Miracle-dz/UniFAR-FTUs huggingface dataset:
Forex Prediction through Financial Trend Analysis

Forecasting the exchange rates has been approached through several ways over the years as the exchange rates of a country holds very high importance in giving us an insight into the economy of a country which largely influences the trade and economic relations. We propose a solution to forecast the currency exchange rates by using a bi-directional Gated Recurrent Unit (GRU) model that learns the variations in different economic trends to forecast the currency exchange rates of Indian rupee when compared to a unit US dollar. The data for six different economic trend features from 1970 to 2020 of both India and USA were collected and used to train the forecasting model. The model was able to acheive an MSE value of 0.000704.
Black-Box Adversarial Windows Malware Generation via United Puppet-based Dropper and Genetic Algorithm

The arms race for Windows PE malware evasion and detection is ongoing. Recently, researchers have found that learning-based detectors are vulnerable to the attacks of adversarial examples. Therefore, the study on the generation of adversarial PE malware is of great significance and practical value for enhancing existing detection mechanisms. Current adversarial PE malware generation methods have several problems: destroying original functions, low efficiency, poor stability, weak flexibility, poor stealthiness, and lack of universality. We proposed a new black-box adversarial PE malware generation framework. It uses a novel Dropper processing method based on a puppet file combined with a size-penalized and entropy-constrained genetic algorithm that can efficiently generate adversarial PE malware with highly stealthy and universality. Experiments proved that our method could escape the target detectors with an extremely high success rate. In the case of bypassing commercial engines, the adversarial malware escaped an average of 37.76 engines more than the original malware. In addition, the results showed that exploring the method of generating adversarial PE malware can reveal the weaknesses of a target detector and thus improve its defensive capabilities by conducting data poisoning and dataset augmentation experiments.
Analysis of Cyber Defense mechanisms using honeypots in Cloud Environment

The demand for IT infrastructure is increasing at an exponential rate as markets migrate to the digital environment. Cloud computing services are becoming more and more enticing to individuals as well as organizations because of its openness and ease of use. Additionally, Cloud Computing provides IT resources at low to no capital costs and the pay-as-you-go model gives the required incentives to startups and new market players. Nevertheless, the sheer enormity of cloud-based services, as well as the enormous volume of data stored there, attracts both attackers and victims. Attack vectors on cloud vary from Insider attacks to Account Hijacking and from Cloud injection to Denial-of- Service attacks. Problems like loss of control and databreaches stem from aforementioned attack vectors that lead to lack of trust among clients. Thus, the security aspect of cloud environments has become paramount.Honeypots are simple mechanisms that are designed to detect and fend off malicious requests away from critical institutional resources. Honeypots predominantly implement virtualization techniques to create virtual systems and environments with apparent vulnerabilities to attract potential attackers. Initial attempts in the area of Cloud security, as well as the approaches utilized to safeguard and protect these systems, are incorporated into this article. Additionally, this article also focuses on Honeypot technologies and compares them to anomaly detection and signature-based intrusion detection.
Energy Efficient Dynamic Load Balancing using Self-Organized Criticality in Grid Computing

Grid computing refers to a collection of compute resources from different geographic locations to achieve a common objective. It involves tremendous amounts of computational task which require reliable resource sharing across the grid for efficiently executing the tasks. Load disparity is a major issue in grid computing. So effectively and efficiently utilize the potentials of huge distributed resources, load balancing algorithms are of primary importance. In this paper, we proposed an elegant approach using Bak-Tang-Wiesenfeld sandpile model for dynamically load-balancing of tasks across the processing sites in an energy efficient manner. The proposed approach reduces redundant migrations between processing sites by transferring tasks in a breadth first manner. In this approach avalanche propagation condition is stricter and depends on the average load of two adjacent layers of neighbours and use of signalling mechanism rather than actual transfer of tasks during an avalanche, until a destination comes in equilibrium. We experimentally validated by a number of numerical experiments using Google traces. The results of the experiments show the effectiveness of the proposed approach which reduces the number of migrations and improves the energy efficiency without much increasing the total flowtime and degrading the load balancing aspects as compared to state-of-the-art approaches.
Numerical calculation on multi-layers solenoidal coil

A method involving the use of computer-aided design to predict accurately the inductance and the divided current among layers of a coreless solenoidal coil with parallel connected multilayers is introduced. The skin and proximity effect of the current-carrying turns can also be evaluated. The adjusted number of turns of each layer is superior to the transposition turns in leading to reasonably divided currents among the layers. The inner layer of the solenoidal coil has a more serious skin effect than the outer layers. >
Application of MDE in the Development of IoT Systems: A Simulation-Based Approach

The Internet of Things (IoT) systems are based on heterogeneous hardware components ranging from microcontrollers to powerful cloud servers. The development of IoT applications requires expertise in many fields. Therefore, developing IoT applications is a challenging issue. This paper presents an approach that can help users to build and test IoT applications. We propose to use the ThingML domain-specific language to describe the application in a platform-independent way. In order to facilitate the modeling process, we develop a modeling editor for the ThingML language. This editor combines textual and graphical notations to accumulate their advantages. After modeling, the resulting models will be used to generate the application source code for multiple platforms using the ThingML code generation framework. We adopt a simulation approach using the Proteus software to test the application. Proteus enables the design of the application hardware circuit. Furthermore, it allows simulation of the execution of the previously generated source code. Experimental results show that our approach can reduce the effort and time needed to build IoT applications. Thanks to the ThingML framework, which allows the generation of fully operational code. Also, the users can test their applications without the availability of IoT components.
Fault-tolerance Optimization of Reconfigurable Manufacturing Systems: Multi-objective Artificial Bee Colony Algorithm for Process Planning

A reconfigurable manufacturing system (RMS) is a modern manufacturing system class that aims to provide functionality and capacity as and when needed. Reconfigurable machine tools (RMTs) are the main component of RMS, which have a modular structure, such that they can be reconfigured into different configurations. In RMSs, process planning aims to assign to each operation the appropriate configuration. During the fulfillment of operations along the process plan, RMTs may defect. Monitoring the state of RMS during process planning allows for diagnosing the defect of broken-down components. After detecting the defect, the repairing team of the company is responsible for repairing this defect. Furthermore, periodic maintenance for the system components is required for facilitating the monitoring of RMS. The accomplishment of all steps of each process, i.e., monitoring, maintenance, and repairing, requires time and a specific cost. This paper deals with the problem of maximizing fault-tolerance optimization in RMSs. A multi-objective optimization model is proposed considering the minimum total time and minimum total cost of process plans incorporating repair time of configurations and maintenance cost of configurations, during the process planning. This problem represents an NP-hard problem, for solving it, the multi-objective artificial bee colony (MOABC) algorithm is adapted to generate a set of non-dominated solutions (i.e., process plans). With the help of a numerical example, the results obtained by MOABC are compared with a well-known meta-heuristic algorithm, called non-dominated Sorting Genetic Algorithm-II (NSGA-II), and demonstrated the superiority of MOABC over the NSGA-II in terms of solution quality, convergence, and solutions' diversity.
Privacy Preserving Fingerprint Authentication based on Homomorphic Encryption and Blockchain

Biometric authentication methods generally rely on centralized authority such as centralized database servers to store biometric templates and manage authentication. These methods suffer from different points of attack. If the central entity is compromised, the system becomes vulnerable and unable to ensure integrity of stored templates. This paper proposes a distributed scheme of biometric authentification, eliminating the need for a central entity. The proposal is based on the Ethereum blockchain, which offers decentralized and irreversible properties for the storage and management of biometric templates. For each user, the fingerprint template is encrypted using homomorphic encryption and stored on the smart card. The hashed encrypted vector is then stored on the blockchain to ensure its confidentiality. Our proposed scheme, allows authentication of users using a smart contract, while the distance calculation is performed on the encrypted domain that achieves the integrity of the calculation results. By using hashed vectors, the proposal is efficient and cost-effective. These improvements provide solutions to many problems in biometric systems such as template modification, channel interception, and override comparator.
Fire Detection Scheme in Tunnels Based on Multi-source Information Fusion

Multi-sensor information fusion technology is an effective method for fire detection. However, in the underground road scenario, due to the closed environment and dispersed sensor layout, common fire detection data fusion methods have defects of poor detection timeliness and low accuracy. Therefore, this paper proposes a new fire detection scheme combining BP neural network and D-S evidence theory, and further puts forward a evidence correction method based on exponential entropy. We compare this method with common methods, and the experimental results show that the new method can detect the fire at the earliest in both open fire and smoldering fire scenes of underground roads, which improves the real-time performance and accuracy of fire detection.
Energy-Efficient Thermal-Aware Data Placement Strategy for Storage Clusters

The explosion of large-scale data has increased the scale and capacity of storage clusters for data centers, leading to colossal power consumption issues. In thermally managed storage clusters, the limited data placement approach has become a key factor affecting the overall power consumption of a data center. In this regard, we propose an energy-efficient thermal-aware data placement strategies called ETDP to reduce the overall power consumption of data centers. The principle of our proposed algorithm is to use a greedy algorithm to calculate the optimal disk sequence at the lowest overall power of a data center and then place the data in the optimal disk sequence. We implement this strategy in a cloud computing simulation platform based on CloudSim. Experimental results unveil that ETDP outperforms MinTin-G and MinTout-G in terms of the supplied temperature of CRAC, storage nodes power, cooling cost, and total power consumption. In particular, the ETDP algorithm can save about 10%-40% of the overall power consumption compared to MinTout-G and MinTin-G algorithms.
Heterogeneous Propagation Graph Representation Learning for Fake News Detection

Recently, social network platforms have led to a deluge of fake news, which tends to mislead readers and may arouse public spark panic. Thus, detecting fake news on social networks re-mains an important role. Previous researches mainly focused on discovering features from diffusion paths and news content. However, these researches insufficiently exploit dynamic interactions on social networks to refine representations of social roles. Therefore, we proposed hetero-geneous propagation graph representation learning (HPGR), which exploits heterogeneous interaction behavior and non-Euclidean propagation preferences of news to improve the detection performance of fake news. Different from previous works, the HPGR transfers spatial propagation activities into a heterogeneous propagation graph. Centered on the news, the graph iteratively traces its publisher representation and user representation to fully learn the static and dynamic representations of nodes. Specifically, we develop a publisher-based attention module to learn publisher representation in the news propagation network, which can capture the shared semantics of publisher's news more accurately and facilitate the fine-grained representation. Then, the fine-grained user representations are constructed by user similarity and pref-erences in the news propagation. Further, the representations of news contents are reconstructed through propagation and aggregation of propagation graph neural network, which inter-pretively describes the feedbacks of news propagation process. Finally, the reconstructed representations of news contents are applied for fake news detection task. The experimental results show that the HPGR can improve the accuracy of fake news detection and provide a novel paradigm for fine-grained heterogeneous graph representation learning.
Approach for Predicting Mobile Malware

Mobile malware is increasing the number of malicious behaviours such as capturing users' information, delivering subscription SMS, and making calls to premium lines without their awareness. Several studies have explored strategies to counter these assaults in order to build an effective detection method. In the world of security, mobile security has been a quickly rising field. The demand for mobile security has risen considerably in recent years as the number of mobile devices and applications has grown. To deal with enormous no. of harmful mobile apps, a variety of malware detection techniques are reported in various studies. In this research, we offer hybrid method for determining most optimal parameters for detecting mobile malware. We developed a system for predicting mobile malware using a hybrid of ANFIS and NIMOA in our hybrid method. In compared to previous optimization methods, evaluations using real-world datasets illustrate the usefulness of our approach.
MACC: MEC-Assisted Collaborative Caching for Adaptive Bitrate Videos in Dense Cell Networks

Caching adaptive bitrate video at edge nodes (ENs) can provide multi-version video-on-demand (VoD) services to end users (EUs) with better experience. However, due to the limited cache capacity of ENs, it is important to decide which video content and corresponding bitrate version to be cached in the EN. In this paper, we first propose a user request hit profit (RHP) model, and then based on the RHP model we envision a mobile edge computing (MEC)-assisted collaborative caching scheme (MACC). Specifically, we model the communication links between ENs and EUs as a bipartite graph to employ the collaborative caching among ENs; and we consider the transcoding relationship between different versions to effectively utilize the processing capacity of ENs. Due to the NP-completeness of the cache placement problem, we prove it is a monotone submodular function maximization problem, and propose the proactive cache placement based on maximum RHP increment (PCP-MRI) algorithm and the reactive cache replacement based on maximum RHP increment (RCR-MRI) algorithm. Extensive simulation results show that, compared with existing methods, the proposed MACC has significant performance improvements in cache hit ratio, initial waiting delay and backhaul traffic load.
An Efficient CL-Signature scheme over NTRU Lattices

The advantages of key escrow and certificate management features over a public key, and identity-based infrastructure has led to extensive use of the CL-Signature in ebusiness, e-government, and programming security to address these underlying issues. Numerous CL-Signature techniques have been proposed based on the elliptic curve, RSA, and chaos. However, the majority of them are vulnerable to attacks using quantum computing. But in quantum time, the lattice-based CL-Signature technique is safe. Therefore, the CL-Signature Scheme, suggested in this study and shown to be secure in post-quantum environments, is implemented on the NTRU lattice. Additionally, the novel approach is more efficient than earlier lattice-based CL-Signature schemes.
Mixed Compositional Pattern-Producing Network-NeuroEvolution of Augmenting Topologies Method for the Locomotion Control of a Snake-Like Modular Robot

Snake robots, which are a type of bionic robots, have high adaptability to various environments. Because of the unique structure and many degrees of freedom (DoFs) of snake robots, these robots can move on rough terrain or in narrow spaces. However, developing a controller for the locomotion of snake robots with many DoFs is challenging. A control model developed for a snake robot moving in one environment might be unsuitable in other environments. Thus, a mixed compositional pattern-producing network (CPPN)-NeuroEvolution of Augmenting Topologies (NEAT) method is proposed in this paper. This method is based on a neuroevolution algorithm and serpentine locomotion, and it can be used to control the locomotion of a snake-like modular robot in multiple environments with different obstacles. In the proposed method, a group of neural networks is constructed for each environment, and these networks are trained using a neuroevolution algorithm for several generations. Some pretrained neural networks for each environment are selected and then integrated to obtain mixed neural networks. These mixed neural networks are then evolved using a multiobjective genetic algorithm (MOGA) to improve the locomotion of a snake-like modular robot in multiple environments. The results of this study indicated that the mixed CPPN-NEAT method outperformed the combined CPPN-NEAT method and use of MOGA alone (without pretraining, combined networks, and mixed networks) in locomotion control for a snake-like modular robot in three environments with different obstacles.
A Novel Semi-supervised Algorithm to Find Accuracy in Fake Review Detection using Comparing with K-neighbours Algorithm

This study topic is centred on semi-supervised algorithms and k-neighbors algorithms for optimising false review identification in an effort to find the accuracy of real time fake review detection. The NSS parameter and the random forest parameter are both adjusted in order to simulate the N-neighbors algorithm (N=32) and the K-neighbors algorithm (N=32). This is done in order to optimise the pH. In this work, a total of 20 samples were utilised, and the sample size was determined by using the Gpower 80% formula to each of the two groups. According to the findings, the accuracy achieved by the NSS algorithm is much higher (75.60%) than that achieved by the Random forest method (74.50%). It was determined that the difference in statistical significance between the semi-supervised algorithm and K-neighbors was 0.916 (p>0.05). When it comes to detecting fraudulent reviews and improving accuracy %, the results produced by the semi-supervised algorithm are superior to those produced by the K-neighbors approach.
Optimization of Delay Tolerant Routing using Machine Learning Techniques

This paper presents the machine learning approach to improvise the routing decisions in DTN routing. Routing strategies in DTN is basically to choose a suitable nodes to transfer message copies for the successful transfer of messages to the destination node. The routing decision, whether to transfer the data to the node encountered or not is considered similar to multi-label classification problems. We have used five multi-label classification techniques for finding the optimum technique for this task on Zebranet UTM-1 data for PRoPHET Routing and Epidemic Routing. The techniques used are Ensemble chain classifiers (ECC), CC(Chain classifiers), BR(Binary relevance), Label Power-set problem transformation and OneVsRest. We have used 5 classifiers as the base learners like XGBoost, k-NN, D-Tree, Naive Bayes, and Random Forest, . The library used for parameter optimization of the classifiers are hyperopt library and GridSearchCV. Ensemble chain technique with XGBoost classifier on PRoPHET routing data outperformed all the techniques for the PRoPHET and epidemic routing with an accuracy score of 96.1%, Jaccard score of 92.08% and f-1score score 94.47%.
Ellipsoid Pricing Based Budget-Constraint Incentives for Noisy Crowdsensing

Recently, online pricing receives extensive attentions for addressing more realistic mobile crowdsensing (MCS) applications. However, existing these works mainly focus on finding an optimal price by repeated price experiments of user's acceptance decision, which incurs a desirable regret. In practice, there are also many other factors that will affect these regrets, such contextual features as discrete features and continuous features. In this paper, we investigate the impact of users' context features and corresponding weights on selecting an optimal price. To apply for multi-dimensional contextual features and learned corresponding weights, two ellipsoid pricing based mechanisms are proposed for homogenous and noisy sensing tasks respectively. Finally, extensive simulations show that our mechanisms outweigh than existing mechanisms.
Multiple Features Extraction from High Resolution Multi-Spectral Satellite Images using Deep Learning Technique

Remote sensing satellite images are a great sources of information for research community. These satellite images helps researchers in many ways by providing various features present on earth surface. Various techniques are used to extract different class of object in the satellite imagery. In this paper, two state-of-art deep learning architectures, i.e., SegNet & Unet have been applied and tested over the DSTL multi-spectral building extraction dataset. The results show that SegNet based model has performed well than the Unet based model. The work can be utilized for urban planning & management.