Miracle-dz/UniFAR-FTUs huggingface dataset:
A Blockchain-based key Management Scheme for Named Data Networking

Named Data Networking is built with security which requires each named Data object to be digitally signed by its producer. Thus, the NDN project has proposed a key management model on NDN testbed for verification of the Data packet to be immune to distributing poisoned content. However, in practice, this model poses two challenges for verifying fake content: (1) the centralized architecture easily leads to a single point of failure, especially when the root key fails, its difficult to verify the keys across sites due to the lack of trust between them, and (2) excessive overhead of certificate chain traversal when verifying signature. This paper first proposes a blockchain-based key management scheme in NDN to address the problem of lack of mutual trust between sites without trust anchors. Specifically, all site nodes form a permissioned blockchain for storing public key hashes to ensure the authenticity, and the proxy gateway participates in verifying to reduce excessively frequent communication between the router and the blockchain. In addition, the NDN public key content object and the scheme of their storage, verification, and revocation are redesigned. The result of our analysis and evaluation shows that the proposed scheme is capable of supporting less verification numbers and higher verification efficiency.
Numerical analysis of dynamic stall on wind turbine airfoils

This paper is focused on a numerical investigation of dynamic stall on a NACA 0012 and S809 airfoils. The study purpose consists of fluid flow modelling around an oscillating airfoil to analyze the dynamic stall phenomenon. Simulations are carried out using an open source CFD code (openFoam); the numerical model is based on solving the unsteady Navier Stokes equations on a dynamic mesh, where the SST $k-\omega $ turbulent model is used. Results present a good agreement comparing to published experimental data.
Bandwidth-based QoS-aware Multisource Architecture for Information-Centric Wireless Multihop Networks

Information-Centric Networking (ICN) is one of the representative architectures for Future Internet that focuses on content rather than IP address. Different from traditional TCP/IP networking, any node that caching content can respond to a request for the content. This property makes it convenient that multiple providers deliver different chunks of a content to a requester concurrently, which can offer more reliable network services and better load balancing. However, it may impact quality of service (QoS) because traditional end-to-end QoS control cannot be performed in this scenario. In this paper, a Bandwidth-based QoS-aware Multisource Architecture (BQMA) is proposed for Information-Centric wireless multihop networks, which performs QoS control for multisource routing, forwarding, reservation procedure, and provider selection functions. Simulation results demonstrate the effectiveness of the proposed architecture.
Similarity Measure Based on Incremental Warping Window for Time Series Data Mining

A similarity measure is one of the most important tasks in the fields of time series data mining. Its quality often affects the efficiency and effectiveness of the related algorithms that need to measure the similarity between two time series in advance. Dynamic time warping is one of the most robust methods to compare one time series with another based onwarping alignments. In this paper, the design of an incremental warping window is used to improve the performance of dynamic time warping. The incremental warping window is changeable for various time series with different lengths. Moreover, the improved dynamic time warping based on the novel window considers the recent alignments as much as possible, which indicates that the proposed method concentrates on more information of the recent data points than that of the previous data points. In addition, it is suitable for online similarity measure between data stream. The experimental evaluation shows that the proposed method is effective and efficient for time series mining.
Optimum Wavelengths on Haemoglobin Concentration Measurement

Hemoglobin is significant part of red blood cell to transport the oxygen and carbon dioxide. The concentration of hemoglobin in the blood can be used to determine a person's health condition. These days, accurate and reliable measurement of hemoglobin concentration is done by using invasive method such as cyanmethemoglobin and automated hematology analyser. Yet this method is costly, high risk of infection, and requires particular technique. Non-invasive method offers a better alternative because it has low-risk infection, obtain faster result, and portable. One of non-invasive method is photoplethysmography. This method used light with particular wavelengths to determine hemoglobin concentration. This work used blood drawn from vena and multispectral sensor to determine the wavelength that had correlation to the difference of haemoglobin concentration. Linear regression was used to determine the wavelength ratios that had higest correlation to haemoglobin concentration. The result showed that 615/455 nm wavelength had highest correlation to the change of hemoglobin concentration. These wavelengths could be used to measure the hemoglobin concentration noninvasively.
Event-based output tracking control for networked T-S fuzzy systems under non-periodic DoS jamming attacks

This paper is concerned with event-based output tracking control for networked Takagi-Sugeno (T-S) fuzzy systems under denial-of-service (DoS) jamming attacks. First, a unified event-based augmented fuzzy switched systems is formulated, in which: 1) an output-based resilient event-triggering transmission strategy is designed to reduce network resource utilization while resisting the impact of the DoS jamming attacks; and 2) an event-based fuzzy tracking controller with the imperfect premise matching is constructed. Second, using a piecewise Lyapunov functional approach, sufficient conditions for the existence of a fuzzy secure output feedback H∞ controller that guarantees stability and a prescribed H∞ performance are given in terms of linear matrix inequalities.
Quantum Games of Quadruple Helix Ecosystem

Computational model of quadruple innovations ecosystem which obeys quantum strategies for disruptive innovations dynamics is proposed: the Dirac-Solow-Swan potential represents the dynamics of quadruple ecosystems and each members obey game theory. The classical entities are Business and Government, while Academia and Community assumed as quantum objects. It is shown that the architecture of quantum strategies of quadruple helix ecosystem applied on data topology has ≈ O(n) computational depth which and can enhance the capital accumulation of innovations.
Coupling DAS, SVC and NDN: an SVC-aware cache and forwarding policy for NDN routers

Video streaming traffic has grown sharply in recent years. Dynamic Adaptive Streaming (DAS) is widely used in video streaming because of its ability to cope with the network dynamics and terminal heterogeneity. Compared with AVC(advanced video coding), SVC(scalable video coding) can better adapt to network conditions, reduce server storage and improve users’ playback QoE. However, the present TCP/IP network is communication-oriented, focusing on addressing rather than content, which can not fully realize the potential of DAS and SVC. Named Data Networking (NDN) is a new network architecture that centers around content. Its name based routing, in-network caching, hop-by-hop forwarding and built-in multicast support naturally fits the pattern of DAS and SVC. As the last piece of jigsaw puzzle, coupled with DAS and SVC, NDN is believed to be able to provide better video streaming performance. In this paper, we proposed a customized SVC-aware cache and forwarding policy for ordinary NDN routers to support video streaming applications. Simulation results based on ndnSIM2.3 showed that the proposed policy can improve the average playback bitrate and reduce the video stall frequency as well as duration.
ICNRL: An Initiative Framework Towards Information Centric Network Representation

The exponentially growing demand for computational resources prevents the Information Centric Networking (ICN) being deployed in practice due to the high dimensional sparse data computation. However, we argue that Network Representation Learning (NRL) can help to solve the problem by transforming the raw network information data into low-dimensional dense adjacency matrix representation. In this paper, we propose ICNRL, a novel task-based NRL scheme for ICN. Based on the adjacency matrix generated by NRL, ICNRL can calculate the index threshold value to support the networking decision making of content-store (CS), pending information table (PIT), and forwarding information base (FIB), and therefore improves the management and processing capabilities of ICN.
A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era

Mobile crowd-sensing (MCS) is a new sensing paradigm that takes advantage of the extensive use of mobile phones that collect data efficiently and enable several significant applications. MCS paves the way to explore new monitoring applications in different fields such as social networks, lifestyle, healthcare, green applications, and intelligent transportation systems. Hence, MCS applications make use of sensing and wireless communication capabilities provided by billions of smart mobile devices, e.g., Android and iOS-based mobile devices. The aim of this paper is to identify and explore the new paradigm of MCS that is using smartphone for capturing and sharing the sensed data between many nodes. We discuss the main components of the infrastructure required to support the proposed framework. The existing and potential applications leveraging MCS are laid out. Furthermore, this paper discusses the current challenges facing the collection methodologies of the participants’ data in task management. The recent issues in the MCS findings are reviewed as well as the opportunities and challenges in sensing methods are analyzed. Finally, open research issues and future challenges facing MCS are highlighted.
Knowledge boundaries in enterprise software platform development: Antecedents and consequences for platform governance

The widespread uptake of platform strategies turns many vendors of enterprise software into curators of an ecosystem of firms that collaboratively develop and commercialize a shared technology. As a platform owner's effectiveness in integrating knowledge across ecosystem participants will distinguish it from its competitors, we investigate the management of development‐related knowledge across firm boundaries. Our exploratory, multiple‐case study of 4 platforms illustrates how “knowledge boundaries” emerge between platform owners and complementors. We observe that knowledge boundaries are influenced by a platform's functional extent, interface design, and evolutionary dynamics, which create differences, dependencies, and novelty of development knowledge, resulting in qualitatively distinct types of knowledge boundaries. To overcome knowledge boundaries, platform owners provide various resources at the boundary, including information portals, documentation, helpdesks, and alignment workshops. We observe that in shaping these resources, platform owners face a trade‐off between providing knowledge at the right scope, while allowing for the scalability of knowledge resources for the entire ecosystem. Depending on their scope and scale, we classify knowledge boundary resources as broadcasting, brokering, and bridging, each representing qualitatively distinct patterns in managing knowledge in platform ecosystems. We conclude with implications for researchers and managers.
Untangling origin-destination flows in geographic information systems

Origin–destination flow maps are a popular option to visualize connections between different spatial locations, where specific routes between the origin and destination are unknown or irrelevant. Visualizing origin–destination flows is challenging mainly due to visual clutter which appears quickly as data sets grow. Clutter reduction techniques are intensively explored in the information visualization and cartography domains. However, current automatic techniques for origin–destination flow visualization, such as edge bundling, are not available in geographic information systems which are widely used to visualize spatial data, such as origin–destination flows. In this article, we explore the applicability of edge bundling to spatial data sets and necessary adaptations under the constraints inherent to platform-independent geographic information system scripting environments. We propose (1) a new clustering technique for origin–destination flows that provides within-cluster consistency to speed up computations, (2) an edge bundling approach based on force-directed edge bundling employing matrix computations, (3) a new technique to determine the local strength of a bundle leveraging spatial indexes, and (4) a geographic information system–based technique to spatially offset bundles describing different flow directions. Finally, we evaluate our method by applying it to origin–destination flow data sets with a wide variety of different data characteristics.
Novus-io: An Internet of Things Platform for Academic Projects

Internet of things (IoT) is based on a global dynamic information network with cloud services where a great number of devices (things) exchange data to provide added-value services and products. There are several commercial and open source IoT platforms available in the market to connect devices to internet; however, they have cost and operational constraints that make them not suitable for academic projects. In this work, an IoT platform, known as Novus-io, is introduced in order to support academic projects for undergraduate students. With this platform and proper training, undergraduate students from different majors (not only from information technology and electronics) are capable to upgrade their school projects with IoT functionalities. The objective of this approach is to provide to any undergraduate student skills and knowledge on IoT, so they will be prepared, in their imminent step toward professionalism, to understand the relevance of digital services in today´s world.
A High-Order Hidden Markov Model and Its Applications for Dynamic Car Ownership Analysis

This paper extends the dynamically formulated hidden Markov models to a high-order hidden Markov model (HO-HMM) formulation. In the HO-HMM, the Markovian assumption that the future states (interpreted as the states of preferences or attitudes) depend only on the current state has been relaxed. Instead, the HO-HMM generalizes that the future states will depend on a number of states occurring beforehand. This paper develops the theoretical formulation of a HO-HMM framework. A recursive algorithm of likelihood computation is derived for model estimation. The algorithm significantly reduces the complexity of estimation and ensures the applicability of high-order hidden Markov modeling. The proposed methodology is further demonstrated on a vehicle ownership choice application using Puget Sound Transportation Panel data coupled with a few supplementary data sources. Long-term life-cycle stage changes in households are used as proxies for the high-order Markov transitions in car ownership hidden states. Results .
Muscle activity response of the audience during an experimental music performance

This exploratory study investigates muscular activity characteristics of a group of audience members during an experimental music performance. The study was designed to be as ecologically valid as possible, collecting data in a concert venue and making use of low-invasive measurement techniques. Muscle activity (EMG) from the forearms of 8 participants revealed that sitting in a group could be an indication of a level of group engagement, while comparatively greater muscular activity from a participant sitting at close distance to the stage suggests performance-induced bodily responses. The self-reported measures rendered little evidence supporting the links between muscular activity and live music exposure, although a larger sample size and a wider range of music styles need to be included in future studies to provide conclusive results.
Real-Time Visualization of Geo-Sensor Data Based on the Protocol-Coupling Symbol Construction Method

Obtaining and visualizing the internal state and position information of the remote device using sensors are important aspects of industrial manufacturing. For large-scale geo-sensors that have been recently used, map-based management and visualization of the geo-sensor devices have become ubiquitous. Users often build multiple map symbols to represent the multiple states of a device based on traditional map symbols. Visualizing multiple geo-sensor data in real time with one map symbol is difficult. In this paper, a protocol-coupling map symbol and a construction method for real-time data visualization is introduced where different sensor states of the geo-sensor are expressed with one symbol. The sensor data visualization method in supervisory control and data acquisition systems (SCADA) was introduced and applied to the construction and visualization process of map symbols. First, based on the traditional vector map symbols and the communication protocol parsing interface, the mapping relationship between the sensor data item and the graphic element is defined in the map symbol construction process. Second, by referring to the streaming services method in ArcGIS GeoEvent, geo-sensor data acquisition and a transfer broker in a GIS server is built, through which the real-time sensor data can be transferred from the remote side to the map client and used for map symbol rendering. Finally, the new map symbols are used for real-time geo-sensor data visualization in applications. In the application of the real-time monitoring of geo-sensor devices, remote device information was acquired by sensor and transmitted to the broker then cached on the server side. If the cached sensor data has changed compared to the previous, the changed data will be pushed to map client by broker. The communication module in the map client that communicates with the broker receives changed geo-sensor data and triggers a refresh of the map. Then the protocol-coupling map symbol is rendered according to the mapping profile and the status of the geo-sensor device will be displayed on the map in real time. All the methods and processes were verified in client-server and browser-server GIS architecture.
Unusual Suspect of Societal Innovativeness in Online Social Innovation Community: A Network and Communication Framework

The widespread adoption of the social computing paradigm has ushered in the development of online social innovation community (OSIC) as a promising method for solving social problems. Previous studies have not explicitly considered the conceptual factors that facilitate these communities’ users’ innovative activities, so it is vital to conduct empirical studies to verify the effectiveness of these factors. In this paper, the primary goals are to construct a theoretical model of the social innovation and empirically verify the casual relationship between theoretical factors and societal innovativeness. A survey of 398 OSIC users was conducted to empirically validate the theoretical model. The causal relationships between network characteristics and social innovativeness were experimentally tested. The results of this study indicated that ambiguity, switching, and multiplexity are important factors that facilitate social innovativeness, which contradicts the prior assumptions about innovation performance.
KETELADANAN GURU SEBAGAI PENGUAT PROSES PENDIDIKAN KARAKTER

Kajian ini berawal dari banyaknya fenomena kegagalan pendidikan karakter yang dilaksanakan di sekolah-sekolah yang diakibatkan tidak adanya figur karakter yang dapat dicontohnya padahal pendidikan karakter akan berhasil ketika lingkungan pembentuk karakter mendukung. Salah satunya adalah ketersediaannya figur teladan. Teladan adalah sesuatu yang patut dicontoh baik itu nilai, sikap, dan perilakunya. Pendidik merupakan figur utama dalam lingkungan pendidikan yang utama dijadikan teladan bagi peserta didik. Pendidik bukan hanya dilingkungan sekolah namun juga dilingkungan keluarga dan masyarakat seperti guru, orang tua dan tokoh masyarakat. Untuk memperlancar pencapaian tujuan pendidikan karakter maka harus dibentuk teladan dalam lingkungan pembentuk karakter sebagai bagian dari komponen penguat karakter peserta didik. Maka dari itu, pendidikan karakter di sekolah harus menyediakan figur teladan untuk contoh bagi peserta didik. Dengan guru memiliki nilai, sikap, dan perilaku yang dapat diteladani maka peserta didik bukan hanya memiliki pengertian tentang nilai namun juga pemahaman dan keyakinan akan nilai yang ingin dibentuk karena adanya figur teladan.
Layered Models for Large Scale Time-Evolving Landscapes. (Modèles à couches pour simuler l'évolution de paysages à grande échelle)

The development of new technologies and algorithms allows the interactive visualization of virtual worlds showing an increasing amount of details and spatial extent. The production of plausible landscapes within these worlds becomes a major challenge, not only because the important part that terrain features and ecosystems play in the quality and realism of 3D sceneries, but also from the editing complexity of large landforms at mountain range scales. Interactive authoring is often achieved by coupling editing techniques with computationally and time demanding numerical simulation, whose calibration is harder as the number of non-intuitive parameters increases. This thesis develops new methods for the simulation of large-scale landscapes. Our goal is to improve both the control and the realism of the synthetic scenes. Our strategy to increase the plausibility consists of building our methods on physically and geomorphologically-inspired laws: we develop new numerical methods, which, combined with intuitive control tools, improve user experience. By observing phenomena triggered by compression areas within the Earth's crust, we propose a method for the intuitive control of the uplift based on a metaphor on the sculpting of the tectonic plates. Combined with new efficient methods for fluvial and glacial erosion, this allows for the fast sculpting of large mountain ranges. In order to visualize the resulting landscapes withing human sight, we demonstrate the need of combining the simulation of various phenomena with different time spans, and we propose a stochastic simulation technique to solve this complex cohabitation. This methodology is applied to the simulation of geological processes such as erosion interleaved with ecosystems formation. This method is then implemented on the GPU, combining long term effects (snowfall, phase changes of water) with highly dynamics ones (avalanches, skiers impact). Our methods allow the simulation of the evolution of large scale, visually plausible landscapes, while accounting for user control. These results were validated by user studies as well as comparisons with data obtained from real landscapes.