Improved LSTM Method of Predicting Cryptocurrency Price Using Short-Term Data

As cryptocurrencies develop, it cannot be denied that crypto prices are volatile. One of the influencing factors is the increasing volume of transactions which attracts the interest of researchers to conduct research in developing coin price predictions from cryptocurrencies. The method, algorithm and amount of data affect the prediction results. In this study, prediction modelling will be carried out using the LSTM method and short-term data. This study will conduct two experiments using the simple LSTM method and utilising multivariate time series with LSTM. The smallest predicted value is obtained using an 80/20 data allocation distribution scenario, input layer LSTM = 360, Epoch = 500, a Solana coin with RMSE = 0.111, R2 = 0.9962. It can be interpreted that short-term data can be used in making predictive models. Still, special attention needs to be paid to the characteristics of the dataset used and the modelling methodology, and it is hoped that the results of this study can be used in further research

IoTA: IoT Automated SIP-based Emergency Call Triggering System for general eHealth purposes

The expansion of Internet of Things (IoT) and the evolution in communication technologies have enabled homes, cars even whole cities to be network connected. However, during an emergency incident, IoT devices have not been used to trigger emergency calls directly to healthcare providers mainly due to their constrained capabilities and lack of support session-oriented communications. Moreover, emergency services are currently offered by public safety stakeholders that do not support call triggering by IoT devices. This paper proposes IoTA framework which enables IoT devices to generate automatically emergency calls and support bi-directional communication sessions between healthcare providers and end users. The IoTA framework incorporates intelligent algorithms for processing and evaluating emergency events from various devices and performs emergency calls immediately after the occurrence of an event. The healthcare providers can interact with the IoTA framework requesting continuous real-time sensor data. A prototype implementation and initial evaluation results are presented as a proof of concept for people suffering from diverse chronic diseases. Experimental results have shown that the proposed framework can be considered as a promising solution for detecting, reporting emergency events, eliminating the hoax calls and responding swiftly saving lives

Modification of the mean-square error principle to double the convergence speed of a special case of Hopfield neural network used to segment pathological liver color images

BACKGROUND: This paper analyzes the effect of the mean-square error principle on the optimization process using a Special Case of Hopfield Neural Network (SCHNN). METHODS: The segmentation of multidimensional medical and colour images can be formulated as an energy function composed of two terms: the sum of squared errors, and a noise term used to avoid the network to be stacked in early local minimum points of the energy landscape. RESULTS: Here, we show that the sum of weighted error, higher than simple squared error, leads the SCHNN classifier to reach faster a local minimum closer to the global minimum with the assurance of acceptable segmentation results. CONCLUSIONS: The proposed segmentation method is used to segment 20 pathological liver colour images, and is shown to be efficient and very effective to be implemented for use in clinics

Unified representation of monitoring information across federated cloud infrastructures

Nowadays one of the issues hindering the potential of federating cloud-based infrastructures to reach much larger scales is their standard management and monitoring. In particular, this is true in cases where these federated infrastructures provide emerging Future Internet and Smart Cities-oriented services, such as the Internet of Things (IoT), that benefit from cloud services. The contribution of this paper is the introduction of a unified monitoring architecture for federated cloud infrastructures accompanied by the adoption of a uniform representation of measurement data. The presented solution is capable of providing multi-domain compatibility, scalability, as well as the ability to analyze large amounts of monitoring data, collected from datacenters and offered through open and standardized APIs. The solution described herein has been deployed and is currently running on a community of 5 infrastructures within the framework of the European Project XIFI, to be extended to 12 more infrastructures

Evaluating the usability and learning potential of a virtual museum tour application for schools

Museums engage people in diverse displays to help them appreciate cultural heritage while improving their cognitive, attitudinal, affective and social outcomes (Jarvis & Pell, 2005). Museums often have schools as their primary targets. Still, school visits to museums are not always possible, especially when the schools are located in remote areas or due to extraordinary circumstances (i.e., a pandemic). This paper presents the evaluation of an early version of a web-based application for virtual museum tours (VMT) for schools. The app enables teachers to create themed museums by selecting artefacts stored in the application’s library or uploading their own. This paper presents the evaluation of an early version of a web-based application for virtual museum tours for schools, which empowers teachers to create their own museums. The evaluation aims to inform its redesign and ensure usability and learning potential. The app evaluation involved two phases - qualitative usability testing and a virtual tours evaluation instrument. Through a concurrent think-aloud protocol, qualitative usability testing has been employed to uncover problems in the user experience of engaging in main application tasks. In addition, a 19-item virtual tours evaluation instrument focused on four dimensions of the virtual tours experience: authenticity, interactivity, navigation, and learning potential (Li, Nie & Ye, 2019). The participants selected the extent to which they agreed with each of the 19 statements on a scale of 1-5 (where 1 = strongly disagree and 5 = strongly agree). The main findings from this first evaluation iteration indicated that overall the users found the application usable, but some recommendations were made for improving its interactivity and learning potential. The average scores for authenticity, interactivity and learning potential were moderate (M = 3.35; M = 3.43; M = 3.56, respectively); for navigation, the score was relatively high (M = 4.10). Participants highlighted issues, for instance, concerning the accessibility of the app (e.g., not visible error messages), missing features (e.g., exit buttons), and difficulties in interacting with the 'edit menu'. They also added suggestions for improving the app, for example, by adding more avatars, artefacts, and evaluation tools. The strength of this VMT application lies in teachers’ ability to personalise the virtual museum in a way that addresses the classrooms’ learning aims and interests. Insights from this evaluation can contribute to the design of online virtual museum tour applications, but they also have important implications for developing other virtual tour applications for schools. References: [1] Jarvis, T., & Pell, A. (2005). Factors influencing elementary school children's attitudes toward science before, during, and after a visit to the UK National Space Centre. Journal of Research in Science Teaching: The Official Journal of the National Association for Research in Science Teaching, 42(1), 53-83. [2] Li, J., Nie, J. W., & Ye, J. (2022). Evaluation of virtual tour in an online museum: Exhibition of Architecture of the Forbidden City. PloS one, 17(1), e0261607

Queue Management in Network Processors

Abstract: -One of the main bottlenecks when designing a network processing system is very often its memory subsystem. This is mainly due to the state-of-the-art network links operating at very high speeds and to the fact that in order to support advanced Quality of Service (QoS), a large number of independent queues is desirable. In this paper we analyze the performance bottlenecks of various data memory managers integrated in typical Network Processing Units (NPUs). We expose the performance limitations of software implementations utilizing the RISC processing cores typically found in most NPU architectures and we identify the requirements for hardware assisted memory management in order to achieve wire-speed operation at gigabit per second rates. Furthermore, we describe the architecture and performance of a hardware memory manager that fulfills those requirements. This memory manager, although it is implemented in a reconfigurable technology, it can provide up to 6.2Gbps of aggregate throughput, while handling 32K independent queues

Medical Image Retrieval: Past and Present

With the widespread dissemination of picture archiving and communication systems (PACSs) in hospitals, the amount of imaging data is rapidly increasing. Effective image retrieval systems are required to manage these complex and large image databases. The authors reviewed the past development and the present state of medical image retrieval systems including text-based and content-based systems. In order to provide a more effective image retrieval service, the intelligent content-based retrieval systems combined with semantic systems are required