Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multi-Clustering Approach to Scale Distributed Tenant Networks for Mobile Edge Computing

Fifth generation (5G) mobile networks will lead to a deep integration between networks and applications. Through novel paradigms like network functions virtualization and edge computing, the new classes of heterogeneous application services will be enabled to run close to the mobile end-user devices with zero-perceived latency and fully-cognitive dynamic reconfiguration capabilities. Such “vertical” applications exhibit diverse performance/scalability requirements, and will rely on highly distributed, extremely virtualized, multi-tenant, and software-defined infrastructures. In such a context, handling the required operations in a scalable and dynamic fashion will be of paramount importance. A specific aspect, addressed by software-defined networking (SDN), regards the provision of suitable communication channels, once resource allocation mechanisms have performed the most efficient deployment of virtual network function instances, and VNF chaining needs to be implemented to enable network services. In this respect, this paper introduces the multi-cluster overlay (MCO) network paradigm: a tunnel-less SDN scheme for scalable realization of virtual tenant networks across the 5G distributed infrastructure, able to support (bulk) migrations of software instances among geo-distributed computing resources in a seamless and effective fashion. The numerical simulation and experimental results show that the MCO achieves up to over one order of magnitude smaller number of forwarding rules than the other state-of-the-art SDN mechanisms, while also assuring high performance during reconfiguration operations.
Outlier Detection Based on Low Density Models

Most outlier detection algorithms are based on lazy learning or imply quadratic complexity. Both characteristics make them unsuitable for big data and stream data applications and preclude their applicability in systems that must operate autonomously. In this paper we propose a new algorithm—called SDO (Sparse Data Observers)—to estimate outlierness based on low density models of data. SDO is an eager learner; therefore, computational costs in application phases are severely reduced. We perform tests with a wide variation of synthetic datasets as well as the main datasets published in the literature for anomaly detection testing. Results show that SDO satisfactorily competes with the best ranked outlier detection alternatives. The good detection performance coupled with a low complexity makes SDO highly flexible and adaptable to stand-alone frameworks that must detect outliers fast with accuracy rates equivalent to lazy learning algorithms.
Another e-learning method in upper primary school: 3D spaces

Students, especially primary and secondary students spend significantly more time using their smart devices compared to the time they spend preparing for their lessons. Teachers must not only coax students back into studying well, but find up-to-date methods that use smart devices to find success in teaching and to develop skills that are needed in the labour market. 3D spaces provide an exciting technology and can effectively improve those skills. Projects in college education have already confirmed that using these spaces resulted in more effective knowledge but this method can also be great in secondary school education and in primary schools among the generation called Alpha. This article highlights the advantages of the MaxWhere 3D VR spaces in teaching optics as part of the school subject of Physics.
A New Advanced Design and Implementation of Laser Warning System

Nowadays there are a huge progress in the Electro-optical System and the Laser weapon, not only were danger of laser weapons on the war but also civilian target. The laser warning receiver system can detect, classify, identify and give warning of the hostel laser threat aiming in a very short time with high sensitivity and it can activate the electro-optical counter measurement system, directly or with the user. In this paper we do a design and implementation for laser warning system circuit, the new purpose circuit increase the performance of the warning system and increase the detection field of view.
Controlled HVDC links between RES and strong or weak power grids:comparative review

Renewable energy source (RES) installations can inject large amounts of power into the main grid either via ac or dc connections. For certain applications such as long-distance located wind turbine (WT) farms, high-voltage dc (HVDC) transmission is preferred due to engineering and economical reasons. In this paper, the dynamic behavior of a fully controlled HVDC-link transmission system is studied and reviewed under two distinct cases, namely, the dc-link connection of a WT system to a stiff ac power system and the connection to a weak one. Critical aspects for the efficient and stable performance of such a HVDC-link transmission are related to the capability of the control scheme applied to achieve maximum power extraction and simultaneously to effectively regulate the reactive power injected to a strong ac grid or the ac voltage magnitude of a weak ac grid. As shown by this study, cascaded control schemes designed in a manner that guarantees stability provide an excellent system performance with fast and smooth response.
An Efficient Multicast Clustering Scheme for D2D Assisted Offloading in Cellular Networks

Device-to-device (D2D) multicast communication is considered as a promising technology to realize the innovative content-aware applications in the fifth generation (5G) networks, such as multimedia multicast service and group-based machine type communications (MTC) applications. Under such scenarios, mobile devices (MDs) that are interested in the same content can be clustered as a group and transmit cooperatively via multiple hops to achieve efficient content distribution. Such multi-hop multicast transmission helps to relief traffic pressure in cellular networks and improve the quality of service for cell edge users. However, it requires careful design of the clustering scheme to ensure delay performance and energy efficiency. In this paper, we propose a multicast clustering algorithm for multi-hop communications. As for each hop, we select a combination of nodes to act as cluster heads (CHs) and multicast the content to a group of next-hop users by jointly considering achievable multicast data rate, remaining operation time and end-to-end delay. In the case of overlapping clusters, we propose a cluster member (CM) reassignment criteria to minimize transmission redundancy. Simulation results show that the performance of our proposed clustering algorithm is competitive in terms of delay and robustness.
Shimmer: Implementing a Heterogeneous-Reliability DRAM Framework on a Commodity Server

In this paper, we present the implementation of a heterogeneous-reliability DRAM framework, Shimmer, on a commodity server with a fully fledged OS. Shimmer enables splitting of DRAM into multiple domains with varying reliability and allocation of data depending on their criticality. Compared to existing studies which use simulators, we consider practical restrictions stemming from the real hardware and investigate methods to overcome them. In particular, we reveal that the implementation of the heterogeneous-reliability memory framework requires disabling of the hardware memory interleaving, which results in a significant degradation of the system performance. To overcome the induced performance loss, we develop a software-based interleaving. We evaluate the performance, power and energy of the server using 35 benchmarks across three memory configurations: the baseline configuration; with disabled hardware memory interleaving and Shimmer with software-based memory interleaving. Our results show that Shimmer introduces a minor 6% performance overhead, while reducing the average DRAM power by 19.9% when memory operates under relaxed refresh rate and lowered memory supply voltage. As one of our main contributions we demonstrate that a heterogeneous-reliability framework based on Shimmer can be realized on a commodity server and save 9.1% of the total processor and memory energy.
Resistive Switching Behaviours and Novel Device Applications of Metal-Oxide-Si/Ge Structures

To achieve fab-friendly RRAM, metal-oxide-(Si, SiGe or Ge) structure is a promising device structure. In this paper, we discuss the challenges for developing high performance RRAM with this structure. Resistive-switching layer inserting and interface engineering are proposed for enhancing the device performance. And CMOS compatible Si and Ge-based bipolar RRAM (TiN/Al-doped-HfOx/Si and TiN/HfOx/GeOx/Ge) cells with ultra-low operations current, asymmetric I-V characteristics, good retention behaviors and AC switching properties are also demonstrated. Furthermore, the reported works show that TiN/HfOx/GeOx/Ge structured RRAM cell is very promising for future high performance content addressable memory (CAM) application.
Analysis of Partial Power DC–DC Converters for Two-Stage Photovoltaic Systems

Two-stage photovoltaic (PV) configurations have become increasingly popular due to the decoupling between the inverter dc-link voltage and the PV voltage, adding flexibility to extend the maximum power point tracking range. However, the additional dc–dc converter increases the power converter losses. The concept of partial power converters (PPCs), which reduce the amount of power handled by the dc stage, can mitigate this effect. However, the type of topology, its power and voltage rating, efficiency, and an operating range can vary significantly depending on the function (boosting or reducing voltage) and type of PV application and scale (micro-, string-, or multi-sting inverter). This paper analyzes the possible configuration of connections of PPC depending on the application and scale of the PV system and introduces a new buck-type PPC. Three solutions for practical PV systems are further elaborated, including experimental validation. Results show that the PPC concept greatly improves the overall PV system efficiency with the added benefit that the dc–dc stage power ratings achieved are only a fraction of the PV system, reducing size and cost of the power converter without affecting the system performance.
BEM-based EKF-RTSS Channel Estimation for Non-stationary Doubly-selective Channel

An extended Kalman filter and Rauch-Tung-Striebel Smoother (EKF-RTSS) based on Basis Expansion Model (BEM) is proposed in this paper to cope with the challenges of doubly-selective and non-stationary channel in high-speed environments. For doubly-selective channel, the BEM is adopted to reduce the estimation complexity. For non-stationary channel, a channel estimation based on EKF which is able to jointly estimate the time-varying time correlation coefficients and channel impulse response (CIR) is proposed. For further improving the channel estimation accuracy, a ‘filtering and smoothing’ channel estimator structure is designed by introducing the RTSS into channel estimation and interpolation. Simulation results illustrate that the proposed BEM-based EKF-RTSS method show better estimation accuracy, robustness and bit error rate (BER) performance than the traditional methods in high-speed scenarios.
Non-Linear Modeling and Simulation with 3-D Animation for 6-DOF Autonomous Underwater Vehicle

this paper focuses on deriving the equations of motion of generic mathematical model in six degrees of freedom for autonomous underwater vehicles. For precise modeling, we used the hydrodynamic coefficients and derivatives of NPS AUV-II benchmark. The derived nonlinear model is simulated using LabVIEW software package. This makes the simulation process more realistic and easy for implementation with compatible embedded system boards. Moreover, it provides more accurate results which; verifies the model through an animated graphical user interface. This interface facilitates the simulation testing and evaluation process. The open loop simulation was acceptable, responsive and paved the way for using different control techniques for the autonomous underwater vehicle.
Impact of Multiple Primaries and Partial CSI on Transmit Antenna Selection for Interference-Outage Constrained Underlay CR

Transmit antenna selection is a low-complexity multiple-antenna technique that exploits spatial diversity using only one radio frequency chain. We investigate it for an underlay cognitive radio system that operates in the presence of multiple primary receivers and is subject to a constraint on the interference outage it causes at any of the primary receivers. The selection is based on a practically motivated and general partial channel state information (CSI) model in which the secondary transmitter (STx) only knows the channel power gains to a subset of the primary receivers. We derive a novel and general antenna selection rule that provably minimizes the symbol error probability (SEP) of the secondary system. We also derive insightful analytical expressions for its average SEP and interference-outage probability. These apply to a general class of channel fading models and any number of transmit and receive antennas, and include the special cases in which the STx knows channel power gains of all or none of the primary receivers. Our numerical results bring out a new insensitivity of the average SEP of the optimal rule to the interference power threshold when the CSI available is partial.
Info-Trust: A Multi-Criteria and Adaptive Trustworthiness Calculation Mechanism for Information Sources

Social media have become increasingly popular for the sharing and spreading of user-generated content due to their easy access, fast dissemination, and low cost. Meanwhile, social media also enable the wide propagation of cyber frauds, which leverage fake information sources to reach an ulterior goal. The prevalence of untrustworthy information sources on social media can have significant negative societal effects. In a trustworthy social media system, trust calculation technology has become a key demand for the identification of information sources. Trust, as one of the most complex concepts in network communities, has multi-criteria properties. However, the existing work only focuses on single trust factor, and does not consider the complexity of trust relationships in social computing completely. In this paper, a multi-criteria trustworthiness calculation mechanism called Info-Trust is proposed for information sources, in which identity-based trust, behavior-based trust, relation-based trust, and feedback-based trust factors are incorporated to present an accuracy-enhanced full view of trustworthiness evaluation of information sources. More importantly, the weights of these factors are dynamically assigned by the ordered weighted averaging and weighted moving average (OWA-WMA) combination algorithm. This mechanism surpasses the limitations of existing approaches in which the weights are assigned subjectively. The experimental results based on the real-world datasets from Sina Weibo demonstrate that the proposed mechanism achieves greater accuracy and adaptability in trustworthiness identification of the network information.
Development of electromotive amoeba-like hydraulic soft robot

Although a soft robot that transforms its shape according to contacting obstacles or terrain shapes adaptively is expected to be utilized for many types of application, a feed-back control system including sensing units and actuation has not been established. This paper deals with a soft robot, especially a water-driven robot, and discusses a method to measure information about contacting objects such as terrain and obstacles by using pressure sensor. In order to achieve such information, we observed a profile of the pressure sensor, and pointed out comprehensible characteristics of the data. By utilizing such data, the paper suggests one of the appropriate methods for the soft robot.
Detection of Breast Diseases using Numerical Study of Light Propagation

Breast cancer is the most women killing disease worldwide which is easily treated in its early stages before symptoms appear. Therefore, early detection of breast tumor is very significant to reduce the breast cancer death rate. Since the limitations of breast diagnosis techniques, such as X-ray mammography, magnetic resonance imaging, breast ultrasonography and breast biopsies, in early assessment of the breast tissue, optical breast imaging methods have been appeared. In this article, an optical imaging method based on numerical study of light propagation in breast tissue, to measure spatially resolved steady state diffuse reflectance, transmittance and absorbed fraction features, by means of Monte-Carlo simulation was applied for early detection of the breast cancer. The features were measured for 5 mm thick samples of healthy breast tissue and breast tissues with benign and malignant tumors. The obtained results revealed that the features can be used for early detection of breast cancer in addition to the discrimination of benign and malignant tumors which can be very helpful in medical diagnosis of many breast diseases.
Moving Sensitive Data Against Live Memory Dumping, Spectre and Meltdown Attacks

The emergence of cloud computing allows users to easily host their Virtual Machines with no up-front investment and the guarantee of always available anytime anywhere. But with the Virtual Machine (VM) is hosted outside of user’s premise, the user loses the physical control of the VM as it could be running on untrusted host machines in the cloud. Malicious host administrator could launch live memory dumping, Spectre, or Meltdown attacks in order to extract sensitive information from the VM’s memory, e.g. passwords or cryptographic keys of applications running in the VM. In this paper, inspired by the moving target defense (MTD) scheme, we propose a novel approach to increase the security of application’s sensitive data in the VM by continuously moving the sensitive data among several memory allocations (blocks) in Random Access Memory (RAM). A movement function is added into the application source code in order for the function to be running concurrently with the application’s main function. Our approach could reduce the possibility of VM’s sensitive data in the memory to be leaked into memory dump file by 2 5% and secure the sensitive data from Spectre and Meltdown attacks. Our approach’s overhead depends on the number and the size of the sensitive data.
A Traffic Congestion Forecasting Model using CMTF and Machine Learning

Traffic congestion severely affects many cities around the world causing various problems like fuel wastage, increased stress levels, delayed deliveries and monetary losses. Therefore, it is urgent to make an accurate prediction of traffic jams to minimize these losses. But forecasting is a real challenge to obtain promising results for vibrant and ambiguous traffic flows in urban networks. This paper proposes a new traffic jam prediction model using pre-calculated density from node information table based on previous traffic data. In this model, we predicted traffic congestion of an intersection according to its adjacent road's node information table, where node information table contains the traffic density of all incoming lanes of an intersection (node). Besides, for this model, we consider all intersections of a city as individual nodes, and we prepare node information table for each node. Our work can be divided into two parts: (1) we perform time series analysis on previous data of a node and its adjacent nodes, and (2) then apply those calculated values to this model and make the prediction based on it. The forecasted value will always be between 0 and 1. Where 0 means no traffic congestion, close to 0 means low traffic congestion and 1 means heavy traffic or close to 1 means congested traffic lane, accordingly.
A Highly Sensitive Broadband Rectenna for Low Power Millimeter-wave Energy Harvesting Applications

In this paper, a highly sensitive broadband millimeter-wave energy harvesting rectenna has been designed, fabricated, and experimentally investigated. The proposed structure includes a high gain 8-by-1 gap-coupled antenna array and a zero-bias Schottky diode rectifier circuit, to efficiently convert RF to DC signal. In order to further characterize the antenna array circuit, both structures (antenna and rectifier) are individually implemented on thin-film ceramic substrate ($\varepsilon_{\mathbf{r}} =$ 9.9, h = $\mu$m), using an MHMIC (Miniature Hybrid Microwave Integrated Circuits) fabrication process. Then, they are joined together using low-loss wideband waveguide (WR12) to microstrip transitions in order to validate the investigated rectenna configuration. A maximum measured RF to DC conversion efficiency of 49.3 %, and sensitivity level of around −60 dBm were successfully achieved. These results make the proposed rectenna structure an excellent candidate for low power millimeter-wave energy harvesting.
ANOMALY DETECTION IN CROWDS USING MULTI SENSORY INFORMATION

This paper presents, a system capable of detecting unusual activities in crowds from real-world data captured from multiple sensors. The detection is achieved by classifying the distinct movements of people in crowds, and those patterns can be different and can be classified as normal and abnormal activities. Statistical features are extracted from the dataset collected by applying sliding time window operations. A model for classifying movements is trained by using Random Forest technique. The system was tested by using two datasets collected from mobile phones during social events gathering. Results show that mobile data can be used to detect anomalies in crowds as an alternative to video sensors with significant performances. Our approach is the first to detect any unusual behaviour in crowd with non-visual data, which is simple to train and easy to deploy. We also present our dataset for public research as there is no such dataset available to perform experiments on crowds for detecting unusual behaviours.
Automated Labeling of Electron Microscopy Images Using Deep Learning

Searching for scientific data requires metadata providing a relevant context. Today, generating metadata is a time and labor intensive manual process that is often neglected, and important datasets are not accessible through search. We investigate the use of machine learning to generalize metadata from a subset of labeled data, thus increasing the availability of meaningful metadata for search. Specifically, we consider electron microscopy images collected at the National Center for Electron Microscopy (NCEM) at the Lawrence Berkeley National Laboratory (LBNL) and use deep learning to discern characteristics from a small subset of labeled images and transfer labels to the entire image corpus. Relatively small training set sizes and a minimum resolution of $512\times 512$ pixels required by the application domain pose unique challenges. We overcome these challenges by using a simple yet powerful convolutional network architecture that limits the number of free parameters to lower the required amount of computational power and reduce the risk of overfitting. We achieve a classification accuracy of approximately 80% in discerning between images recorded in two operating modes of the electron microscope-transmission electron microscopy (TEM) and scanning transmission electron microscopy (STEM). We use transfer learning-i.e., re-using the pre-trained convolution layers from the TEM vs. STEM classification problem-to generalize labels and achieve an accuracy of approximately 70 % despite current experiments being limited to small training data sets. We present these predictions as suggestions to domain scientists through a web-based tool to accelerate the labeling process with the goal of further validating our approach and improving the accuracy of automatically created labels.