Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards a software-based mobility management for 5G: An experimental approach for flattened network architectures

The number of mobile subscribers, as well as the data traffic generated by them, is increasing exponentially with the growth of wireless smart devices and the number of network services that they can support. This significant growth is pushing mobile network operators towards new solutions to improve their network performance and efficiency. Thus, the appearance of Software Defined Networking (SDN) can overcome the limitations of current deployments through decoupling the network control plane from the data plane, allowing higher flexibility and programmability to the network. In this context, the process of handling user mobility becomes an essential part of future mobile networks. Taking advantage of the benefits that SDN brings, in this article we present a novel mobility management solution. This proposal avoids the use of IP-IP tunnels and it adds the dynamic flow management capability provided by SDN. In order to analyse performance, an analytical model is developed to compare it with NB-DMM (Network-based DMM), one of the main DMM (Distributed Mobility Management) solutions. Additionally, performance is also evaluated with an experimental testbed. The results allow handover latency in real scenarios and numerical investigations to be measured, and also show that SR-DMM achieves better efficiency in terms of signaling and routing cost than NB-DMM solution.
Improved Filter Method for Feature Selection

To solve the problem of high data feature dimension in intrusion detection, a hybrid feature selection method has been proposed to reduce the feature dimension. This approach combines the filter and sequence floating forward search methods. Firstly, the original feature series is sorted by different filter methods, and the top ranked features are selected as the next original feature set. Based on this, the sequence floating forward search method is used to select the optimal feature subset with the Support Vector Machine (SVM) as the classifier. The method can avoid the selection and screening of the features based on the threshold and the evaluation value of single feature and categorical variable. Thereby the high evaluation characteristics obtained by the Filter method may be complementary to the low evaluation characteristics. The result shows that the proposed method can not only effectively reduce the number of features, but also can achieve a better classification performance.
The Contemporary Affirmation of Taxonomy and Recent Literature on Workflow Scheduling and Management in Cloud Computing

The Cloud computing systemspreferred over the traditional forms of computing such as grid computing, utility computing, autonomic computing is attributed forits ease of access to computing, for its QoS preferences, SLA’s conformity, security and performance offered with minimal supervision. A cloud workflow schedule when designed efficiently achieves optimalre source sage, balance of workloads, deadline specific execution, cost control according to budget specifications, efficient consumption of energy etc. to meet the performance requirements of today’ svast scientific and business requirements. The businesses requirements under recent technologies like pervasive computing are motivating the technology of cloud computing for further advancements. In this paper we discuss some of the important literature published on cloud workflow scheduling.
VLSI implementation of timing and control unit (TCU) for memory processor ALU system

This paper describes the analysis and modeling of timing and control unit (TCU) for memory processor ALU system, to improve the microprocessor ALU arithmetic and logic operations performance. The system blocks and the behavior of all the blocks are defined and the logical design is implemented in gate level in the design phase. Then the logical circuits are simulated and all of the subunits are converted into FPGA and VLSI CMOS layout. The TCU and Data Stack Swap (DSS) have been integrated in 0. 12μm, 90nm CMOS technology. The CMOS logic design is preferred for implement low leakage and high-speed model. In this paper, the functioning of TCU, DSS operations with ALU, the design steps and the obtained results are explained. The main achievement is the implementation is single operations per a single clock cycle as well as double operations per a single clock cycle can perform with respect to select the TCU mode.
A Two-Stage Multi-Focus Image Fusion Framework Robust to Image Mis-Registration

Multi-focus image fusion technique is to yield a composite image with all objects in focus. However, most of fusion methods do not obtain the satisfactory performance when the focused objects in the source images are not registered. In this paper, a novel classification and probability optimization based multi-focus image fusion method is proposed, which consists of two main steps. First, by the integration of multinomial logistic regression classifier and random walker based optimization in a two-stage framework, we can extract the focused regions of each source image. Then, in order to construct the fused image, the focused regions are combined together. Experimental results show that compared with other advanced fusion methods, the proposed method can obtain competitive performance in terms of subjective and objective evaluations.
TGRID Location Service in AD-HOC Networks

Geographic addresses are essential in position-based routing algorithms in mobile ad hoc networks, i.e. a node that intends to send a packet to some target node, has to know the target's current position. A distributed location service is required to provide each node's position to the other network nodes. Hierarchical Location Service (HGRID) has been known as a promising location service approach. In this paper we present a new approach called TGRID and describe the performance of a novel multi-level Tree-walk grid location management protocol for large scale ad hoc networks. The Tree-walk grid location service mechanism is evaluated by GLOMOSIM against well known location service protocol HGRID when increasing node density and node speed. It is observed that TGRID outperforms HGRID in terms of packet delivery fraction and storage cost and also maintains low control overhead in a uniformly randomly distributed network.
Parmbsc1: Parameterization and Validation of a new State-of-the-art Force Field for DNA Simulations

Classical force fields are the core of classical simulations, particularly of molecular dynamics (MD), a technique that is changing our view on the structure, flexibility and function of biological macromolecules. Originated from the pioneering work of Lifson's group in the sixties, force fields have been in continuous evolution, improving in each generation the accuracy in the representation of proteins and nucleic acid. Force field development is tightly connected to the refinement of simulation procedures and to the extension of simulation time scales. Thus, as simulation time passed the microsecond barrier, MD simulations have revealed the existence of sorne errors in the default force field for DNA simulations, parmbsc0 (developed in the group). The goal of this thesis is to address these problems by a reparameterization of AMBER force field that aims to represent a wide range of DNA structures under physiological and non-physiological conditions. Keeping a/y parmbscO corrections and parm99 non-bonded parameters; we systematically reparameterized sugar puckering, E, 􀀚 and x torsions using high level QM calculations both in gas phase and solution. The refined force field has been tested for more than 3 years to an unprecedented level of detail, considering a large variety of DNAs, and analyzing structural, mechanical and dynamical properties of the DNAs resulting from the corresponding MD simulations. The new force field parameters, called parmbscl, were able to significantly improve or keep the good results of all DNA molecules simulated, ranging from canonical B-DNA duplexes to unusual DNA structures, correcting known errors of previous force fields. Parmbscl results show high accuracy matching with experimental values, mainly direct experimental observables like NOEs, RDCs, WAXS spectra and behaves well in reproducing long transitions, and a variety of unusual DNA structure. It has shown not be contaminated by overtraining artifacts as shown by de novo predictions on previously unknown structures of DNA. Benchmark of all prominent DNA force field shows that parmbscl provides the best fit to various experimental data and best reproduces averages and profiles of helical parameters, strongly suggesting using parmbscl for simulations of DNA duplexes. We believe that parmbscl will serve as the state-of-the-art force field for DNA simulation for years to come. In the world of RNA simulations, despite the recent efforts to improve the description of RNA in MD simulations, RNA force fields are still far in accuracy from those of DNA. A probable cause could be the incomplete understanding of the mechanism of 2'-OH orientation, which in big extent determines the RNA conformation and most probably serves as the molecular switch. Despite all the existing shortcomings, simulation techniques allow to predict RNA properties and design a new dumbbell structure, with good RISC- activity and large biostability that promise large impact in siRNA regulation of gene activity.
SMU Data

As more organizations and businesses in different sectors are moving to a digital transformation, there is a steady increase in malware, facing data theft or service interruptions caused by cyberattacks on network or application that impact their customer experience. Bot and Distributed Denial of Service (DDoS) attacks consistently challenge every industry relying on the internet. In this paper, we focus on Machine Learning techniques to detect DDoS attack in network communication ﬂows using continuous learning algorithm that learns the normal pattern of network trafﬁc, behavior of the network protocols and identify a compromised network ﬂow. Detection of DDoS attack will help the network administrators to take immediate action and mitigate the impact of such attacks. DDoS attacks are costing enterprises anywhere between $50,000 to $2.3 million per year. We performed experiments with Intrusion Detection Evaluation Dataset (CICIDS2017) available from Canadian Institute for Cybersecurity to detect anomalies in network trafﬁc. We use ﬂow based trafﬁc characteristics to analyze the difference in pattern between normal vs anomaly packet. We evaluate several supervised classiﬁcation algorithms using metrics like maximum detection accuracy, lowest false negatives prediction, time taken to train and run. We prove that decision tree based Random Forest is the most promising algorithm whereas Dense Neural network performs equally well on certain DDoS types but require more samples to improve the accuracy of low sampled attacks.
Evaluating Street Quality for Walkability from 3D Models

Street quality is critical to urban sustainability. Well-designed streets encourage walking behaviour and attract public activities, thus advancing the sustainability of a city as a whole. Currently, most quantificational analyses of street quality are based on two dimensional maps, using network algorithms and economic theories to calculate accessibility and land value or to account for a series of elements such as number of trees and number of courtyards in order to audit quality scores. The analysis from three dimensional models and human-centred views is rarely utilized but could offer new possibilities to provide urban planners and designers with analytical feedback on the built environment and design process. This research explores ways to relate physical features with walking behaviour using Reid Ewing’s Urban Design Tool to measure the street quality for walkability. It applies physical metrics from measuring parameters in 3D models based on the human’s perspective and other building metrics from GIS data online to systematically audit street walkability. To validate this method, we take two separate studies. One is a case study to evaluate the street walkability of two districts in NYC from their 3D models and GIS data and to visualize the scores. The result from digital models and real environment are similar which indicates the feasibility of applying this method in urban scale. The other is a pilot study asking a group of students (N = 8) to evaluate street walkability of 3D models, telling them the scores calculated by this method, to explore how the scores influence their evaluations. This study illustrates the possibility to quantify the evaluation of street quality for walkability using Urban Design Tool in 3D models to inform urban designer’s evaluation in street scale during the design process.
Robotic Search and Rescue through In-Pipe Movement

So far, we have been engaged in the research and development of various kinds of robots that could be applied to in-pipe inspections that existing methods (screw-drive type, parallel multi-modular type, and articulated wheeled type) cannot perform. In this chapter, we categorized each in-pipe inspection robot depending on its configuration and structure, which includes the design of the propulsive mechanism, steering mechanism, stretching mechanism, and the locations of the wheel and joint axes. On the basis of this classification and from a developer’s point of view, we also discussed the various kinds of robots that we have developed, along with their advantages and disadvantages.
GNSS and Inertial Fused Navigation Filter Simulation

A navigation filter simulation and analysis environment was developed through the integration of DRAGON, a high fidelity real-time PNT sensor measurement source, and Scorpion, a modular navigation filter implementation framework. The environment allows navigation filters to be prototyped and tested in varying complex scenarios with a configurable set of navigation sensors including GNSS and IMU. An analysis of an EKF using the environment showed the utility and functionality of the system.
A review on multimodal interaction in Mixed Reality Environment

Mixed Reality (MR) environment proposes to blend together virtual and real environments where both world coexist and can respond to each other. As MR has the understanding of real environment, it is possible for user to use their surrounding as a means to interact with the virtual content. The user interaction in Augmented Reality (AR) and Virtual Reality (VR) are widely explored however very less in MR. Interaction must be intuitive as possible to produce natural user interaction. Multimodal interaction is an advanced interaction techniques when involve the combination of more than one input modalities such as speech with gesture, gaze and gesture, and gaze with speech. The inputs or sensory however are not restricted to just two modalities. Therefore this paper presents a review on multimodal interaction in MR environments.
A Dynamic Method to Adjust Overlapping Community Structures

Identifying all cohesion subgroups in a complex network is a critical issue in social network analysis. Once new trends coming from new actors’ interactions, a community detection method has to adopt an effective mechanism to adjust to structural changes in community composition. This research proposes a novel two-phase distributed method for dynamic overlapping community discovery on the MapReduce framework to improve performance. The first phase implements a static overlapping community detection. First, all neighbors of each node are collected from the complex network, and the TTT algorithm is applied to enumerate all maximal cliques. Following that, all adjacent maximal cliques are merged to complete the overlapping detection operation. In the second phase, the proposed dynamic overlapping community detection method is applied on influence area to adjust the struct of detected community structure. This approach applies MapReduce to adjust detected community’s structure in order to avoid redundant analysis and enhance efficiency. The experiment’s data are derived from YouTube users’ interaction. The static overlapping community detection consists of six groups ranging from 50,000 to 300,000 nodes. The data for the dynamic overlapping community discovery consists of five groups ranging from 2,000 to 10,000 nodes, which are in turn dynamically added to the static overlapping communities to generate the final clustering results.
Implementation of Spectrum Analysis System for Vibration Monitoring

Factory monitoring systems are gaining importance in wide areas of industry. Especially, there have been many efforts in implementation of vibration measurement and analysis for monitoring the status of rotating machines. In this paper, a digital signal processor (DSP) based monitoring system dedicated to the vibration monitoring and analysis on rotating machines is discussed. Vibration signals are acquired and processed for the continuous monitoring of the machine status. Time domain signals and fast Fourier transform (FFT) are used for vibration analysis. All of the signal processing procedures are done in the DSP to reduce the production and maintenance cost. The developed system could also provide remote and mobile monitoring capabilities to operator via internet connection. This paper describes the overview of the functional blocks of the implemented system. Test results based on signals from small-size single phase motors are discussed for monitoring and defect diagnosis of the machine status.
The Accumulative Component Kurtosis in the SVD Based Denoising of the Fault Diagnosis for Rolling Element Bearing of Generator

Rolling bearings are important load-bearing components that are prone to failure, so it is necessary to reveal their failure mechanisms. As the singular value mainly reflect the energy of the decomposed singular components (SCs), the singular value decomposition (SVD) de-noising tend to preserve the significant-energy SCs and ignoring the neglected one, thus weaken singular has the ability to detect the incipient fault of the mechanical systems. In this paper, accumulative component kurtosis based SVD reconstitution scheme is proposed for mechanical signal denoising to detect the incipient fault of the generator systems. With the kurtosis index, the decomposed SCs can be evaluated and sorted not only according to their energy but also the contribution to the de-noising effect. In this way, the sensitive SCs could be taken into calculation effectively. The advantages of the ACK-SVD over traditional approaches are verified by both simulated signals and real vibration data from the rolling element bearing rig. The results proved the incipient fault feature even with heavy background noise could be extracted successfully.
PBloofi: An Enhanced Version of BloofI in Recommender Systems

In this paper, we focus on improving the performance of recommender systems. To do this, we propose a new algorithm named PBloofI which is a kind of hierarchical bloom filter. Actually, the Bloom filter is an array-based technique for showing the items’ features. Since the feature vectors of items are sparse, the Bloom filter reduces the space usage by using the hashing technique. And also, to reduce the time complexity we used the hierarchical version of bloom filter which is based on B+ tree of order d. Since Bloom filters can make a tradeoff between space and time, proposing a new hierarchical Bloom filter causes a remarkable reduction in space and time complexity of recommender systems. To increase the accuracy of the recommender systems we use Probabilistic version of hierarchical Bloom filter. By measuring the accuracy of the algorithm we show that the proposed algorithms not only decrease the time complexity but also have no significant effect on accuracy.
Distributed Group Key Management for Event Notification Confidentiality Among Sensors

There is an increasing involvement of the Internet of Things (IoT) in many of our daily activities, with the aim of improving their efficiency and effectiveness. We are witnessing the advent of smart cities, in which IoT is exploited to improve the management of a city's assets, as well as smart factories, where IoT is paving the way for the forth industrial revolution. These applications and many other ones imply several non-functional requirements to be satisfied by the adopted IoT solution, where security assumes paramount importance. Secure communications among the IoT nodes are strongly needed due to the use of wireless technologies that are easy to eavesdrop, in order to steal valuable information. Accordingly, confidentiality is a fundamental prerequisite, but the existing solutions based on transport-level encryption are ineffective, while the ones with application-level encryption may be too expensive in terms of energy consumption. In this work, we propose a series of solutions and methods to achieve confidentiality with end-to-end guarantees, by using group-based keys within the context of a clustered and distributed key management framework. We have implemented such solutions on top of TinyOS, and assessed their achievable quality by means of the TOSSIM simulator.
An Improved Approach based on Balanced Keyword Weight to Traceability Recovery

Software traceability recovery has become more and more important to the software life cycle recent days with the increasing complexity of software system. However, it is a tedious and error-prone task by manual. Many researchers have devoted themselves in building and predicting traceability links. In this paper, we approach a novel method to calculate text similarity and improve our results by a learning to rank method. And a couple of experiments are conducted to verify our method.
Efficient Influence Maximization Under Network Uncertainty

We consider the influence maximization (IM) problem in a partially visible social network. The goal is to design a decision-making framework for an autonomous agent to select a limited set of influential seed nodes to spread a message as widely as possible across the network. We consider the realistic case where only a partial section of the network is visible to the agent, while the rest is one of a finite set of known structures, each with a given realization probability. We show that solving the IM problem in this setting is NP-hard, and we provide analytical guarantees for the performance of a novel computationally-efficient seed-selection approximation algorithm for the agent. In empirical experiments on real-world social networks, we demonstrate the efficiency of our scheme and show that it outperforms state-of-the-art approaches that do not model the uncertainty.
RACE: Randomized Counter Mode of Authenticated Encryption using Cellular Automata

In this paper, we propose a new Randomized Counter mode of Authenticated Encryption using Cellular Automata, named as RACE. AES-GCM, the NIST standard Authenticated Encryption scheme is efficient but it is vulnerable against some of the known attacks. In our design, we try to overcome the limitations of AES-GCM by exploiting the random evolution of Cellular Automata (CA). Here, the CA is used to make counter values randomized instead of sequential values used in AES-GCM. In addition, to produce the Message Authentication Code (MAC), a non-linear CA-based hash-primitive (NASH) is introduced which avoids the complex Galois field multiplication operations of GHASH of AES-GCM. We show that NASH provides more security over GHASH against Cycling Attack. Thus, NASH together with AES makes RACE more secure than AES-GCM with respect to this attack.