Miracle-dz/UniFAR-FTUs huggingface dataset:
Utility driven grid scheduling framework

The grid computing paradigm enables access to geographically and administratively distributed networked resources, and delivers functionality of those resources to individual users. Stemming from the core composition and aggregation of individual resources, the grid is primarily characterized by the heterogeneity it offers. Although such heterogeneity is often considered a feature, it also presents an obstacle in terms of application execution patterns and expectations (in terms of job runtime, resource utilization, and/or user Quality of Service (QoS)). Typical users have little or no knowledge about the concrete requirements their application imposes on such resources and thus have to stumble through a sea of options and uncertainties when submitting a job, leading to inefficient use of available resources. In order to alleviate the user from having to understand existing dependencies and make low-level decisions, a grid metascheduling framework has been devised that enables automated application- and user-oriented job metascheduling. In order to enable application-oriented metascheduling, a set of core grid services, Application Information Services (AIS), were designed and developed to provide application metaschedulers with relevant information regarding each application's execution requirements and preferences. With such information, a metascheduler is capable of automatically realizing more job-to-resource mappings. In order to enable user-oriented metascheduling, a novel mode of user-scheduler interaction has been devised that builds on top of AIS. The model is realized in terms of two-way communication between a user and the scheduler enabling strict focus on an individual user and their current job. Overall, this dissertation makes contributions regarding efficiency of use and ease of access for grid resources. Results of grid job metaschedulers implementing the devised framework are shown as capable of consuming application-specific data in a manner that leverages existing heterogeneity and, in turn, automatically deliver effective application-to-resource mappings. Results achieved are two-fold: (1) behavior of application jobs across grid resources has been significantly improved in terms of job execution control, capable of increasing resource utilization and achieving significant runtime reduction (up to 50%), and (2) each job submission is being tailored specifically to an individual user and their respective job, delivering significantly higher QoS to the user. Keywords: grid, application-oriented job scheduling, Application Information Services, job execution space, user-scheduler interaction
Routing in Flying Ad Hoc Networks: Survey, Constraints, and Future Challenge Perspectives

Owing to the explosive expansion of wireless communication and networking technologies, cost-effective unmanned aerial vehicles (UAVs) have recently emerged and soon they will occupy the major part of our sky. UAVs can be exploited to efficiently accomplish complex missions when cooperatively organized as an ad hoc network, thus creating the well-known flying ad hoc networks (FANETs). The establishment of such networks is not feasible without deploying an efficient networking model allowing a reliable exchange of information between UAVs. FANET inherits common features and characteristics from mobile ad hoc networks (MANETs) and their sub-classes, such as vehicular ad hoc networks (VANETs) and wireless sensor networks (WSNs). Unfortunately, UAVs are often deployed in the sky adopting a mobility model dictated by the nature of missions that they are expected to handle, and therefore, differentiate themselves from any traditional networks. Moreover, several flying constraints and the highly dynamic topology of FANETs make the design of routing protocols a complicated task. In this paper, a comprehensive survey is presented covering the architecture, the constraints, the mobility models, the routing techniques, and the simulation tools dedicated to FANETs. A classification, descriptions, and comparative studies of an important number of existing routing protocols dedicated to FANETs are detailed. Furthermore, the paper depicts future challenge perspectives, helping scientific researchers to discover some themes that have been addressed only ostensibly in the literature and need more investigation. The novelty of this survey is its uniqueness to provide a complete analysis of the major FANET routing protocols and to critically compare them according to different constraints based on crucial parameters, thus better presenting the state of the art of this specific area of research.
Securing Virtual Infrstructure in Cloud Computing using Big Data Analytics

With the popularity of cloud, attacks on cloud are also increasing. For advanced attacks on cloud, attackers choose Virtual Infrastructures to attack as they provide the platform to develop and deploy the applications through Virtual Machines (VMs). Attacks on virtualized infrastructure are increasing day by day. Attackers try to attack on a VM which sometimes leads them to take the unauthorized access of the cloud. There are different techniques to detect and prevent these attacks. Big Data Analytics field is getting better to perform the analysis of attacks efficiently. This paper focuses on Big Data Analytics approaches to detect the advanced Malware attacks in virtualized infrastructure. In this paper we use Big Data Analytics techniques to analyze log files and network traffic to identify anomalies and suspicious activities in virtualized infrastructure in cloud computing. Network logs and application logs are collected periodically from virtual machines (VMs) and stored in the HadoopDistributed File System. We then use Map Reduce Parser and Graph event based correlation to extract the attack features and with the help of logistic regression and belief propagation we will calculate the attacks conditional probabilities and belief in existence of an attack.
Implementation Approach for IDS based on Risk Assessment and Attack Pattern in Cloud Computing

Cloud computing is an evolution in the computing area that takes advantage of the falling cost of CPU's and storage, combined with the ubiquity of broadband Internet access and dramatic increase of Internet connected devices. The impact of Cloud Computing technologies adoption include new standards, architecture paradigms, security challenges and disruptive business models. Security is a top concern in choosing the right cloud service provider. Analyzing intrusion based on attack patterns and risk assessment in cloud computing has demonstrated its efficiency in reducing the number of false alarms and optimizing the IDS performances. This paper intended to present the implementation of a new architecture to quantify attacks in cloud environment based on risk assessment. With the main objective to increase the efficiency of IDS and decrease the number of alarms. Experimental results show that our approach is better to the state-of-the-art approach for intrusion detection in cloud.
Direct Power Control for Three-Phase Active Power Filter Using Backstepping Technique

In this work we are considering the problem of compensation of the reactive power absorbed by a linear load from a three-phase network. For this, we proposed a three-phase active power filter based on three-phase voltage inverter to compensate the Reactive power absorbed or injected by the load, we have based on the non-linear model of the system and we developed a nonlinear control laws by the technique of Backstepping. The objective is twofold in this paper the regulation of the active power exchange between the filter and the network to track its reference value and the reactive power to compensate for that, which is absconded by the load. The theoretical results evaluated by simulation.
Low-Crosstalk Multistage Silicon Wavelength Multi/Demultiplexer

Low-crosstalk multistage silicon wavelength multi/demultiplexer –consist of an arrayed waveguide grating and Bragg grating filters is proposed. The crosstalk of less than -40 dB can be achieved even if a waveguide has phase error. The tolerances for the fabrication error and temperature shift are also studied.
Proof of Delivery in a Trustless Network

We propose a novel proof of delivery (PoD) mechanism to incentivize peers to participate in peer-to-peer content delivery services in a secure and effective fashion. By analyzing transaction records on a blockchain, the PoD can detect a fraudulent peer which does not pay for its content retrieval. Moreover, the PoD can enhance the delivery performance of a trustworthy peer by substantiating the notion of a trust window, whose size is proportional to the trustworthiness of a peer. That is, a trustworthy peer will experience enhanced delivery performance while a fraudulent one will be isolated from the network, which is checked by simulation results.
Adaptive Traffic Lights through Traffic Density Calculation on Road Pattern

Traffic jam is a problem that is often faced by people especially those living in crowded cities, which is wasting time and money. Mainly, traffic light has been the common cause for traffic jam. Almost all traffic lights that caused traffic jam, work conventionally with timer. This ineffective method sometime needs Police’s intervention to reduce traffic jam. Basically, Police rules traffic based on traffic density. Indeed, this paper proposes adaptive traffic lights to control its timing through traffic density calculation on road pattern. The traffic density is obtained by using image processing on several road pattern. Later, the traffic density is utilized to calculate timing of the traffic light. There is a server that collects the data and controls the traffic light operations in a crossroad. The entire algorithms, including the calculation of traffic density and timing of traffic light are validated through real road condition. The results show that traffic density detection system has good accuracy and timing of traffic light is successfully calculated.
Design, Fabrication and Control of Four Degrees of Freedom Serial Manipulator

In this paper, a 4-DOF serial manipulator is designed, simulated, analyzed, fabricated and controlled. The manipulator is designed for a pick and place task. The DH parameters of the robotic arm are found and using Peter Corke’s Robotics Toolbox for MATLAB, direct kinematic model of the manipulator is realized. The implementation of Robotics Toolbox is not so common to find in the literature. Each joint’s position, speed and acceleration are plotted with respect to time. During fabrication and assembly, 4 servo motors are used. Arduino UNO board has been used for controlling the servo motors and potentiometer. It is extremely beneficial to have all the steps in a single article to realize a robot.
Big Data and Internet of Things: A Survey

In this digital era, here the sum of data is generate with store has prolonged inside a less period of time. The data in this era generated high speed leads to many challenges. The size is primarily and periodically, just the measurement that bounces in the big data position. In this survey, we have attempt to give a broad explanation of big data that captures its other unique and important features. We have discussed 4V’s model. Also, the latest technologies uses in big data, like Hadoop, HDFS, MapReduce and different type of methods used by big data. Finally various benefits of using big data analysis and include some feature of cloud computing.
An Adaptive Block Backstepping Controller For A Quadrotor

An adaptive block backstepping controller is proposed to control the position of a quadrotor with uncertain model parameters. The controller design procedure is provided considering the stability of six blocks involving the states of the model. In each step the stability of the concerned block is shown by means of Lyapunov stability theorem. The viability and the success of the proposed controller are tested through numerical simulations.
Context-Aware Co-attention Neural Network for Service Recommendations

Context-aware recommender systems are able to produce more accurate recommendations by harnessing contextual information, such as consuming time and location. Further, user reviews as an important information resource, providing valuable information about users' preferences, items' aspects, and implicit contextual features, could be used to enhance the embeddings of users, items, and contexts. However, few works attempt to incorporate these two types of information, i.e., contexts and reviews, into their models. Recent state-of-the-art context-aware methods only characterize relations between two types of entities among users, items and contexts, which may be insufficient, as the final prediction is closely related to all the three types of entities. In this paper, we propose a novel model, named Context-aware Co-Attention Neural Network (CCANN), to dynamically infer relations between contexts and users/items, and subsequently to model the degree of matching between users' contextual preferences and items' context-aware aspects via co-attention mechanism. To better leverage the information from reviews, we propose an embedding method, named Entity2Vec, to jointly learn embeddings of different entities (users, items and contexts) with words in a textual review. Experimental results, on three datasets composed of millions of review records crawled from TripAdvisor, demonstrate that our CCANN significantly outperforms state-of-the-art recommendation methods, and Entity2Vec can further boost the model's performance.
Color-image coding by an advanced vector-quantizer

An advanced vector quantizer for coding true-color images is presented. The starting point is the classical algorithm proposed by Y. Linde, A. Buzo, and A.M. Gray (LBG, 1980). On the basis of the LBG algorithm, a neural net able to furnish a starting codebook quite close to the optimal one is developed. As several vectors are infrequently used, a merge-and-split process has been performed to merge such vectors into adjacent ones, thus allowing free space to be reutilized to redistribute the most numerous partitions. Another aspect to be exploited is the existence of statistical redundancies between neighboring blocks. To sharply reduce the bit rate, an RGB predictor based on some transition matrices is developed. The basic idea of the development of an RGB predictor is provided by the address vector quantization (AVQ) technique. Results obtained by processing standard images are discussed. <<ETX>>
Decision Support System for Choosing Daycare in Surabaya City Using Analytical Hierarchy Process (AHP)

Economic problems led to changes in women's roles where women are choosing to work, so they cannot always be with the children and tend to choose child care in daycare when they are working as a solution. The routines make them lack any information about daycares especially profiles and locations. So parents need an information system that can provide information about daycare and advise the decision. The purpose of this research is to provide a system that gives information of each daycare and daycare recommendations, so that it can be used by parents. In this research, decision-making system was conducted using Analytical Hierarchy Process (AHP), which is a multi-criteria decision-making method. A recommendation is made by user’s rank. The conclusion of this research is that the system is able to provide information and recommendations about daycare and able to meet the needs of parents.
Personalized Scientific Paper Recommendation Based on Heterogeneous Graph Representation

The accelerating rate of scientific publications makes it extremely difficult for researchers to find out the relevant papers and related works. Recommender systems that aim at solving the information overload problem have attracted lots of attention. However, existing paper recommendation works generally rely on the simple citation-ships between papers, which ignore the heterogeneity of the academic graphs. In this paper, we solve the personalized paper recommendation problem in the setting of heterogeneous information networks. A heterogeneous graph representation based recommendation method named HGRec is proposed. First, the author and paper profiles are constructed based on the extracted contents information. Second, we initialize the node vectors by employing the word-embedding technique. Third, we jointly update the node embeddings in the heterogeneous graph by proposing two meta-path based proximity measures. Finally, the paper recommendation is completed by calculating the similarity of the generated author and paper feature vectors. We present experiments on a real academic network, the DBLP network. The comparative results demonstrate the effectiveness of the proposed personalized recommendation approach compared to state-of-the-art methods.
Discrete Hand Motion Intention Decoding Based on Transient Myoelectric Signals

In the application of human–robot interaction (HRI) rehabilitation exercise controlled by Electromyogram (EMG), if the discrete motion intention decoded by EMG signals is within the range of electromechanical delay (EMD), through the rehabilitation training, it will largely enhance the user’s feeling feedback and fully activate the brain plasticity. So the decoding of hand movement intention by transient EMG is investigated to reach ideal characteristics needed in the HRI. The high-density EMG signal (HDEMG) database CapgMyo was used to decode the motion intention based on the transient EMG signals within the EMD rather than the whole recorded signals. We investigate the impact of the different decoding window lengths (WL) and training sets constructing methods when using several machine learning algorithms. In addition, the transient EMG signals decoding performance of the sparse multi-electrode EMG signal database NinaPro performing the same hand movement was compared. The visual inspection of the EMG map was used to determine the onset of HDEMG. The proposed approach was tested on EMG decoding window length of 150 ms, demonstrating a mean±SD testing performance of 94.21%±4.84% after voting. However, it is worth noting that sparse EMG signal did not achieve the desired decoding accuracy. The result showed that the high-density EMG signal could be used to decode the motion intention within EMD by simple machine learning algorithms, and extending the window length of training set could improve the decoding accuracy.
Artificial Intelligence Based Energy Efficient Grid PEGASIS Routing Protocol in WSN

In Wireless Sensor Networks routing data and information from one node to another and to base station is a major challenges. It shows the commutation between efficiency and responsiveness. Fuzzy logic is one such protocol existing in this category which is used to reduce the overburden cluster head (CH) in Wireless sensor network (WSN), it is a rule based architecture system and with each variation in the network, rules are added in the network. For a network like WSN or MANET, the fuzzy logic may contain hundreds of rules, which makes the architecture more complicated. In this work, a hybrid Power Efficient Gathering in Sensor Information System (PEGASIS) hierarchical protocol is proposed which uses Firefly optimization technique and artificial neural network for making the lifespan of the Wireless Sensor Network (WSN) enhanced. The Firefly optimization algorithm will work on the grid deployment model. PEGASIS is best used for routing with other protocols based on sensors lifetime, total energy consumed and shows comparative results with other mainline protocols. In this work the technique used for optimization is Firefly algorithm in firefly algorithm, attractiveness and light intensity are the significant variables used to attract other firefly with more light intensity than them-self. The light intensity is directly dependent on attractiveness of the firefly. PEGASS is considered as one of the best redirecting method which allows better routing technique. In PEGASIS the sensor node location is random and every node has the capability to detect data, blend data, and equally sends the load among the nodes. Chain of nodes is made according to the positioning of the node and the nodes are plotted by using greedy algorithm. Artificial neural network (ANN) is used as a classifier to remove distortion from the network or to overcome the battery discharge problem. The accomplishment of the proposed work can be assessed by measuring the performance parameters named as Total number of packet transmitted, Energy dissipation, Throughput, Network lifetime by using MATLAB simulator.
Developement of Prediction in Clients’ Consent to a Bank Term Deposit Using Feature Selection

This paper presents the effect of Feature Selection on the accuracy of predictıve results. It shows that using feature selection algorithms help to get almost the same accurate results comparing to when feature selection is not used. However, the results show that implementing feature selection algorithms reduces data processing time and gives more flexibility in the software package selection since smaller amount of data is resulted.
Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques

Heart disease is one of the most significant causes of mortality in the world today. Prediction of cardiovascular disease is a critical challenge in the area of clinical data analysis. Machine learning (ML) has been shown to be effective in assisting in making decisions and predictions from the large quantity of data produced by the healthcare industry. We have also seen ML techniques being used in recent developments in different areas of the Internet of Things (IoT). Various studies give only a glimpse into predicting heart disease with ML techniques. In this paper, we propose a novel method that aims at finding significant features by applying machine learning techniques resulting in improving the accuracy in the prediction of cardiovascular disease. The prediction model is introduced with different combinations of features and several known classification techniques. We produce an enhanced performance level with an accuracy level of 88.7% through the prediction model for heart disease with the hybrid random forest with a linear model (HRFLM).
Characterizing Nanoparticle Swarms With Tuneable Concentrations for Enhanced Imaging Contrast

Microrobots capable of performing targeted delivery are promising for biomedical applications. Due to the restriction of their small size and volume, however, the real-time in vivo imaging strategy for microrobots meets limitations at the current stage. Performing swarm actuation and control may be a promising candidate to increase the contrast of the imaging feedback signal. Herein, we use paramagnetic nanoparticles as tiny agents to systematically investigate the relationship between the imaging intensity and the areal concentration of the nanoparticles using three different bio-imaging approaches. Magnetic actuation strategies are applied to effectively tune the nanoparticle concentration via spread and gathering processes. Three imaging modalities are applied, including fluorescent imaging (FI), ultrasound imaging (UI), and photoacoustic imaging (PAI) for tracking the nanoparticles. The results of spreading and vortex-like swarm generation (gathering) are first validated using optical microscope, and then the processes are performed under the monitoring of FI, UI, and PAI, respectively. The change of the imaging intensity with the particle areal concentration is analyzed. This work supports that forming a high-concentrated swarm enhances the intensity of feedback signals with multiple imaging methods.