Miracle-dz/UniFAR-FTUs huggingface dataset:
Compression Behavior of Al-Mg Phases, Molecular Dynamics Simulation

Aluminum alloys development always exit in the manufacturing process. Al/Mg alloys have been attracted significant attention because of their excellent mechanical properties. The microstructural evolution and deformation mechanisms are still challenging issues, and it is hard to observe directly by experimental methods. Accordingly, in this paper atomic simulations are performed to investigate the uniaxial compressive behavior of Al/Mg phases; with different ratio of Mg ranging from 31% to 56%. The compression is at the same strain rate (3.1010 s⁻¹), at the same temperature (300K) and pressure, using embedded atom method (EAM) potential to model the interactions and the deformation behavior between Al and Mg.From these simulations, we get the radial distribution function; the stress–strain responses to describe the elastic and plastic behaviors of β-Al3Mg2, ε-Al30Mg23, Al1Mg1 and γ-Al12Mg17 phases with 31, 41, 50 and 56% of Mg added to pure aluminum, respectively. The mechanical properties, such as Young’s modulus, elasticity limit and rupture pressure, are determined and presented. The engineering equation was used to plot the stress-strain curve for each phase.From the results obtained, the chemical composition has a significant effect on the properties of these phases. The stress-strain behavior comprised elastic, yield, strain softening and strain hardening regions that were qualitatively in agreement with previous simulations and experimental results. These stress-strain diagrams obtained show a rapid increase in stress up to a maximum followed by a gradual drop when the specimen fails by ductile fracture. Under compression, the deformation behavior of β-Al3Mg2 and γ-Al12Mg17 phases is slightly similar. From the results, it was found that ε-Al30Mg23 phase are brittle under uniaxial compressive loading and γ-Al12Mg17 phase is very ductile under the same compressive loading.The engineering stress-strain relationship suggests that β-Al3Mg2 and γ-Al12Mg17 phases have high elasticity limit, ability to resist deformation and also have the advantage of being highly malleable. From this simulation, we also find that the mechanical properties under compressive load of ε-Al30Mg23 phase are evidently less than other phases, which makes it the weakest phase. The obtained results were compared with the previous experimental studies, and generally, there is a good correlation.The Al-Mg system was built and simulated using molecular dynamics (MD) software LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator).
Effect of Water Temperature, pH Value, and Film Thickness on the Wettability Behaviour of Copper Surfaces Coated with Copper Using EB-PVD Technique

This research investigates the effect of surface roughness, water temperature, and pH value on the wettability behaviour of copper surfaces. An electron beam physical vapour deposition technique was used to fabricate 25, 50, and 75 nm thin films of copper on the surface of copper substrates. Surface topographical analysis, of the uncoated and coated samples, was performed using an atomic force microscopy device to observe the changes in surface microstructure. A goniometer device was then employed to examine the surface wettability of the samples by obtaining the static contact angle between the liquid and the attached surface using the sessile drops technique. Waters of pH 4, 7, and 9 were employed as the contact angle testing fluids at a set of fixed temperatures that ranged from 20°C to 60°C. It was found that increasing the deposited film thickness reduces the surface roughness of the as-prepared copper surfaces and thus causing the surface wettability to diverge from its initial hydrophobic nature towards the hydrophilic behaviour region. A similar divergence behaviour was seen with the rise in temperature of water of pH 4, and 9. In contrast, the water of pH 7, when tested on the uncoated surface, ceased to reach a contact angle below 90o. It is believed that the observed changes in surface wettability behaviour is directly linked to the liquid temperature, pH value, surface roughness, along with the Hofmeister effect between the water and the surface in contact.
Effect of Microstructure and Surface Energy on the Static and Dynamic Characteristics of FG Timoshenko Nanobeam Embedded in an Elastic Medium

This paper presents an investigation of the size-dependent static and dynamic characteristics of functionally graded (FG) Timoshenko nanobeams embedded in a double-parameter elastic medium. Unlike existing Timoshenko nanobeam models, the combined effects of surface elasticity, residual surface stress, surface mass density and Poisson’s ratio, in addition to axial deformation, are incorporated in the newly developed model. Also, the continuous gradation through the thickness of all the properties of both bulk and surface materials is considered via power law. The Navier-type solution is developed for simply supported FG nanobeam in the form of infinite power series for bending, buckling and free vibration. The obtained results agree well with those available in the literature. In addition, selected numerical results are presented to explore the effects of the material length scale parameter, surface parameters, gradient index, elastic medium, and thickness on the static and dynamic responses of FG Timoshenko nanobeams.
Numerical Study of the Biomechanical Behaviour of the Different Implantation Methods of the Reverse Shoulder Replacement

Despite the widespread use of reverse total shoulder arthroplasty, there is still a problem of conflict between the polyethylene cup of the prosthesis and the scapula, which over time causes the phenomenon of notching. In order to circumvent this problem correctly, several innovations have been proposed regard to the implementation method. In this context, the aim of this work is to study the biomechanical behavior of new implantation methods using different glenoid configurations in order to avoid the notching phenomenon between the cup and the scapula. The study was performed using virtual prototypes of the shoulder prosthesis assembly. Using CT scan images, three-dimensional models of shoulder bones were reconstructed. The implantation of the prosthesis in the three-dimensional model was performed in collaboration with an experienced surgeon from the Caduceus Clinic (Oran, Algeria). The numerical models were imported to finite element calculation software. After the validation of the numerical model using the literature results, we assessed the biomechanical behavior of four implantation methods under the same boundary conditions and abduction movements. From the obtained results, it was found that among the proposed methods, the BIO-SR lateralization method offers significant biomechanical advantages in terms of the forces applied to the glenoid during the abduction movement.
A Mathematical Model for Root Canal Preparation Using Endodontic File: Part B

The aim of this article is to develop the mathematical model to describe response of endodontic file in curved root canal during the preparation of root canal using fracture mechanics approach. Any obturation process involves the filling of prepared root canal using bio-compatible materials like gutta-percha. During preparation of infected root canal, substrate, dead tissue and pulp is removed and tapered shape is formed so that any practitioner can fill it effectively. During preparation process of root canal, the canal wall applies locking action and causes resistance to motion of endodontic file, finally resulting into fatigue failure. This article described plastic behavior of endodontic file in curved root canal, mathematical model describing the necessary condition for crack growth in endodontic file, a mathematical model describing plastic zone size for crack on surface of endodontic file, a mathematical model describing crack tip opening displacement for crack on surface of endodontic file and mathematical model for pure-combined torsion and bending consideration for design of endodontic file. The mathematical model described is helpful for the Endodontic experts, researchers, design engineers. However the applicability of the described mathematical model limited to assumption of study. The gap between root canal to be prepared and endodontic file is zero while preparation. The endodontic file weight, speed of rotation and substrate removal rate is assumed to be constant. The mathematical model for endodontic file discussed above proved to be efficient tool for studying the root canal preparation.
Optimization of distributed detection in energy harvesting wireless sensor networks with multiple antenna fusion center

This paper addresses the detection improvement using multiple antenna fusion center (FC) and optimal collaboration among sensors with energy harvesting (EH) capability in WSNs. Using multiple antennas at FC, we benefit from the available fundamental spatial diversity in distributed WSN. We formulate to maximize the probability of collaborative detection based on parameters of collaboration among sensors and the energy of each sensor for the different scenarios as the optimization problem. In particular, we consider several practical constraints for EH and battery energy storage to maintain network connectivity. The objective function is the nonconvex function so the optimization problem is obtained as the nonconvex problem. To resolve this issue, the objective function is converted to a convex function using a relaxation method, and an optimum solution is obtained for this problem using local optimum values. The numerical results show the impact of the different parameters on the performance of the collaborative decision. These results also demonstrate that using distributed spatial diversity at FC by multiple antennas leads to a better decision in terms of error probability, and distributed wireless sensor‐enabled with EH capability results to more robust network connectivity in practice.
Secure and Efficient Client-Side Data Deduplication with Public Auditing in Cloud Storage

In this paper, we propose a secure and eﬃcient client-side data deduplication scheme with public auditing. In the process of deduplication, the proposed scheme improves the probability that the cloud server detects the missing blocks by eliminating the aggregated proofs structure. Meanwhile, we combine the oblivious pseudo-random function protocol with proxy re-encryption technology to implement key distribution without online data owners or the authorized party. Moreover, during public auditing, proxy re-signature technology is utilized to require only one auditing tag for each data block. For data deduplication, the proposed scheme is a zero-knowledge proof of knowledge assuming that the Discrete Logarithm (DL) problem is hard. In addition, the symmetric key can not be recovered by the cloud server or malicious users. And security analysis indicates that our scheme is secure against adaptive chosen-message attack under the Computational Diﬃe-Hellman (CDH) assumption during public auditing. Finally, the performance evaluation demonstrates that the proposed scheme is practical and eﬃcient.
Joint optimization of computation cost and delay for task offloading in vehicular fog networks

The vehicles equipped with computing devices can provide computing services in vehicular fog networks, thereby reducing latency and improving system efficiency. Hence, we face with the problem of how to select vehicles for the computation offloading of the task. In this paper, we jointly consider the delay and cost, and study the resource allocation problem of multivehicles users. First of all, we construct a hierarchical offloading model, use mathematical method to construct consumption function of the system, and generate a convex optimization problem with inequality constraints. Then, we use Lagrange method to solve the problem and develop a low‐complexity algorithm to select vehicles, optimize the offloading ratio, and reduce the consumption of the system. Numerical results show that the algorithm has a fast convergence rate and good performance.
Intrusion Detection Method Based on Support Vector Machine and Information Gain for Mobile Cloud Computing

Intrusion detection system (IDS) has become an important security method that monitors and investigates the network security in mobile cloud computing (MCC). However, in some existing methods, there are still some limitations such as high false positive rates, low classiﬁcation accuracies, and low true positive rates. To counter these limitations, an intrusion detection method based on support vector machine (SVM) and information gain (IG) for MCC was proposed in this paper. In the proposed method, the SVM classiﬁer is adopted to classify network data into normal and attack behaviors, and due to the irrelevant and redundant features found in KDD datasets, IG is used to select the relevant features and remove unnecessary features. The KDD’99 and NSL-KDD datasets are used to evaluate the eﬀectiveness of the proposed method. Compared with other methods, the experimental results show that the proposed method can detect malicious attacks with high accuracy, true positive rate, low false positive rate and high training speed.
Resource allocation in 5G heterogeneous networks with downlink‐uplink decoupled access

Fifth‐generation (5G) heterogeneous network (HetNet) with downlink (DL) and uplink (UL) decoupled cell association strategy is a promising solution to challenges faced in fourth‐generation (4G) HetNet, ie, mitigating interference, addressing traffic imbalances, and enhanced sum‐rate. This work carries out performance analysis of 4G HetNet with DL and uplink coupled (DUCo) access scheme vs 5G HetNet with DL and UL decoupled (DUDe) access scheme employing outer approximation and heuristic algorithms. First, a mathematical model, mixed integer nonlinear programming (MINLP) problem, is formulated for DUCo access and DUDe access schemes considering cell association, addressing user traffic imbalances, mitigating interference, and sum‐rate maximization in HetNet. Then, the formulated problem is solved, employing outer approximation algorithm (OAA) to find near optimal solution. Similarly, heuristic algorithms are developed for DUCo access and DUDe access schemes considering cell association, addressing user traffic imbalances, mitigating interference, and sum‐rate maximization in HetNet. Detailed performance analysis of DUCo access and DUDe access schemes is done by comparing results employing OAA and heuristic algorithms. Simulation results have shown that proposed DUDe access scheme outperforms DUCo access scheme in HetNet in term of cell association, addressing user traffic imbalances, mitigating interference, and sum‐rate maximization.
Using Parametric t-Distributed Stochastic Neighbor Embedding Combined with Hierarchical Neural Network for Network Intrusion Detection

Parametric t-distributed stochastic neighbor embedding (t-SNE) algorithm is a kind of unsupervised dimensionality reduction method which is widely used and eﬀectively. However, current research rarely involves the application of parametric t-SNE in network attack detection. Simultaneously, it is rare to apply a reasonable model for parametric t-SNE. Therefore, we propose a novel unsupervised dimensionality reduction algorithm to detect attack behaviors, which uses t-SNE combined with a hierarchical neural network. This algorithm maps a high-dimensional network data space into a low-dimensional latent space. Furthermore, we evaluate the performance of the parametric t-SNE method in experiments using two public network intrusion datasets and a self-collected network dataset. In experiments, several unsupervised dimensionality reduction algorithms are discussed and compared with the algorithm we proposed. This comparison shows that parametric t-SNE based on hierarchical neural network gets excellent dimensionality eﬀect, which achieved a maximum of 99% accuracy for 1-nearest neighbor.
Edge computing and power control in NOMA‐enabled cognitive radio networks

Due to the limited computation resources of mobile devices in cognitive radio networks, the secondary users in the network can suffer from long executing time, which is not acceptable for latency‐sensitive and computation‐intensive tasks. To tackle this issue, this paper proposes to reduce the task computing latency for secondary networks by offloading the tasks to edge servers through leveraging mobile edge computing (MEC) that is emerging as a promising technology to augment the computation capacity of mobile devices. Specifically, under the conditions that the interference caused by secondary users is tolerable to primary user and within the available computation resources of the MEC server, the primary user and secondary users both can offload tasks to the MEC server through nonorthogonal multiple access. Thus, we jointly formulate the offloading decision and power control as an optimization problem, aiming at minimizing the overall computing latency for secondary networks. To overcome the computational complexity caused by the nonconvexity of the original problem, we transform the original problem to a solvable problem and decouple the transformed problem into the separate offloading decision and power control. An iterative algorithm is proposed based on block coordinate decent method to achieve the near‐optimal solution. Simulation results show that under the same parameters, such as the number of primary users, maximum transmit power, computational capability of the MEC server and the computational capability of the secondary users, the proposed NOMA‐enabled computation offloading scheme can effectively reduce the overall computing latency for the secondary network and improve the percentage of offloading secondary users than those of OMA‐enabled.
NEUMITLL @ TRECVid 2015: Multimedia EventDetection by Pre-trained CNN Models

We introduce a framework for multimedia event detection (MED), which was developed for TRECVID 2015 using convolutional neural networks (CNNs) to detect complex events via deterministic models trained on video frame data. We used several well-known CNN models designed to detect objects, scenes, and a combination of both (i
Trimps at TRECVID 2015: Localization and Instance Search

This paper introduces our work at the automatic instance search task and localization task of TRECVID 2015 [11]. We introduce localization (LOC) task in details, especially data collection and two methods we introduced: NeC (Negative Clustering) and extra convolutional layers. Then our methods used in instance search (INS) task were summarized briefly
面向点云的三维物体识别方法综述 (Survey of 3D Object Recognition for Point Clouds)

With the rapid development of 3D scanning technology,it is convenient to obtain point clouds of different scenes.Since point clouds are not influenced by light,shadows and textures,recognizing 3D object from scene point clouds has become a research hotspot of computer vision.This paper first summarized the 3D object recognition methods from point clouds in recent years.Then the advantages and disadvantages of the existing methods were discussed . Final— ly,the challenges and further research directions of object recognition were pointed out.
Detecting Improper Behaviors of Stubbornly Requesting Permissions in Android Applications

Android applications may stubbornly request permissions at initialization: if the user does not grant the requested permissions, these applications would simply exit, refusing to provide any functionalities. As a result, users are urged by this behavior to grant sensitive permissions and users actually lose the power to control their sensitive data, which may cause permission abuse and privacy leakage. In this paper, we propose an approach to automatically detect the improper behaviors of stubbornly requesting permissions. Experiments on real-world applications demonstrate the eﬀectiveness of our approach and reveal that almost 24% analyzed applications contain stubborn permission requests
Chaotic NHCP: Building an Efficient Secure Framework for Cloud Computing Environment Based on Chaos Theory

Cloud computing is an advanced trend, which provides access to applications and resources over the internet. In a cloud computing environment, the data is stored on remote servers accessed through the internet. The increasing volume of necessary data brings up more focus on securely storing data. Encryption plays a vital role in security for diﬀerent types of data. The existing meth-ods encrypt all data using the same key without taking into account the conﬁdentiality level of data, which in turn will increase the encryption time. In this research, a novel encryption algorithm based on chaos theory in the cloud computing environment is developed. The new hybrid cryptography algorithm based on chaotic mapped called (Chaotic NHCP). Chaotic NHCP uses a classiﬁcation method. The new framework of data encryption operates as follows, Firstly, KNN method is used to classify the data credibility level, and then Fast RSA algorithm and blowﬁsh algorithm are used to encrypt the data to achieve the eﬀect of Fast data encryption. The objects are classiﬁed by a maximum value of its neighbours, with the object being assigned to the class with most common among its K-nearest neighbours. Then, the 32-bit plaintext data was split into two 16-bit plaintext data, and the 32-bit ciphertext data was synthesised after encryption by Fast RSA and Blowﬁsh hybrid algorithm, respectively. The proposed method was tested with different encryption algorithms and evaluated according to the encryption time, throughput and power consumption. The experimental results show that the Chaotic NHCP method minimises the encryption time needed to secure data that leads to a suitable conﬁdentiality level required for the data. In addition, it has high throughput and low power consumption along with time-saving. The proposed method has proven the superior in the performance of processing time when compared with other encryption algorithms.
Mitigation of DIS flooding attacks in RPL‐based 6LoWPAN networks

The IPv6 Routing Protocol for Low‐Power and Lossy Networks (RPL) is the de facto routing protocol for IPv6‐based Low‐Power Wireless Personal Area Networks (6LoWPAN). In RPL protocol, DODAG Information Solicitation (DIS) messages are sent by the node to join the network. A malicious node can exploit this mechanism to send illegitimate DIS messages to the neighbor nodes to perform a DIS flooding attack. In this research paper, it is observed that the DIS flooding attack increases the control packet overhead of the network, which significantly degrades the network's performance. This further increases the power consumption of the nodes in the network. To address this problem, a mitigation scheme named Secure‐RPL is proposed. The proposed scheme mitigates significantly the effects of DIS flooding attack on the network's performance. The effectiveness of the proposed Secure‐RPL scheme is compared with standard RPL protocol. The experimental results show that Secure‐RPL detects and mitigates DIS flooding attack quickly and efficiently in both static and dynamic network scenarios, without adding any significant overhead to the nodes.