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arXiv 2020年

作者： Al-Obiedollah, Haitham Cumanan, Kanapathippillai Thiyagalingam, Jeyarajan Tang, Jie Burr, Alister G. Ding, Zhiguo Dobre, Octavia A. Electrical Engineering Department Hashemite University Zarqa Jordan Department of Electronic Engineering University of York YorkYO10 5DD United Kingdom Scientific Computing Department of Rutherford Appleton Laboratory Science and Technology Facilities Council Harwell Campus Oxford United Kingdom School of Electronic and Information Engineering South China University of Technology Guangzhou China School of Electrical and Electronic Engineering University of Manchester Manchester United Kingdom Department of Electrical and Computer Engineering Memorial University St. JohnsNLA1B 3X5 Canada

Energy efficiency (EE) and spectral efficiency (SE) are two of the key performance metrics in future wireless networks, covering both design and operational requirements. For previous conventional resource allocation techniques, these two performance metrics have been considered in isolation, resulting in severe performance degradation in either of these metrics. Motivated by this problem, in this paper, we propose a novel beamforming design that jointly considers the trade-off between the two performance metrics in a multiple-input single-output non-orthogonal multiple access system. In particular, we formulate a joint SE-EE based design as a multi-objective optimization (MOO) problem to achieve a good trade-off between the two performance metrics. However, this MOO problem is not mathematically tractable and, thus, it is difficult to determine a feasible solution due to the conflicting objectives, where both need to be simultaneously optimized. To overcome this issue, we exploit a priori articulation scheme combined with the weighted sum approach. Using this, we reformulate the original MOO problem as a conventional single objective optimization (SOO) problem. In doing so, we develop an iterative algorithm to solve this non-convex SOO problem using the sequential convex approximation technique. Simulation results are provided to demonstrate the advantages and effectiveness of the proposed approach over the available beamforming designs. Copyright © 2020, The Authors. All rights reserved.

关键词： Beamforming

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IoT based engineering paradigm for real-time home automation

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AIP Conference Proceedings 2020年第1期2270卷

作者： M. Suguna S. J. Syed Ali Fathima D. Prakash G. Shobana 1Department of Computer Science and Engineering Kumaraguru College of Technology Coimbatore Tamilnadu India 2Department of EEE Veltech Multitech Engineering College Chennai India

Home automation system has reached popularity due to advancement in communication technology where appliances are controlled over internet. It has been developed to support elderly and disabled people. Smart phones are used to receive input which is processed by Google application. In this system, android has been used to capture input voices which are transferred to Ardunio Uno. The received input signals are processed using Bluetooth interfaced Ardunio Uno which is used to control fan and light using relay module. Android operating system is used to achieve automation. Mobile phone and Bluetooth communication protocol are used to control various devices. Home automation aims to supply improved comfort, convenience and security. It helps the elderly and disabled by providing quality of life instead of requiring caregivers or institutional care.

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DSADF: Thinking Fast and Slow for Decision Making

arXiv

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arXiv 2025年

作者： Dou, Alex Zhihao Cui, Dongfei Yan, Jun Wang, Weida Chen, Benteng Wang, Haoming Xie, Zeke Zhang, Shufei Pratt School of Engineering Duke University 103 Allen Bldg DurhamNC27708 United States School of Computer Science Northeast Electric Power University No. 169 Changchun Road Chuanying District Jilin Jilin132012 China Department of Information and Communication Engineering Tongji University No. 4800 Caoan Highway Shanghai Shanghai201804 China School of International Education Beijing University of Chemical Technology No. 15 Beisanhuan East Road Chaoyang District Beijing100029 China Department of Education Information Technology Faculty of Education East China Normal University 3667 North Zhongshan Road Changfeng Xincun Subdistrict Putuo District Pudong New Area Shanghai200062 China Duxue Road No.1 Nansha Guangdong Guangzhou511453 China Shanghai Artificial Intelligence Laboratory Chinese Academy of Science No. 1 Xueyang Road Pudong New Area Shanghai201304 China

Although Reinforcement Learning (RL) agents are effective in well-defined environments, they often struggle to generalize their learned policies to dynamic settings due to their reliance on trial-and-error interactions. Recent work has explored applying Large Language Models (LLMs) or Vision Language Models (VLMs) to boost the generalization of RL agents through policy optimization guidance or prior knowledge. However, these approaches often lack seamless coordination between the RL agent and the foundation model, leading to unreasonable decision-making in unfamiliar environments and efficiency bottlenecks. Making full use of the inferential capabilities of foundation models and the rapid response capabilities of RL agents and enhancing the interaction between the two to form a dual system is still a lingering scientific question. To address this problem, we draw inspiration from Kahneman’s theory of fast thinking (System 1) and slow thinking (System 2), demonstrating that balancing intuition and deep reasoning can achieve nimble decision-making in a complex world. In this study, we propose a Dual-System Adaptive Decision Framework (DSADF), integrating two complementary modules: System 1, comprising an RL agent and a memory space for fast and intuitive decision making, and System 2, driven by a VLM for deep and analytical reasoning. DSADF facilitates efficient and adaptive decision-making by combining the strengths of both systems. The empirical study in the video game environment: Crafter and Housekeep demonstrates the effectiveness of our proposed method, showing significant improvements in decision abilities for both unseen and known tasks. © 2025, CC BY.

关键词： Reinforcement learning

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Experimental Demonstration of Quantum Fully Homomorphic Encryption with Application in a Two-Party Secure Protocol

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Physical Review X 2020年第1期10卷 011038-011038页

作者： W. K. Tham Hugo Ferretti Kent Bonsma-Fisher Aharon Brodutch Barry C. Sanders Aephraim M. Steinberg Stacey Jeffery Department of Physics and Center for Quantum Information and Quantum Control University of Toronto 60 St. George Street Toronto Ontario M5S 1A7 Canada The Edward S. Rogers Department of Electrical and Computer Engineering University of Toronto 10 Kings College Road Toronto Ontario M5S 3G4 Canada Institute for Quantum Science and Technology University of Calgary Alberta T2N 1N4 Canada Institute for Quantum Information and Matter California Institute of Technology Pasadena California 91125 USA Canadian Institute for Advanced Research Toronto Ontario M5G 1M1 Canada QuSoft and CWI Amsterdam 1098 XG Netherlands

A fully homomorphic encryption system hides data from unauthorized parties while still allowing them to perform computations on the encrypted data. Aside from the straightforward benefit of allowing users to delegate computations to a more powerful server without revealing their inputs, a fully homomorphic cryptosystem can be used as a building block in the construction of a number of cryptographic functionalities. Designing such a scheme remained an open problem until 2009, decades after the idea was first conceived, and the past few years have seen the generalization of this functionality to the world of quantum machines. Quantum schemes prior to the one implemented here were able to replicate some features in particular use cases often associated with homomorphic encryption but lacked other crucial properties, for example, relying on continual interaction to perform a computation or leaking information about the encrypted data. We present the first experimental realization of a quantum fully homomorphic encryption scheme. To demonstrate the versatility of a a quantum fully homomorphic encryption scheme, we further present a toy two-party secure computation task enabled by our scheme.

关键词： Photon pairs & parametric down-conversion Quantum cryptography Quantum gates Quantum information with atoms & light

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Outcomes from a Workshop on a National Center for Quantum Education

arXiv

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arXiv 2024年

作者： Barnes, Edwin Bennett, Michael B. Boltasseva, Alexandra Borish, Victoria Brown, Bennett Carr, Lincoln D. Ceballos, Russell R. Dukes, Faith Easton, Emily W. Economou, Sophia E. Edwards, E.E. Finkelstein, Noah D. Fracchiolla, C. Franklin, Diana Freericks, J.K. Goss, Valerie Hannum, Mark Holincheck, Nancy Kelly, Angela M. Lanes, Olivia Lewandowski, H.J. Matsler, Karen Jo Mercurio, Emily Montaño, Inès Murdock, Maajida Peltz, Kiera Perron, Justin K. Richardson, Christopher J.K. Rosenberg, Jessica L. Ross, Richard S. Ryu, Minjung Samuel, Raymond E. Schrode, Nicole Schwamberger, Susan Searles, Thomas A. Singh, Chandralekha Tingle, Alexandra Zwickl, Benjamin M. Department of Physics Virginia Tech Center for Quantum Information Science and Engineering Virginia Tech 850 West Campus Drive BlacksburgVA24061 United States Q-SEnSE NSF QLCI University of Colorado 440 UCB BoulderCO80301 United States Elmore Family School of Electrical and Computer Engineering Birck Nanotechnology Center Purdue Quantum Science and Engineering Institute Purdue University 610 Purdue Mall West LafayetteIN47907 United States Quantum Science Center A National Quantum Information Science Research Center of The U.S. Department of Energy Oak Ridge National Laboratory 1 Bethel Valley Road Oak RidgeTN37830 United States JILA National Institute of Standards and Technology the University of Colorado BoulderCO80309 United States Department of Physics University of Colorado 390 UCB BoulderCO80309 United States QuSTEAM 510 Ronalds St. Iowa CityIA52245 United States Quantum Engineering Program Department of Physics Colorado School of Mines 1523 Illinois St. GoldenCO80401 United States QuSTEAM Non-Profit Wilbur Wright College City Colleges of Chicago 4300 N Narragansett Ave ChicagoIL60634 United States Government and Community Relations Office - K-12 Programs Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA United States Chicago Quantum Exchange 5235 S. Harper Court ChicagoIL60615 United States Department of Physics Virginia Tech 850 West Campus Drive BlacksburgVA24060 United States Department of Electrical and Computer Engineering Duke Quantum Center Duke University Duke University 130 Hudson Hall DurhamNC27708 United States American Physical Society 555 12th St. NW WashingtonDC20004 United States Computer Science University of Chicago 5730 S. Ellis Ave. ChicagoIL60637 United States Department of Physics Georgetown University 552 Reiss Science Building WashingtonDC20057 United States Chemistry Physics and Engineering Studies Chicago State University 9501 S. King Drive WSC 309 ChicagoIL606

In response to numerous programs seeking to advance quantum education and workforce development in the United States, experts from academia, industry, government, and professional societies convened for a National Science Foundation-sponsored workshop in February 2024 to explore the benefits and challenges of establishing a national center for quantum education. Broadly, such a center would foster collaboration and build the infrastructure required to develop a diverse and quantum-ready workforce. The workshop discussions centered around how a center could uniquely address gaps in public, K-12, and undergraduate quantum information science and engineering (QISE) education. Specifically, the community identified activities that, through a center, could lead to an increase in student awareness of quantum careers, boost the number of educators trained in quantum-related subjects, strengthen pathways into quantum careers, enhance the understanding of the U.S. quantum workforce, and elevate public engagement with QISE. Core proposed activities for the center include professional development for educators, coordinated curriculum development and curation, expanded access to educational laboratory equipment, robust evaluation and assessment practices, network building, and enhanced public engagement with quantum science. © 2024, CC BY.

关键词： Students

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User-configurable decision support system for clinical risk management

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Procedia computer Science 2021年 190卷 463-470页

作者： Elena Kleymenova Mikhail Dronov Liubov Yashina Elena Matrosova Svetlana Payushchik Maria Nigmatkulova Vitaly Otdelenov General Medical Center of the Bank of Russia Sevastopolsky pr.66 Moscow 117593 Russia Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences Vavilova str. 44 Moscow 119333 Russia Russian Medical Academy of Continuous Professional Education Barrikadnaya st. 2/1 Moscow 125993 Russia Faculty of Physics M.V. Lomonosov Moscow State University Leninskie Gory GSP-1 Moscow 119991 Russia Information Technology Department Moscow City University Vtoroy Selskohoziajstvenny proezd 4 Moscow 129226 Russia National Research Nuclear University MEPhI (Moscow Engineering Physics Institute) Kashirskoe shosse 31 Moscow 115409 Russia

The paper describes an application of relational structure-independent database for the design of user-configurable information system aimed at the intellectual support of clinical risk management in a hospital. The system represents a clinical decision support system (CDSS) and includes three modules: prevention of hospital-acquired adverse events (HA-AE), HA-AE prevention processes and audit of HA-AE cases. Thanks to flexible system configuration and graphic user interface, the clinical experts are able to compose complex knowledge artifacts directly working with the system, without engagement of knowledge engineers for supporting knowledge formalization or highly skilled professionals to ensure the physical layer performance. The successful practical approval of CDSS in a medical facility opens up prospects for a new level of cognitive support for diagnostic and treatment processes, patient safety improvement and healthcare costs reduction.

关键词： decision support system clinical risk management structure-independent database

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Efficiently Predicting Frequent Patterns over Uncertain Data Streams

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Procedia computer Science 2019年 160卷 15-22页

作者： Chuan-Ming Liu Kuan-Teng Liao Department of Computer Science and Information Engineering National Taipei University of Technology 1 Sec. 3 Zhongxiao E. Rd. Taipei 10608 Taiwan R.O.C.

In recent decades, frequent pattern mining has become popular in real time computing because it helps users extract meaningful business or technical trends over uncertain data streams. Generally, mainstream frequent pattern mining approaches can accurately explore frequent patterns whether the approaches are used in expected support or probabilistic frequentness mechanisms. However, whichever are implemented, they all have time issues when extracting frequent patterns if there exists a volume of data tuples in each time instance. To explore frequent patterns over uncertain data streams efficiently, we propose a predicted approach for forecasting frequent patterns with hidden Markov models. The model consists of a construction phase and a testing phase. In the former phase, the hidden Markov model is constructed by the history of past data, and in the latter phase, the results of evaluations and adjustments of transition probabilities occur. By constructing models of patterns, frequent patterns can be easily obtained. Experimental results show the proposed approach can save time cost when exploring frequent patterns and provide approximate 72% average accuracy in 50 sequential predictions; thereby, our approach can efficiently and accurately predict frequent patterns over uncertain data streams.

关键词： Hidden Markov model frequent pattern analysis uncertain data streaming computing

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Bio-supported of gold nanoparticles over magnetic nanoparticles: Synthesis, characterization and evaluation its catalytic activity for one-pot preparation of pyrano[2,3-d]pyrimidines

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Journal of Organometallic Chemistry 2025年

作者： Ali B.M. Ali Abdulsalam Abdulsattar Abdulazez Elyor Berdimurodov Narinderjit Singh Sawaran Singh Maher Ali Rusho Aseel Smerat M.A. Diab Heba A. El-Sabban Amir Ibrahim Ali Arabi Saiful Islam Air Conditioning Engineering Department College of Engineering University of Warith Al-Anbiyaa Karbala Iraq Medical Laboratory Techniques department College of Health and Medical Technology University of Al-maarif Anbar Iraq Faculty of Chemistry National University of Uzbekistan Tashkent 100034 Uzbekistan School of Medicine Central Asian University Tashkent 111221 Uzbekistan Faculty of Chemistry and Biology Karshi State University Karshi Uzbekistan Faculty of Data Science and Information Technology INTI International University Persiaran Perdana BBN Putra Nilai Nilai 71800 Malaysia Department of Lockheed Martin Engineering Management University of Colorado Boulder Boulder CO 80309 USA Faculty of Educational Sciences Al-Ahliyya Amman University Amman 19328 Jordan Computer Technologies Engineering Mazaya University College Nasiriyah Iraq Department of Biotechnology Yeungnam University Gyeongsan Gyeongbuk 38541 Republic of Korea Water Analysis Department Central Public Health Laboratories Ministry of Health and Population Cairo 11613 Egypt Integrated Materials Chemistry Laboratory School of Materials Science and Engineering Yeungnam University Gyeongsan 38541 Republic of Korea Analysis and Evaluation Department Egyptian Petroleum Research Institute (EPRI) 1 Ahmed El Zomor St. Nasr Cairo 11727 Egypt Mechanical Engineering Department College of Engineering King Khalid University Abha 61421 Saudi Arabia Civil Engineering Department College of Engineering King Khalid University Abha 61421 Saudi Arabia

In this research, we introduced the synthesis of bio-induced gold nanoparticles (Au NPs) utilizing Achillea millefolium extract to functionalize magnetic Fe 3 O 4 NPs. The extract of Achillea millefolium was used as a sustainable reducing agent and excels in preserving the stability of the synthesized NPs. The resulting Fe 3 O 4 @AMextract/Au nanocomposite underwent a thorough physicochemical characterization employing techniques such as FE-SEM, TEM, EDX, VSM, and ICP-OES. The heterogeneous Fe 3 O 4 @AMextract/Au catalyst exhibited remarkable catalytic efficiency in the production of pyrano[2,3-d]pyrimidines via a three-component reaction that included barbituric acid, aryl aldehydes, and malononitrile. Furthermore, the Fe 3 O 4 @AMextract/Au catalyst demonstrated commendable recyclability, sustaining its catalytic efficacy after being utilized seven times with minimal reduction in performance.

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Learning of long-horizon sparse-reward robotic manipulator tasks with base controllers

arXiv

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arXiv 2020年

作者： Wang, Guangming Xin, Minjian Wu, Wenhua Liu, Zhe Wang, Hesheng Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China Department of Computer Science and Engineering University of California San Diego La Jolla CA92093 United States Department of Computer Science and Technology University of Cambridge CambridgeCB2 1TN United Kingdom

Deep Reinforcement Learning (DRL) enables robots to perform some intelligent tasks end-to-end. However, there are still many challenges for long-horizon sparse-reward robotic manipulator tasks. On the one hand, a sparse-reward setting causes exploration inefficient. On the other hand, exploration using physical robots is of high cost and unsafe. In this paper, we propose a method of learning long-horizon sparse-reward tasks utilizing one or more existing traditional controllers named base controllers in this paper. Built upon Deep Deterministic Policy Gradients (DDPG), our algorithm incorporates the existing base controllers into stages of exploration, value learning, and policy update. Furthermore, we present a straightforward way of synthesizing different base controllers to integrate their strengths. Through experiments ranging from stacking blocks to cups, it is demonstrated that the learned state-based or image-based policies steadily outperform base controllers. Compared to previous works of learning from demonstrations, our method improves sample efficiency by orders of magnitude and improves the performance. Overall, our method bears the potential of leveraging existing industrial robot manipulation systems to build more flexible and intelligent controllers. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Data-driven model-based detection of malicious insiders via physical access logs

Data-driven model-based detection of malicious insiders via ...

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作者： Cheh, Carmen Thakore, Uttam Fawaz, Ahmed Chen, Binbin Temple, William G. Sanders, William H. Department of Computer Science University of Illinois UrbanaIL61801 United States Information Trust Institute University of Illinois UrbanaIL61801 United States Singapore University of Technology and Design 8 Somapah Road Singapore487372 Singapore Advanced Digital Sciences Center 1 Create Way Singapore138602 Singapore Department of Electrical and Computer Engineering University of Illinois UrbanaIL61801 United States

The risk posed by insider threats has usually been approached by analyzing the behavior of users solely in the cyber domain. In this article, we show the viability of using physical movement logs, collected via a building access control system, together with an understanding of the layout of the building housing the system's assets, to detect malicious insider behavior that manifests itself in the physical domain. In particular, we propose a systematic framework that uses contextual knowledge about the system and its users, learned from historical data gathered from a building access control system, to select suitable models for representing movement behavior. We suggest two different models of movement behavior in this article and evaluate their ability to represent normal user movement. We then explore the online usage of the learned models, together with knowledge about the layout of the building being monitored, to detect malicious insider behavior. Finally, we show the effectiveness of the developed framework using real-life data traces of usermovement in railway transit stations. © 2019 Copyright held by the owner/author(s).

关键词： Intrusion detection

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