cell parameters after annealing is related to In-promoted bandgap tailoring and efficient curing of carrier traps during annealing. This occurred without change in the macroscopic composition of the GaInNAs alloy, most likely by In-N bond formation upon annealing, a phenomenon which does not exist in the GaNAs alloy.

Hongyan Shi, Xiaofei Wu, Guogang Wang

Achieving satisfactory control accuracy in typical chemical processes is a challenging task. This is primarily due to their complex nonlinear dynamic characteristics. To address this challenge, this study proposes a novel deep reinforcement learning (DRL) approach that integrates an improved Ornstein-Uhlenbeck (IOU) noise into the twin delayed deep deterministic policy gradient (TD3) algorithm. This method is applied to the tracking control of continuous stirred tank reactors (CSTRs). Initially, a mechanistic model of the CSTRs system is established to simulate its dynamic environment, enabling interaction between the TD3 agent and the system. To enhance exploration capabilities and convergence speed, fractional-order characteristics and a reward feedback mechanism are introduced into the OU noise, dynamically adjusting noise intensity to improve adaptability to complex states and optimize the exploration strategy of the TD3 algorithm. Furthermore, a well-designed reward function and optimized hyperparameters enable the agent to efficiently learn the optimal control policy, achieving high-precision tracking control of the CSTRs system. Simulation results demonstrate that the proposed TD3 algorithm outperforms conventional control methods, such as PID control and nonlinear model predictive control, as well as other DRL algorithms, including SAC and PPO, in terms of control accuracy and convergence speed.

Ye Wang, Yaling Deng, Ge Wang et al.

Modern Large Language Models (LLMs) exhibit complexity and granularity similar to humans in the field of natural language processing, challenging the boundaries between humans and machines in language understanding and creativity. However, whether the semantic network of LLMs is similar to humans is still unclear. We examined the representative closed-source LLMs, GPT-3.5-Turbo and GPT-4, with open-source LLMs, LLaMA-2-70B, LLaMA-3-8B, LLaMA-3-70B using semantic fluency tasks widely used to study the structure of semantic networks in humans. To enhance the comparability of semantic networks between humans and LLMs, we innovatively employed role-playing to generate multiple agents, which is equivalent to recruiting multiple LLM participants. The results indicate that the semantic network of LLMs has poorer interconnectivity, local association organization, and flexibility compared to humans, which suggests that LLMs have lower search efficiency and more rigid thinking in the semantic space and may further affect their performance in creative writing and reasoning.