Build a mining rig 2021 goda

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Acknowledgements


Multimedia data and information management is an important task according to the development of media processing technology. Multimedia is a useful resource that people understand complex situations such as the elderly care domain. Appropriate annotation is beneficial in several tasks of information management, such as storing, retrieval, and summarization of data, from a semantic perspective.

However, the metadata annotation for multimedia data remains problematic because metadata is obtained as a result of interpretation depending on domain-specific knowledge, and it needs well-controlled and comprehensive vocabulary for annotation. In this study, we proposed a collaborative methodology for developing ontologies and annotation with domain experts. The method includes 1 classification of knowledge types for collaborative construction of annotation data, 2 division of tasks among a team composed of domain experts, ontology engineers, and annotators, and 3 incremental approach to ontology development.

We applied the proposed method to 11 videos on elderly care domain for the confirmation of its feasibility.

We focused on annotation of actions occurring in these videos, thereby the annotated data is used as a support in evaluating staff skills. The application results show the content in the ontology during annotation increases monotonically. This study concludes by presenting lessons learnt from the case studies. With the development of cameras and sensors and the spread of cloud computing, life logs can be easily acquired and stored in general households for the various services that utilize the logs.

However, it is difficult to analyze moving images that are acquired by home sensors in real time using machine learning because the data size is too large and the computational complexity is too high.

Moreover, collecting and accumulating in the cloud moving images that are captured at home and can be used to identify individuals may invade the privacy of application users. We propose a method of distributed processing over the edge and cloud that addresses the processing latency and the privacy concerns. On the edge sensor side, we extract feature vectors of human key points from moving images using OpenPose, which is a pose estimation library. On the cloud side, we recognize actions by machine learning using only the feature vectors.

In this study, we compare the action recognition accuracies of multiple machine learning methods. In addition, we measure the analysis processing time at the sensor and the cloud to investigate the feasibility of recognizing actions in real time.

Then, we evaluate the proposed system by comparing it with the 3D ResNet model in recognition experiments. The experimental results demonstrate that the action recognition accuracy is the highest when using LSTM and that the introduction of dropout in action recognition using categories alleviates overfitting because the models can learn more generic human actions by increasing the variety of actions.

In addition, it is demonstrated that preprocessing using OpenPose on the sensor side can substantially reduce the transfer quantity from the sensor to the cloud. The proposed method inputs the divided dataset of security blog posts based on a fixed period using an overlap period to the TOT. The results suggest the extraction of topics that include malware and attack campaign names that are appropriate for the multi-labeling of cyber threat intelligence reports.

In this paper, the effective Long Range LoRa based wireless sensor network is designed and implemented to provide the remote data sensing functions for the planned smart agricultural recycling rapid processing factory. The proposed wireless sensor network transmits the sensing data from various sensors, which measure the values of moisture, viscosity, pH, and electrical conductivity of agricultural organic wastes for the production and circulation of organic fertilizers.

In the proposed wireless sensor network design, the LoRa transceiver module is used to provide data transmission functions at the sensor node, and the embedded platform by Raspberry Pi module is applied to support the gateway function. To design the cloud data server, the MySQL methodology is applied for the database management system with Apache software. The proposed wireless sensor network for data communication between the sensor node and the gateway supports a simple one-way data transmission scheme and three half-duplex two-way data communication schemes.

For the proposed two-way data communication schemes, under the condition of sending one packet data every thirty seconds, the average packet data loss rates without and with the data-received confirmation at the gateway side can be 3.

With the widespread diffusion of Internet of Things IoT , the number of applications using wireless sensor devices are increasing, and Quality of Service QoS required for these applications is diversifying. Thus, it becomes difficult to satisfy a variety of QoS with a single wireless system, and many kinds of wireless systems are working in the same domains; time, frequency, and place.

This paper considers coexistence environments of ZigBee and Wi-Fi networks, which use the same frequency band channels, in the same place. In such coexistence environments,ZigBee devices suffer radio interference from Wi-Fi networks, which results in severe ZigBee packet losses because the transmission power of Wi-Fi is much higher than that of ZigBee.

Many existing methods to avoid interference from Wi-Fi networks focus on only one of time, frequency, or space domain. However, such avoidance in one domain is insufficient particularly in near future IoT environments where more ZigBee devices and Wi-Fi stations transfer more amount of data.

Therefore, in this paper, we propose joint channel allocation and routing in both frequency and space domains. Finally, we show the effectiveness of the proposed method by computer simulation.

Currently, the IEEE For the efficient channel assignment to access-points APs from the limited number of partially overlapping channels POCs at 2.

However, non-CB links should be used in dense WLANs, since the CB links often reduce the transmission capacity due to high interferences from other links. In this paper, we examine the throughput drop estimation model for concurrently communicating links without using the CB in First, we verify the model accuracy through experiments in two network fields.

The results show that the average error is 9. Then, we verify the effectiveness of the POC assignment to the APs using the model through simulations and experiments. The results show that the model improves the smallest throughput of a host by In this paper, in order to avoid the cascading failure by increasing the number of links in the physical network in D2D-based SNS, we propose an autonomous device placement algorithm.

In this method, some relay devices are placed so as to increase the number of links in the physical network. Here, relay devices can be used only for relaying data and those are not SNS users. For example, unmanned aerial vehicles UAV with D2D communication capability and base stations with D2D communication capability are used as the relay devices.

In the proposed method, at first, an optimization problem for minimizing node resilience which is a performance metric in order to place relay devices. Then, we investigate how relay devices should be placed based on some approximate optimal solutions. From this investigation, we propose an autonomous relay device placement in the physical network. In our proposed algorithm, relay devices can be placed without the complete information on network topology.

We evaluate the performance of the proposed method with simulation, and investigate the effectiveness of the proposed method.

From numerical examples, we show the effectiveness of our proposed algorithm. Network function virtualization NFV enables network operators to flexibly provide diverse virtualized functions for services such as Internet of things IoT and mobile applications. To meet multiple quality of service QoS requirements against time-varying network environments, infrastructure providers must dynamically adjust the amount of computational resources, such as CPU, assigned to virtual network functions VNFs.

To provide agile resource control and adaptiveness, predicting the virtual server load via machine learning technologies is an effective approach to the proactive control of network systems. In this paper, we propose an adjustment mechanism for regressors based on forgetting and dynamic ensemble executed in a shorter time than that of our previous work. The framework includes a reducing training data method based on sparse model regression.

Application-aware Traffic Engineering TE plays a crucial role in ensuring quality of services QoS for recently emerging applications such as AR, VR, cloud gaming, and connected vehicles. While a deterministic application-aware TE is required for these mission-critical applications, a negotiation procedure between applications and network operators needs to undergo major simplification to fulfill the scalability of the application based on emerging microservices and container-based architecture.

On the prototype implementation, a basic packet forwarding performance is evaluated to demonstrate the feasibility of our framework. For the same test server, we first negotiate a connection with Not-ECN-Capable, and then negotiate a connection with ECN-Capable, if the sender does not receive the response to ECN-Capable negotiation from the receiver by the end of retransmission timeout, it will enter the Fallback state, and switch to negotiating a connection with Not-ECN-Capable.

Meanwhile, we provided the updated information about the characteristics related to ECN with Fallback in different regions by considering the geographical region distribution of all targeted servers. A global content delivery plays an important role in the current Internet.

Information-Centric Networking ICN is a future internet architecture which attempts to redesign the Internet with a focus on the content delivery. However, it has the potential performance degradation in the global content delivery. The key idea is to prefetch Data packets and to serve them to the consumer with the shorter round trip time. By utilizing ICN features, it can be developed as an offline and state-less proxy which has an advantage of scalability. In this study, we propose a secure data-providing system by using a verifiable attribute-based keyword search VABKS , which also has the functions of privacy preservation and feedback to providers with IP anonymous server.

We give both theoretic and experimental result, which show that our proposed system is a secure system with real-time property. One potential application of the system is to Integrated Broadcast-Broadband IBB services, which acquire information related to broadcast programs via broadband networks. One such service is a recommendation service that delivers recommendations matching user preferences such as to TV programs determined from the user's viewing history.

We have developed a real-time system outsourcing data to the cloud and performing keyword searches on it by dividing the search process into two stages and performing heavy processing on the cloud side. In Internet applications, when users search for information, the search engines invariably return some invalid webpages that do not contain valid information.

These invalid webpages interfere with the users' access to useful information, affect the efficiency of users' information query and occupy Internet resources. Accurate and fast filtering of invalid webpages can purify the Internet environment and provide convenience for netizens.

This paper proposes an invalid webpage filtering model HAIF based on deep learning and hierarchical attention mechanism. HAIF improves the semantic and sequence information representation of webpage text by concatenating lexical-level embeddings and paragraph-level embeddings. HAIF introduces hierarchical attention mechanism to optimize the extraction of text sequence features and webpage tag features.

Among them, the local-level attention layer optimizes the local information in the plain text. By concatenating the input embeddings and the feature matrix after local-level attention calculation, it enriches the representation of information. The tag-level attention layer introduces webpage structural feature information on the attention calculation of different HTML tags, so that HAIF is better applicable to the Internet resource field.

In order to evaluate the effectiveness of HAIF in filtering invalid pages, we conducted various experiments.

Experimental results demonstrate that, compared with other baseline models, HAIF has improved to various degrees on various evaluation criteria. Computer networks are facing serious threats from the emergence of sophisticated new DGA bots. These DGA bots have their own dictionary, from which they concatenate words to dynamically generate domain names that are difficult to distinguish from human-generated domain names.

In this letter, we propose an approach for identifying the callback communications of DGA bots based on relations among the words that constitute the character string of each domain name.

Our evaluation indicates high performance, with a recall of 0. Two-state number-conserving cellular automaton NCCA is a cellular automaton of which cell states are 0 or 1, and the total sum of all the states of cells is kept for any time step. It is a kind of particle-based modeling of physical systems. By employing this structure, we show a relation between the neighborhood size n and n - 2 NCCAs.

Metabolic networks represent the relationship between chemical reactions and compounds in cells. In useful metabolite production using microorganisms, it is often required to calculate reaction deletion strategies from the original network to result in growth coupling, which means the target metabolite production and cell growth are simultaneously achieved.

Although simple elementary flux mode EFM -based methods are useful for listing such reaction deletions strategies, the number of cases to be considered is often proportional to the exponential function of the size of the network. Therefore, it is desirable to develop methods of narrowing down the number of reaction deletion strategy candidates. In this study, the author introduces the idea of L1 norm minimal modes to consider metabolic flows whose L1 norms are minimal to satisfy certain criteria on growth and production, and developed a fast metabolic design listing algorithm based on it minL1-FMDL , which works in polynomial time.

In the computational experiments, it was seen that the average value of the target metabolite production rates of minL1-FMDL was higher than that of the simple EFM-based method, and the computation time of minL1-FMDL was fast enough even for genome-scale networks.



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Home Call For Papers. This conference continues to build on the long history of COMAD and CODS conferences and will bring together the two related communities closer under a common umbrella for the fourth time. Transaction processing, query processing, query optimisation, indexing and storage, distributed data platforms, spatio-temporal databases, RDBMS, NoSQL systems, key-value stores, cloud data management, big data systems, data cleansing, data analytics, data integration, benchmarking, tuning and testing, graph database management, security and privacy. Data pre-processing, Classification and regression, parallel and distributed learning, semi- and unsupervised learning, matrix and tensor methods, graph mining, network analytics, reinforcement learning, feature engineering, deep learning, Bayesian methods, time series analysis, optimization, graphical models, relational models, text analytics and NLP, information retrieval, knowledge representation, knowledge-based systems, human-in-the-loop learning, planning and reasoning, ML for mobiles and other resource constrained environments, data mining, causality, fairness accountability and transparency, interpretability. The research track invites full as well as short papers. The goal of short papers is to provide a venue for relatively simpler but innovative ideas such as engineered solutions, exciting work-in-progress or even negative results that would be interesting to the broader community. Authors of accepted papers will get an opportunity to showcase their work either as an oral presentation or as a poster.

Pages. ISSN. Client. Ministry of Defence which naval ships and vessels Russia can build at any given time.

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Streit; R. A robust criterion with an adaptive diagnosis for a fail-safe localization system is presented in the paper. As known, the Unscented Transformation UT deals well with non-linearity problems, but the performance decreases significantly under the presence of non-Gaussian noises. The MEE overcomes this problem and shows high robustness dealing with heavy non-Gaussian noises especially multi-Gaussian noises. The proposed approach is tested and validated using real experimental data, for a multi-sensor fusion of Global Navigation Satellite System GNSS , and Odometer Odo data for an autonomous vehicle localization application. Vincent Poor Princeton University An inequality connecting the Bayesian Bregman risk, the Kullback-Leibler divergence and distributions from the exponential family is derived. The inequality has applications in directional and robust estimation and can provide universal lower bounds on Bregman risks. Its usefulness is illustrated using the example of estimation in Poisson noise with a logarithmic cost function.


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build a mining rig 2021 goda

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View the Executive Summary. Research assistance was provided by Tessa Cole and David Flint.

Fundamentals of Soft Matter Science

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It is situated in the central part of the city at the intersection of 8th Marta street and Kuybysheva street between metro stations "Ploshchad Goda" and "Chkalovskaya". After the opening of the exit to the Greenwich shopping center, it became the first deep-level station of the Yekaterinburg metro with two lobbies before that there were only shallow stations located at the edges of the line and in the first section. The project name of the station was "Kuybyshevskaya". Yeltsin and the Sverdlovsk Executive Committee A. Mekhrentsev, at the request of the staff of Uralgeologiya due to the fact that in the area of the metro station there is an administration building, where outstanding geologists of the Urals, and discoverers of deposits worked, as well as the Sverdlovsk Mining Institute.

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Beginning of fieldwork in 2021 and corporate social responsibility

Multimedia data and information management is an important task according to the development of media processing technology. Multimedia is a useful resource that people understand complex situations such as the elderly care domain. Appropriate annotation is beneficial in several tasks of information management, such as storing, retrieval, and summarization of data, from a semantic perspective.


Best CPU for Mining 2021: Mining Cryptocurrency ( Processors)

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    Authoritative response, cognitive ...