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1 |
iRestroom : A smart restroom cyberinfrastructure for elderly people
iRestroom: زیرساخت سایبری سرویس بهداشتی هوشمند برای افراد مسن-2022 According to a report by UN and WHO, by 2030 the number of senior people (age over 65) is
projected to grow up to 1.4 billion, and which is nearly 16.5% of the global population. Seniors
who live alone must have their health state closely monitored to avoid unexpected events (such as
a fall). This study explains the underlying principles, methodology, and research that went into
developing the concept, as well as the need for and scopes of a restroom cyberinfrastructure
system, that we call as iRestroom to assess the frailty of elderly people for them to live a
comfortable, independent, and secure life at home. The proposed restroom idea is based on the
required situations, which are determined by user study, socio-cultural and technological trends,
and user requirements. The iRestroom is designed as a multi-sensory place with interconnected
devices where carriers of older persons can access interactive material and services throughout
their everyday activities. The prototype is then tested at Texas A&M University-Kingsville. A Nave
Bayes classifier is utilized to anticipate the locations of the sensors, which serves to provide a
constantly updated reference for the data originating from numerous sensors and devices installed
in different locations throughout the restroom. A small sample of pilot data was obtained, as well
as pertinent web data. The Institutional Review Board (IRB) has approved all the methods. keywords: اینترنت اشیا | حسگرها | نگهداری از سالمندان | سیستم های هوشمند | یادگیری ماشین | IoT | Sensors | Elder Care | Smart Systems | Machine Learning |
مقاله انگلیسی |
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Evolution of Quantum Computing: Theoretical and Innovation Management Implications for Emerging Quantum Industry
تکامل محاسبات کوانتومی: مفاهیم مدیریت نظری و نوآوری برای صنعت کوانتومی در حال ظهور-2022 Quantum computing is a vital research field in science
and technology. One of the fundamental questions hardly known
is how quantum computing research is developing to support scientific advances and the evolution of path-breaking technologies
for economic, industrial, and social change. This study confronts
the question here by applying methods of computational scientometrics for publication analyses to explain the structure and
evolution of quantum computing research and technologies over
a 30-year period. Results reveal that the evolution of quantum
computing from 1990 to 2020 has a considerable average increase of
connectivity in the network (growth of degree centrality measure),
a moderate increase of the average influence of nodes on the flow
between nodes (little growth of betweenness centrality measure),
and a little reduction of the easiest access of each node to all other
nodes (closeness centrality measure). This evolutionary dynamics
is due to the increase in size and complexity of the network in
quantum computing research over time. This study also suggests
that the network of quantum computing has a transition from
hardware to software research that supports accelerated evolution
of technological pathways in quantum image processing, quantum
machine learning, and quantum sensors. Theoretical implications
of this study show the morphological evolution of the network in
quantum computing from a symmetric to an asymmetric shape
driven by new inter-related research fields and emerging technological trajectories. Findings here suggest best practices of innovation
management based on R&D investments in new technological directions of quantum computing having a high potential for growth
and impact in science and markets.
Index Terms: Innovation management | quantum algorithms | quantum computing (QC) | quantum network | technological change | technological paradigm | technological trajectories. |
مقاله انگلیسی |
3 |
High-Performance Reservoir Computing With Fluctuations in Linear Networks
محاسبات مخزن با کارایی بالا با نوسانات در شبکه های خطی-2022 Reservoir computing has emerged as a powerful
machine learning paradigm for harvesting nontrivial information
processing out of disordered physical systems driven by sequential inputs. To this end, the system observables must become
nonlinear functions of the input history. We show that encoding
the input to quantum or classical fluctuations of a network of
interacting harmonic oscillators can lead to a high performance
comparable to that of a standard echo state network in several
nonlinear benchmark tasks. This equivalence in performance
holds even with a linear Hamiltonian and a readout linear in the
system observables. Furthermore, we find that the performance of
the network of harmonic oscillators in nonlinear tasks is robust to
errors both in input and reservoir observables caused by external
noise. For any reservoir computing system with a linear readout,
the magnitude of trained weights can either amplify or suppress
noise added to reservoir observables. We use this general result to
explain why the oscillators are robust to noise and why having
precise control over reservoir memory is important for noise
robustness in general. Our results pave the way toward reservoir
computing harnessing fluctuations in disordered linear systems.
Index Terms: Dynamical systems | machine learning | quantum mechanics | recurrent neural networks | reservoir computing | supervised learning. |
مقاله انگلیسی |
4 |
How to Build a Scalable Quantum Controller
چگونه یک کنترلر کوانتومی مقیاس پذیر بسازیم-2022 We discuss quantum computers from the
perspective of one of their major building
blocks, the hardware controller, explaining how
it fits into the computer and the requirements
and challenges it poses for engineers and
scientists.
|
مقاله انگلیسی |
5 |
VisuaLizations As Intermediate Representations (VLAIR): An approach for applying deep learning-based computer vision to non-image-based data
تجسم ها به عنوان بازنمایی های میانی (VLAIR): رویکردی برای به کارگیری بینایی کامپیوتری مبتنی بر یادگیری عمیق برای داده های غیر مبتنی بر تصویر-2022 Deep learning algorithms increasingly support automated systems in areas such as human activity
recognition and purchase recommendation. We identify a current trend in which data is transformed
first into abstract visualizations and then processed by a computer vision deep learning pipeline. We
call this VisuaLization As Intermediate Representation (VLAIR) and believe that it can be instrumental
to support accurate recognition in a number of fields while also enhancing humans’ ability to
interpret deep learning models for debugging purposes or for personal use. In this paper we describe
the potential advantages of this approach and explore various visualization mappings and deep
learning architectures. We evaluate several VLAIR alternatives for a specific problem (human activity
recognition in an apartment) and show that VLAIR attains classification accuracy above classical
machine learning algorithms and several other non-image-based deep learning algorithms with several
data representations.
keywords: تجسم اطلاعات | شبکه های عصبی کانولوشنال | تشخیص فعالیت های انسانی | خانه های هوشمند | بازنمایی داده ها | نمایندگی های میانی | تفسیر پذیری | یادگیری ماشین | یادگیری عمیق | Information visualization | Convolutional neural networks | Human activity recognition | Smart homes | Data representation | Intermediate representations | Interpretability | Machine learning | Deep learning |
مقاله انگلیسی |
6 |
Artificial intelligence versus natural selection: Using computer vision techniques to classify bees and bee mimics
هوش مصنوعی در مقابل انتخاب طبیعی: استفاده از تکنیکهای بینایی کامپیوتری برای طبقهبندی زنبورها و تقلیدهای زنبور عسل-2022 Many groups of stingless insects have independently evolved mimicry of bees to fool would-be predators. To investigate this mimicry, we trained artificial intelligence (AI) algorithms—specifically, computer vision—to classify citizen scientist images of bees, bumble bees, and diverse bee mimics. For detecting bees and bumble bees, our models achieved accuracies of and , respectively. As a proxy for a natural predator, our models were poorest in detecting bee mimics that exhibit both aggressive and defensive mimicry. Using the explainable AI method of class activation maps, we validated that our models learn from appropriate components within the image, which in turn provided anatomical insights. Our t-SNE plot yielded perfect within-group clustering, as well as between-group clustering that grossly replicated the phylogeny. Ultimately, the transdisciplinary approaches herein can enhance global citizen science efforts as well as investigations of mimicry and morphology of bees and other insects.
keywords: Artificial intelligence | Bioinformatics | Computing methodology | Entomology | Zoology |
مقاله انگلیسی |
7 |
Quantum Pythagorean Fuzzy Evidence Theory: A Negation of Quantum Mass Function View
نظریه شواهد فازی کوانتومی فیثاغورث: نفی عملکرد جرم کوانتومی-2022 Dempster–Shafer (D-S) evidence theory is an effective methodology to handle unknown and imprecise information
because it can assign probability into the power set. However, the
process of obtaining information is a complex task, which can consider the rational, conscious, objective evaluation of utility with behavioral effects. Besides, in most cases, information can be obtained
from different angles at the same time. The quantum model of mass
function (QM) uses amplitude and phase angle to easily express
those properties of information that can extend D-S evidence theory
to the unit circle in a complex plane. Moreover, everything in nature
will have its opposite, which is a kind of universality. The Bayes
theorem is essentially the process of negation. However, in most
cases, decisions can be made by only fully considering the known
information without considering the other side of the information.
Hence, considering the negation of information is a question to
be investigated deeply, which can analyze information from the
other point. This article proposes negation of QM by using the
subtraction of vectors in the unit circle, which can degenerate into
negation proposed by Yager in standard probability theory and
negation proposed by Yin et al. in D-S evidence theory. Negation
can provide us more information to consider the problem from
both positive and negative aspects. In this article, negation can be
understood information, which does not belong to event A, that is
to say, negation can be regarded as nonmembership by using the
fuzzy terms. Based on the above discussion, this article proposes the
quantum pythagorean fuzzy evidence theory (QPFET), which is the
novel work to consider QPFET from the point of negation. Besides,
there are some numerical examplesto explainthe proposed method.
In order to explore the applications of QPFET, this article discusses
the possibility ofthe VIsˇekriterijumskoKompromisno Rangiranje
method underQPFETto handle multicriteria decision-makingthat
enables us to capture 2-D data, considering not only amplitude but
also phase angle.
IndexTerms— Dempster–Shafer(D-S) evidencetheory | negation | pythagorean fuzzy sets (PFSs) | quantum mass function | quantum pythagorean fuzzy evidence theory (QPFET). |
مقاله انگلیسی |
8 |
برهم کنش متقابل جهت گیری ها نشاندهنده رمز گشایی سطح بالا به پایین در حافظه کار بصری است
سال انتشار: 2022 - تعداد صفحات فایل pdf انگلیسی: 12 - تعداد صفحات فایل doc فارسی: 41 کدگذاری حسی ( چگونه محرکها واکنشهای حسی را برمیانگیزد ) به پیشرفت از ویژگیهای سطح پایین به سطح بالا مشهور است .
رمزگشایی ( چگونه پاسخها منجر به ادراک میشود ) کمتر درک میشود اما اغلب فرض میشود که از همان سلسلهمراتب پیروی میکند .
بر این اساس ، رمز گشایی جهت گیری باید در نواحی سطح پایین مانند V۱ ، بدون برهم کنش متقابل رخ دهد .
با این حال , یک مطالعه , دینگ ,کوا , تی سودیکس , و کان ( 2017 ) شواهدی در برابر این فرض ارائه دادند و پیشنهاد کردند که رمزگشایی بصری اغلب ممکن است از سلسلهمراتب سطح بالا به پایین در حافظه کاری پیروی کند , که در آن محدودیتهای سطح به پایین تعامل بین ویژگیهای سطح پایینتر را ایجاد میکند . اگر دو جهت گیری در جهت مخالف تثبیت هر دو عملی هستند و حافظه فعال را وارد میکنند , پس باید با هم تعامل داشته باشند. ما در واقع هم برهم کنش متقابل پیشبینیشده ( تنفر و همبستگی ) بین جهت گیری ها را پیدا کردیم .
آزمایشها کنترل و تجزیه و تحلیلهای کنترلی , توضیحات دیگری همچون تعصب گزارش دهی و انطباق در سراسر آزمایشها در همان سمت تثبیت را رد کردند . به علاوه , ما دادهها را با استفاده از چارچوب رمزگشایی Bayesian سطح پایین به سطح پایین توضیح دادیم .
واژه های کاربردی: کدگشایی بصری | جانبداری | سر و صدا | بیزین گذشته نگر |
مقاله ترجمه شده |
9 |
The Present and Future of Discrete Logarithm Problems on Noisy Quantum Computers
حال و آینده مسائل لگاریتم گسسته در کامپیوترهای کوانتومی پر سر و صدا-2022 The discrete logarithm problem (DLP) is the basis for several cryptographic primitives. Since
Shor’s work, it has been known that the DLP can be solved by combining a polynomial-size quantum circuit
and a polynomial-time classical postprocessing algorithm. The theoretical result corresponds the situation
where a quantum device working with a medium number of qubits of very small errors can solve the DLP.
However, all the quantum devices that we can use have a limited number of noisy qubits, as of the noisy
intermediate-scale quantum (NISQ) era. Thus, evaluating the instance size that the latest quantum device can
solve and giving a future prediction of the size along the progress of quantum devices are emerging research
topics. This article contains two proposals to discuss the performance of quantum devices against the DLP in
the NISQ era: 1) a quantitative measure based on the success probability of the postprocessing algorithm to
determine whether an experiment on a quantum device (or a classical simulator) succeeded; and 2) a procedure to modify bit strings observed from a Shor’s circuit to increase the success probability of a lattice-based
postprocessing algorithm. In this article, we conducted our experiments with the ibm_kawasaki device
and discovered that the simplest circuit (7 qubits) from a 2-bit DLP instance achieves a sufficiently high
success probability to proclaim the experiment successful. Experiments on another circuit from a slightly
harder 2-bit DLP instance, on the other hand, did not succeed, and we determined that reducing the noise
level by half is required to achieve a successful experiment. Finally, we give a near-term prediction based on
required noise levels to solve some selected small DLPs and integer factoring instances.
INDEX TERMS: Discrete logarithm problem (DLP) | IBM quantum | lattice | postprocessing method | Shor’s algorithm. |
مقاله انگلیسی |
10 |
فعل و انفعالات فیکساسیون متقاطع جهت ها، پیشنهاد کدگشایی سطح بالا به پایین در حافظه کاری بصری
سال انتشار: 2022 - تعداد صفحات فایل pdf انگلیسی: 12 - تعداد صفحات فایل doc فارسی: 37 کدگذاری حسی (چگونگی برانگیختن پاسخ های حسی توسط محرک ها) به پیشرفت از ویژگی های سطح پایین به سطح بالا معروف است. کمتر به فهم و درک کدگشایی (چگونه پاسخ ها منجر به ادراک می شوند) پرداخته شده است اما اغلب فرض می شود که از سلسله مراتب مشابهی پیروی می کند. بر این اساس، کدگشایی جهت باید در مناطق سطح پایین مانند V1، بدون فعل و انفعالات فیکساسیون متقابل رخ دهد. با این حال، در مطالعه ی Ding, Cueva, Tsodyks, and Qian (2017) شواهدی برخلاف این فرض ارائه شد و آنها پیشنهاد کردند که کدگشایی بصری اغلب از سلسله مراتبی از سطح بالا به سطح پایین در حافظه کاری پیروی می کند، که در آن محدودیتهای سطح از بالاتر به پایینتر ، تعامل بین ویژگیهای سطح پایینتر را معرفی میکنند. دو جهت در سویه مخالف فیکساسیون، هم مربوط به کار هستند و هم حافظه کاری می و باید با یکدیگر تعامل داشته باشند. در واقع فعل و انفعالات فیکساسیون متقابل پیش بینی شده (دفعه و همبستگی) بین جهت ها را پیدا کرده. کارآزماییها و تجزیه و تحلیلهای کنترلی، توضیحات جایگزین مانند گزارش سوگیری و انطباق در سراسر کارآزماییها را در جهت مشابه فیکساسیون، رد کردند. علاوه بر این، دادهها را با استفاده از چارچوب کدگشایی بیزی سطح بالا به پایین گذشتهنگر شرح دادیم.
کلیدواژه ها: کدگشایی بصری | سوگیری ادراکی | نویز حافظه | گذشته نگر بیزی |
مقاله ترجمه شده |