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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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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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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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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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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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Distributed resource scheduling for large-scale MEC systems: A multi-agent ensemble deep reinforcement learning with imitation acceleration

arXiv

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arXiv 2020年

作者： Jiang, Feibo Dong, Li Wang, Kezhi Yang, Kun Pan, Cunhua Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Changsha China Department of Computer and Information Sciences Northumbria University United Kingdom School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom

We consider the optimization of distributed resource scheduling to minimize the sum of task latency and energy consumption for all the Internet of things devices (IoTDs) in a large-scale mobile edge computing (MEC) system. To address this problem, we propose a distributed intelligent resource scheduling (DIRS) framework, which includes centralized training relying on the global information and distributed decision making by each agent deployed in each MEC server. More specifically, we first introduce a novel multi-agent ensemble-assisted distributed deep reinforcement learning (DRL) architecture, which can simplify the overall neural network structure of each agent by partitioning the state space and also improve the performance of a single agent by combining decisions of all the agents. Secondly, we apply action refinement to enhance the exploration ability of the proposed DIRS framework, where the near-optimal state-action pairs are obtained by a novel Lvy flight search. Finally, an imitation acceleration scheme is presented to pre-train all the agents, which can significantly accelerate the learning process of the proposed framework through learning the professional experience from a small amount of demonstration data. Extensive simulations are conducted to demonstrate that the proposed DIRS framework is efficient and outperforms the existing benchmark schemes. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Stacked auto encoder based Deep Reinforcement Learning for online resource scheduling in large-scale MEC networks

arXiv

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arXiv 2020年

作者： Jiang, Feibo Wang, Kezhi Dong, Li Pan, Cunhua Yang, Kun Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China Department of Computer and Information Sciences Northumbria University United Kingdom Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Changsha China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom University of Electronic Science and Technology of China Chengdu China

An online resource scheduling framework is proposed for minimizing the sum of weighted task latency for all the mobile users, by optimizing offloading decision, transmission power, and resource allocation in the mobile edge computing (MEC) system. Towards this end, a deep reinforcement learning (DRL) method is proposed to obtain an online resource scheduling policy. Firstly, a related and regularized stacked auto encoder (2r-SAE) with unsupervised learning is proposed to perform data compression and representation for high dimensional channel quality information (CQI) data, which can reduce the state space for DRL. Secondly, we present an adaptive simulated annealing based approach (ASA) as the action search method of DRL, in which an adaptive h-mutation is used to guide the search direction and an adaptive iteration is proposed to enhance the search efficiency during the DRL process. Thirdly, a preserved and prioritized experience replay (2p-ER) is introduced to assist the DRL to train the policy network and find the optimal offloading policy. Numerical results are provided to demonstrate that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computational time compared with existing benchmarks. It also shows that the proposed framework is suitable for resource scheduling problem in large-scale MEC networks, especially in the dynamic environment. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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