Miracle-dz/UniFAR-FTUs huggingface dataset:
An efficient and secure data storage in cloud computing using modified RSA public key cryptosystem

Cloud Computing is the ability to improve the utility or train new human resources without investing in new infrastructure, or add capabilities to existence without the latest software licensing. It expanded the capabilities of Information Technology (IT). From the past few years, cloud computing has developed from a good business concept in the best rising sectors of the IT industry. But more information on individuals and companies was put in the cloud, and concerns began to think about how secure the cloud environment was. Despite cloud surrounding structures, enterprise users still do not want to expand their business in the cloud. Security reduces the growth of cloud computing and continues to spread the market with complexity with data privacy and data protection. The security of cloud computing has constantly been an significant aspect of improved quality of service from cloud service providers. Data storage in the cloud has a problem related to data security. However, cloud computing construct many new security challenges which have not been well examine. In order to ensure that the user's data in the cloud is secure, we have proposed an effective mechanism with a distinctive feature of data integrity and privacy. This paper focusing on problems relating to the cloud data storage techniques and security in virtual environment. We recommend a method for providing data storage and security in cloud using public key Cryptosystem, which uses the concept of the modified RSA algorithm to provide better security for the data stored in the cloud.
Estudo da integração de sinais na avaliação da severidade de flebite associada à cateterização venosa periférica

Clinical evaluation of peripheral catheter related phlebitis (vein inflammation) severity may be considered the integrated resultant of the information conveyed by several signs and symptoms. This research is an experimental approach to the way the signs of Pain, Tumour and Erythema are cognitively integrated into a global phlebitis severity judgement by use of the Information Integration Theory (IIT). The results reveal an additive type integration of the studied signs. This algebraic cognitive model is at odds with the assumptions made by several normative phlebitis severity scales. Additionally, the application of the Functional Measurement Theory associated with IIT reveal a nursing student’s discriminatory deficit of the lower intensity levels of Erythema. Implications to nursing practice are extracted. Some limitations of this study are noted, and some directions the research may take in the future are pointed.
Uma Ferramenta para Identificar Desvios de Linguagem na Língua Portuguesa (A tool to identify the linguistic deviations in the Portuguese Language)[In Portuguese]

The revision of formal texts is a complex task and occurs in several areas. The objective of this work is to create a tool to support the revision of texts and promote studies in automatic correction of descriptive texts. We propose a reviewer for automatic identification of language deviations in formal descriptive texts using natural language processing techniques. A case study was carried out to evaluate the proposed approach in a public set of essays. The tool identified 3,255 deviations in a universe of 762 essays.
Software Defined Network’s Garbage Collection With Clean-Up Packets

Rule updates, such as policy or routing changes, occur frequently and instantly in software-defined networks managed by the controller. In particular, the controller software can modify the network routes by introducing new forwarding rules and deleting old ones in a distributed set of switches, a challenge that has received lots of attention in the last few years. In this paper, we present a problem that consists of determining the appropriate point in the rule update where it is safe to garbage collect old rules. To illustrate the difficulty of the problem, we list the previously proposed assumptions, like the upper-bound on the transmission delay of every packet through the network, and we offer a solution that alleviates these assumptions and significantly reduces the rule update time with a guarantee that no data packet is lost due to the rule alteration through the use of dedicated clean-up packets that detect the absence of in-flight packets. We then prove that the proposed technique guarantees per-packet consistency, blackhole-freedom, and loop-freedom. Our evaluations, via network emulations and real deployment in an SDN testbed, demonstrate that by using the proposed garbage collection solution the rule update times of the two-phase rule update can be reduced by up to 99%.
Measuring and minimizing cybersickness in virtual reality

Virtual Reality (VR) technology immerses users in a three-dimensional virtual environment. The use of VR technology, through Head Mounted Displays (HMDs), is predicted to increase exponentially in the near future. However, there are usability issues with VR that may inhibit this. One issue is the negative, unwanted symptoms that some user experience when in VR. These symptoms include nausea, dizziness, disorientation and headaches and are collectively called Cybersickness. Cybersickness is related to classical motion sickness and simulator sickness. It is a poly-symptomatic, multiphasic ailment that affects up to 80 percent of first-time VR HMD users. There has been modest success in the development of techniques to minimize cybersickness. However, effective strategies of completely preventing cybersickness remain unclear. A significant problem is the lack of standardized methods for capturing accurate measurements of cybersickness. We conducted a thorough investigation into how to measure and minimize cybersickness. We focused on analyzing relevant factors and conducting a cognitive engineering analysis to inform the development of a potential solution. We then developed a physical dial interface to accurately capture momentary user cybersickness and feed this information back to the user. We added an additional layer to the system, consisting of sensory warnings to encourage the user to take well-timed breaks and habituate to VR. We tested the system with 36 participants in a seated roller-coaster VR environment. Our main findings from the experiment were firstly, the physical dial measurement of cybersickness significantly positively correlates to post-exposure questionnaire scores. Secondly, the physical dial had more significant correlations with post-exposure questionnaires than a Verbal measuring technique, the Fast Motion sickness Scale (FMS). Lastly, we found that a visual warning is reacted to more quickly than an auditory warning. The key contribution of this study is the evidence of the physical dial being an appropriate reporting and measuring tool for cybersickness during VR experiences.
Performance of adaptive mobile terminal selection schemes for collaborative MMSE linear MIMO detection

: In collaborative multi-input multi-output detection, a virtual terminal consisting of neighboring mobile stations (MSs) is formed. This virtual terminal acts as a single MS with a large number of antennas. In this system, received signals of MSs in collaboration with each other are shared through short-range wireless communications. In this paper, in order to reduce the amount of collaboration tra ﬃ c, simple adaptive MS selection schemes are studied by an urban measurement campaign using a software-de ﬁ ned testbed operating at 5.1 GHz. The MS selection schemes perform close to the full collaboration scheme, and outperform the round-robin scheme.
Transfer learning on GPR data for IED detection in various environments

Learning an object category from only a few samples is generally not adequate for correct classification. One needs many training samples to obtain a classifier that generalizes well and that has sufficient success rates. However, in several applications, including target detection from Ground Penetrated Radar (GPR) data, collecting many annotated data is not always possible. In a GPR data collection, the images formed show a nonlinear dependence on the soil properties such as the permeability and permittivity. Therefore, even if enough training data were available to train a good classifier for one soil type (such as dry sand); the success of this classifier does not translate well if the soil type is changed (say, to wet sand). In this work, we propose a multi-model knowledge transfer (KT) framework to detect a picnic tube buried in different media using GPR. In the proposed method, scale invariant feature transform (SIFT) features are extracted from GPR data. Then, least-square support vector machine (LS-SVM) classifiers are trained for three soil types (i.e. dry sand, dry sandstone, and wet sandstone) where there is ample data available. Then, adaptive LS-SVM is used to train a classifier that detects the target picnic tube in wet sand from where there is only scarcely available training data. We show that (i) knowledge transfer from multiple sources (i.e. multiple types of sand) generates better results than single source transfer; and (ii) as little as 3 training data from the unknown source increases the detection rates by 10% for single KT, and 4% for multiple KT.
Image-Based Copy-Paste Tamper Detection Technology Based on Improved Surf

Aiming at the problem that the existing copy-paste tamper detection algorithm has poor detection effect on the image area, and the speed is slow, an image region copy-paste tampering detection algorithm based on improved SURF is proposed. The detected image extracts feature points by improving the fast Hessian matrix, and uses k-g2NN method to match the feature points, then estimates the affine transformation parameters between the matching points, and combines the R-RANSAC and SPRT to eliminate the mismatch and finally obtain the copy-paste area. The experimental results show that the proposed algorithm can accurately locate the image copy-paste tampering region, and has strong robustness to Gaussian blur, rotation and scaling operations, and the detection speed is greatly improved.
Overview on NLP Techniques for Content-based Recommender Systems for Books

Recommender systems are an essential part of today’s largest websites. Without them, it would be hard for users to find the right products and content. One of the most popular methods for recommendations is content-based filtering. It relies on analysing product metadata, a great part of which is textual data. Despite their frequent use, there is still no standard procedure for developing and evaluating content-based recommenders. In this paper, we will first examine current approaches for designing, training and evaluating recommender systems based on textual data for books recommendations for GoodReads’ website. We will give critiques on existing methods and suggest how natural language techniques can be employed for the improvement of content-based recommenders.
Robustness of Ontario power network under systemic risks

ABSTRACT A failure of one or even a few components would have a limited impact on the network-level performance if the negative consequences are compensated for by neighboring components. However, under certain conditions, this component-level failure may not remain localized, but may rather propagate along other components, thus inducing an entire network/system-level cascade (i.e., systemic) risks. In this respect, the current study develops a simplified model of the Ontario Power Network (OPN) to simulate its topology and load demands. The study then evaluates the different OPN characteristics and develops robustness bands for the OPN under both random failures and targeted threats. Finally, the study presents two dynamic vulnerability indices to facilitate detecting the most critical components within power networks. This study is expected to not only expand the Canadian and the international power network topological analysis database, but also to provide the foundation for innovative network-level systemic robustness enhancement solutions.
A General Impedance-Emulating Digital Control of Single-Phase Rectifier for High Power Factor

This article presents a general impedance-emulating control method for single-phase rectifiers. To repress the harmonic currents due to the distorted input supplies, a general harmonic impedance based on the concept of repetitive control is emulated. To realize unity power factor, a negative inductor-emulating technique based on the extremum-seeking scheme is applied. In this method, the input voltage sensor is not needed, so the cost is reduced and the reliability is improved. The experiments are conducted to verify the effectiveness of the proposed method.
Elements of Yogic Practice: Perceptions of Health Professions Students

 Modern Western yoga is increasingly focused on physical health and fitness (White, 2011).  Yoga is becoming disconnected from ancient roots in Eastern philosophy and Western insights about the mind, body, and spirit connection (de Michelis, 2004; Feuerstein, 2014; Singleton, 2010).  Previous studies have demonstrated yoga’s utility as an integrative healthcare strategy, but have mostly focused on physical practices such as asana and, to a lesser degree, pranayama (Jeter, Slutsky, Singh, & Khalsa, 2015).  The extensive literature on meditation and mindfulness does not openly acknowledge whether the practices are within the yoga traditions.  Boggs (2014) issued a call for yoga researchers to broaden scientific horizons beyond the physical practices to endorse yoga as a healing strategy, not simply physical exercise.  The current study explores students’ perceptions of the essential practices of yoga (related to the eight limbs of yoga based on Patanjali’s Yoga Sutras) to investigate which elements of yoga are endorsed by actual and contemplating practitioners.
Performance Analysis of MANET Routing Protocols using different Mobility Models and Varying Speed

: A mobile ad-hoc network is a continuously self-configuring, infrastructure less, network of mobile devices. It is the network of mobile nodes without any fixed topology. In this network, each node acts as both hosts and router simultaneously. Each device in a MANET is free to move independently in any direction, and will therefore change its link to other devices frequently. In such a network there are lots of possibilities of mobility of nodes. Therefore mobility of nodes is challenging issue for designer. We have surveyed comparative performance analysis of different routing protocols. Under the different mobility models in term of Packet Delivery Ratio, Average End to End Delay. We have carefully studied the behavior of these routing protocols and compared their performance in different scenarios based. This paper defines which protocol to use in different cases such as if time is important or if data is important in various mobility modes such as all node mobility, destination node mobility and source node mobility and proposed the new algorithm on the basis of protocols evaluation in different situation.
Exposure Invariance in Spectral Reconstruction from RGB Images

In the spectral reconstruction (SR) problem, reflectance and/or radiance spectra are recovered from RGB images. Most of the prior art only attempts to solve this problem for fixed exposure conditions, and this limits the usefulness of these approaches (they can work inside the lab but not in the real world). In this paper, we seek methods that work well even when exposure is unknown or varies across an image, namely 'exposure invariance'. We begin by re-examining three main approaches - regression, sparse coding and Deep Neural Networks (DNN) - from a varying exposure viewpoint. All three of these approaches are predominantly implemented assuming a fixed capturing condition. However, the leading sparse coding approach (which is almost the best approach overall) is shown to be exposure-invariant, and this teaches that exposure invariance need not come at the cost of poorer overall performance. This result in turn encouraged us to revisit the regression approach. Remarkably, we show that a very simple root-polynomial regression model - which by construction is exposure-invariant - provides competitive performance without any of the complexity inherent in sparse coding or DNNs.
Doppler Shift Characterization of Wideband Mobile Radio Channels

The prevailing approach for characterizing the Doppler shift (DS) of mobile radio channels assumes the transmission of an unmodulated carrier. This consideration is valid for the analysis of narrowband channels, but its pertinence is questionable in regards to the modeling of wideband channels. In this correspondence, we redefine the DS from a time-frequency analysis perspective that does not depend on the aforementioned assumption. We systematically demonstrate that the DS can be characterized by the instantaneous frequency of the channel transfer function. This generic definition makes evident a fundamental aspect of the DS that is seldom acknowledged, namely, the DS is a frequency-varying quantity. We show that the second-order statistics of wideband mobile radio channels are non-stationary due to the DS's frequency variations. In addition, we present numerical results of a case study showing that such non-stationarities can cause significant system performance degradations.
What is behind the calcium? The relationship between calcium and necrotic core on virtual histology analyses: reply

In our recent study, the ratio of necrotic core to calcification (NC/DC ratio) detected by virtual histology intravascular ultrasound (VH-IVUS) in diseased coronary segments was related to known risk factors for sudden coronary death (SCD) in men, in line with previous observations that vulnerable plaques have more NC and less DC than stable plaques.1 We also demonstrated in a previous publication, also from the VH-IVUS registry, that the NC/DC ratio separated high-risk non-ST elevation ACS patients from low-risk patients.2 This new IVUS-derived imaging method allows spectral analysis of backscattered radiofrequency (RF) ultrasound signal for the characterization of four basic plaque components: fibrous, …
Unleashing the Power of Participatory IoT with Blockchains for Increased Safety and Situation Awareness of Smart Cities

IoT has emerged as an unprecedented paradigm with great potential for changing how people interact, think and live. It is making existing Internet services feasible in ways that were previously impossible, as well as paving the way for new situation- awareness applications suitable for smart cities, such as realtime video surveillance, traffic control, and emergency management. These applications will typically rely on large numbers of IoT devices to collect and collaboratively process streamed data to enable real-time decision making. In this article, we introduce the concept of Semantic Virtual Space (SVS), an abstraction for virtualized cloud-enabled IoT infrastructure that is commensurate with the goals and needs of these emerging smart city applications, and propose and discuss scalable architectures and mechanisms that enable and automate the deployment and management of multiple SVS instances on top of the cloud-enabled IoT infrastructure.
Autonomic Intrusion Detection and Response Using Big Data

We present a method for autonomic intrusion detection and response to optimize processes of cybersecurity in large distributed systems. These environments are characterized by technology fragmentation and complex operations making them highly susceptible to attacks like hijacking, man-in-the-middle, denial-of-service, phishing, and others. The autonomic intrusion response system introduces models of operational analysis and reaction based on the combination of autonomic computing and big data. We implemented a proof-of-concept and executed experiments that demonstrate significant improvement in effectiveness and scalability of the method in complex environments.
Mapping the ocean floor in extreme resolution using interferometric synthetic aperture sonar

The synthetic aperture (SA) principle is based on coherent combination of multiple pulses in order to improve angular resolution. The SA principle is used in radar (SAR), seismic imaging, ultrasound (SAFT), and sonar (SAS). SAS has matured substantially the last two decades. Today, SAS represents the state-of-the-art in sonar imaging, being able to produce images and maps of the seabed in extreme resolution. The Norwegian Defence Research Establishment (FFI) collaborates with Kongsberg Maritime to develop the HISAS family of interferometric SAS systems for the HUGIN autonomous underwater vehicle (AUV) and other underwater platforms. In this work, we describe the SA principle, the link between the different SA imaging domains, and the particular limitations in SAS. We show example images from the HISAS system illustrating its capabilities and performance limitations.The synthetic aperture (SA) principle is based on coherent combination of multiple pulses in order to improve angular resolution. The SA principle is used in radar (SAR), seismic imaging, ultrasound (SAFT), and sonar (SAS). SAS has matured substantially the last two decades. Today, SAS represents the state-of-the-art in sonar imaging, being able to produce images and maps of the seabed in extreme resolution. The Norwegian Defence Research Establishment (FFI) collaborates with Kongsberg Maritime to develop the HISAS family of interferometric SAS systems for the HUGIN autonomous underwater vehicle (AUV) and other underwater platforms. In this work, we describe the SA principle, the link between the different SA imaging domains, and the particular limitations in SAS. We show example images from the HISAS system illustrating its capabilities and performance limitations.
An Extended Attention-based LSTM with Knowledge Embedding for Aspect-level Sentiment Analysis

Aspect-level sentiment analysis is a highly concerned content of sentiment analysis in recent years. For sentiment analysis, however, some of the necessary external knowledge such as commonsense is very important, and these models can’t acquire this knowledge through training data. To this end, in this paper we propose an extension of the LSTM based on attention mechanism to combine explicit knowledge with tacit knowledge to improve the accuracy of the aspect-level sentiment analysis model.
System architecture to select the charging station by optimizing the travel time considering the destination of electric vehicle drivers in smart cities

The main limitations of electric vehicles are the limited scope of the battery and their relatively long charging times. This may cause discomfort to drivers of electric vehicles due to a long waiting period at the service of the charging station, during their trips. In this paper, we suggest a model system based on argorithms, allowing the management of charging plans of electric vehicles to travel on the road to their destination in order to minimize the duration of the drivers' journey. The proposed system decision to select the charging station, during advance reservation of electric vehicles, take into account the time of arrival of electric vehicles at charging stations, the expected charging time at charging stations, the local status of the charging stations in real time, and the amount of energy sufficient for the electric vehicle to arrive at the selected charging station. Furthermore, the system periodically updates the electric vehicule reservations to adjust their recharge plans, when they reach their selected earlier station compared to other vehicules requesting new reservations, or they may not arrive as they were forecast, due to traffic jams on the road or certain reluctance on the part of the driver.