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تعداد مقالات یافته شده: 740
ردیف عنوان نوع
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
2 DOPIV: Post-Quantum Secure Identity-Based Data Outsourcing with Public Integrity Verification in Cloud Storage
DOPIV: برون سپاری داده مبتنی بر هویت امن پس از کوانتومی با تأیید صحت عمومی در فضای ذخیره سازی ابری-2022
Public verification enables cloud users to employ a third party auditor (TPA) to check the data integrity. However, recent breakthrough results on quantum computers indicate that applying quantum computers in clouds would be realized. A majority of existing public verification schemes are based on conventional hardness assumptions, which are vulnerable to adversaries equipped with quantum computers in the near future. Moreover, new security issues need to be solved when an original data owner is restricted or cannot access the remote cloud server flexibly. In this paper, we propose an efficient identity-based data outsourcing with public integrity verification scheme (DOPIV) in cloud storage. DOPIV is designed on lattice-based cryptography, which achieves post-quantum security. DOPIV enables an original data owner to delegate a proxy to generate the signatures of data and outsource them to the cloud server. Any TPA can perform data integrity verification efficiently on behalf of the original data owner, without retrieving the entire data set. Additionally, DOPIV possesses the advantages of being identity-based systems, avoiding complex certificate management procedures. We provide security proofs of DOPIV in the random oracle model, and conduct a comprehensive performance evaluation to show that DOPIV is more practical in post-quantum secure cloud storage systems.
Index Terms: Cloud storage | public verification | lattice-based cryptography | identity-based data outsourcing | post-quantum security
مقاله انگلیسی
3 Efficient Construction of a Control Modular Adder on a Carry-Lookahead Adder Using Relative-Phase Toffoli Gates
ساخت کارآمد یک جمع کننده ماژولار کنترلی بر روی جمع کننده Carry-Lookahead با استفاده از گیت های توفولی فاز نسبی-2022
Control modular addition is a core arithmetic function, and we must consider the computational cost for actual quantum computers to realize efficient implementation. To achieve a low computational cost in a control modular adder, we focus on minimizing KQ (where K is the number of logical qubits required by the algorithm, and Q is the elementary gate step), defined by the product of the number of qubits and the depth of the circuit. In this article, we construct an efficient control modular adder with small KQ by using relative-phase Toffoli gates in two major types of quantum computers: fault-tolerant quantum computers (FTQ) on the logical layer and noisy intermediate-scale quantum computers (NISQ). We give a more efficient construction compared with Van Meter and Itoh’s, based on a carry-lookahead adder. In FTQ, T gates incur heavy cost due to distillation, which fabricates ancilla for running T gates with high accuracy but consumes a lot of especially prepared ancilla qubits and a lot of time. Thus, we must reduce the number of T gates. We propose a new control modular adder that uses only 20% of the number of T gates of the original. Moreover, when we take distillation into consideration, we find that we minimize KQT (the product of the number of qubits and T-depth) by running (n/√log n) T gates simultaneously. In NISQ, cnot gates are the major error source. We propose a new control modular adder that uses only 35% of the number of cnotgates of the original. Moreover, we show that the KQCX (the product of the number of qubits and cnot-depth) of our circuit is 38% of the original. Thus, we realize an efficient control modular adder, improving prospects for the efficient execution of arithmetic in quantum computers.
INDEX TERMS: Carry-lookahead adder | control modular adder | fault-tolerant quantum computers (FTQ) | noisy intermediate-scale quantum computers (NISQ) | Shor’s algorithm.
مقاله انگلیسی
4 High-accuracy in the classification of butchery cut marks and crocodile tooth marks using machine learning methods and computer vision algorithms
دقت بالا در طبقه بندی علائم برش قصابی و علائم دندان تمساح با استفاده از روش های یادگیری ماشین و الگوریتم های بینایی کامپیوتری-2022
Some researchers using traditional taphonomic criteria (groove shape and presence/absence of microstriations) have cast some doubts about the potential equifinality presented by crocodile tooth marks and stone tool butchery cut marks. Other researchers have argued that multivariate methods can efficiently separate both types of marks. Differentiating both taphonomic agents is crucial for determining the earliest evidence of carcass processing by hominins. Here, we use an updated machine learning approach (discarding artificially bootstrapping the original imbalanced samples) to show that microscopic features shaped as categorical variables, corresponding to intrinsic properties of mark structure, can accurately discriminate both types of bone modifications. We also implement new deep-learning methods that objectively achieve the highest accuracy in differentiating cut marks from crocodile tooth scores (99% of testing sets). The present study shows that there are precise ways of differentiating both taphonomic agents, and this invites taphonomists to apply them to controversial paleontological and archaeological specimens.
keywords: تافونومی | علائم برش | علائم دندان | فراگیری ماشین | یادگیری عمیق | شبکه های عصبی کانولوشنال | قصابی | Taphonomy | Cut marks | Tooth marks | Machine learning | Deep learning | Convolutional neural networks | Butchery
مقاله انگلیسی
5 General Mixed-State Quantum Data Compression With and Without Entanglement Assistance
فشرده سازی داده های کوانتومی حالت مخلوط عمومی با و بدون کمک درهم تنیدگی-2022
We consider the most general finite-dimensional quantum mechanical information source, which is given by a quantum system A that is correlated with a reference system R. The task is to compress A in such a way as to reproduce the joint source state ρAR at the decoder with asymptotically high fidelity. This includes Schumacher’s original quantum source coding problem of a pure state ensemble and that of a single pure entangled state, as well as general mixed state ensembles. Here, we determine the optimal compression rate (in qubits per source system) in terms of the Koashi-Imoto decomposition of the source into a classical, a quantum, and a redundant part. The same decomposition yields the optimal rate in the presence of unlimited entanglement between compressor and decoder, and indeed the full region of feasible qubit-ebit rate pairs.
keywords: Quantum information | source coding | entanglement.
مقاله انگلیسی
6 Pork primal cuts recognition method via computer vision
روش تشخیص برش های اولیه گوشت خوک از طریق بینایی کامپیوتری-2022
Pork accounts for more than 33% of global meat consumption and dominates meat consumption in China. With the improvement of peoples quality of life, people pay more and more attention to the quality of pork. There are many factors that affect the quality of pork, and the cutting position of pork is also one of them. The quality of different pork primal cuts varies greatly. Aiming at the difficulty of distinguishing pork primal cuts, this study proposes a computer vision-based method to identify different pork primal cuts, using images of four different pork primal cuts (ham, loin, belly, and neck) as the experimental data, the results show that the method proposed in this paper can identify the original cuts of pork well. It also proves that computer vision technology has the potential to help people identify pork cuts.
keywords: برش های اولیه گوشت خوک | شناسایی برش گوشت خوک | بینایی کامپیوتر | تشخیص برش های اولیه | Pork primal cuts | Identifying pork cut | Computer vision | Primal cuts recognition
مقاله انگلیسی
7 Implementing Graph-Theoretic Feature Selection by Quantum Approximate Optimization Algorithm
پیاده سازی انتخاب ویژگی گراف-نظری توسط الگوریتم بهینه سازی تقریبی کوانتومی-2022
Feature selection plays a significant role in computer science; nevertheless, this task is intractable since its search space scales exponentially with the number of dimensions. Motivated by the potential advantages of near-term quantum computing, three graph-theoretic feature selection (GTFS) methods, including minimum cut (MinCut)-based, densest k -subgraph (DkS)-based, and maximal-independent set/minimal vertex cover (MIS/MVC)-based, are investigated in this article, where the original graph-theoretic problems are naturally formulated as the quadratic problems in binary variables and then solved using the quantum approximate optimization algorithm (QAOA). Specifically, three separate graphs are created from the raw feature set, where the vertex set consists of individual features and pairwise measure describes the edge. The corresponding feature subset is generated by deriving a subgraph from the established graph using QAOA. For the above three GTFS approaches, the solving procedure and quantum circuit for the corresponding graph-theoretic problems are formulated with the framework of QAOA. In addition, those proposals could be employed as a local solver and integrated with the Tabu search algorithm for solving large-scale GTFS problems utilizing limited quantum bit resource. Finally, extensive numerical experiments are conducted with 20 publicly available datasets and the results demonstrate that each model is superior to its classical scheme. In addition, the complexity of each model is only O(pn2) even in the worst cases, where p is the number of layers in QAOA and n is the number of features.
Index Terms: Feature selection | graph theory | parameterized quantum circuit | quantum approximation optimization algorithm | quantum computing.
مقاله انگلیسی
8 Performance analysis of machine learning algorithm of detection and classification of brain tumor using computer vision
تحلیل عملکرد الگوریتم یادگیری ماشین تشخیص و طبقه بندی تومور مغزی با استفاده از بینایی کامپیوتر-2022
Brain tumor is one of the undesirables, uncontrolled growth of cells in all age groups. Classification of tumors depends no its origin and degree of its aggressiveness, it also helps the physician for proper diagnosis and treatment plan. This research demonstrates the analysis of various state-of-art techniques in Machine Learning such as Logistic, Multilayer Perceptron, Decision Tree, Naive Bayes classifier and Support Vector Machine for classification of tumors as Benign and Malignant and the Discreet wavelet transform for feature extraction on the synthetic data that is available data on the internet source OASIS and ADNI. The research also reveals that the Logistic Regression and the Multilayer Perceptron gives the highest accuracy of 90%. It mimics the human reasoning that learns, memorizes and is capable of reasoning and performing parallel computations. In future many more AI techniques can be trained to classify the multimodal MRI Brain scan to more than two classes of tumors.
keywords: هوش مصنوعی | ام آر آی | رگرسیون لجستیک | پرسپترون چند لایه | Artificial Intelligence | MRI | Logistic regression | OASIS | Multilayer Perceptron
مقاله انگلیسی
9 Parameterized Hamiltonian Learning With Quantum Circuit
یادگیری همیلتونی پارامتری شده با مدار کوانتومی-2022
Hamiltonian learning, as an important quantum machine learning technique, provides a significant approach for determining an accurate quantum system. This paper establishes parameterized Hamiltonian learning (PHL) and explores its application and implementation on quantum computers. A parameterized quantum circuit for Hamiltonian learning is first created by decomposing unitary operators to excite the system evolution. Then, a PHL algorithm is developed to prepare a specific Hamiltonian system by iteratively updating the gradient of the loss function about circuit parameters. Finally, the experiments are conducted on Origin Pilot, and it demonstrates that the PHL algorithm can deal with the image segmentation problem and provide a segmentation solution accurately. Compared with the classical Grabcut algorithm, the PHL algorithm eliminates the requirement of early manual intervention. It provides a new possibility for solving practical application problems with quantum devices, which also assists in solving increasingly complicated problems and supports a much wider range of application possibilities in the future.
Index Terms: Quantum machine learning | Parameterized Hamiltonian learning (PHL) | parameterized quantum circuit | Hamiltonian learning algorithm | Image segmentation
مقاله انگلیسی
10 Quantum Annealing Methods and Experimental Evaluation to the Phase-Unwrapping Problem in Synthetic Aperture Radar Imaging
روش‌های آنیل کوانتومی و ارزیابی تجربی مسئله بازکردن فاز در تصویربرداری رادار دیافراگم مصنوعی-2022
The focus of this work is to explore the use of quantum annealing solvers for the problem of phase unwrapping of synthetic aperture radar (SAR) images. Although solutions to this problem exist based on network programming, these techniques do not scale well to larger sized images. Our approach involves formulating the problem as a quadratic unconstrained binary optimization (QUBO) problem, which can be solved on a quantum annealer. Given that present embodiments of quantum annealers remain limited in the number of qubits they possess, we decompose the problem into a set of subproblems that can be solved individually. These individual solutions are close to optimal up to an integer constant, with one constant per subimage. In a second phase, these integer constants are determined as a solution to yet another QUBO problem. This basic idea is extended to several passes, where each pass results in an image which is subsequently decomposed to yet another set of subproblems until the resulting image can be accommodated by the annealer at hand. Additionally, we explore improvements to the method by decomposing the original image into overlapping subimages and ignoring the results on the overlapped (marginal) pixels. We test our approach with a variety of software-based QUBO solvers and on a variety of images, both synthetic and real. Additionally, we experiment using D-wave systems’ quantum annealer, the D-wave 2000Q_6 and developed an embedding method which, for our problem, yielded improved results. Our method resulted in high quality solutions, comparable to state-of-the-art phase-unwrapping solvers.
INDEX TERMS: Interferometric synthetic aperture radar (SAR) | phase unwrapping, quadratic unconstrained binary optimization (QUBO) | quantum annealing.
مقاله انگلیسی
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