Miracle-dz/UniFAR-FTUs huggingface dataset:
Maximum energy harvesting in solar photovoltaic system using fuzzy logic technique

ABSTRACT In this paper, fuzzy logic controller (FLC)-based maximum power point tracking (MPPT) for a three-port bidirectional isolated dc–dc converter proposed for power management to photovoltaic-battery sources. This converter has an advantage of using least number of switches which reduces the switching losses. The inductor–capacitor–inductor resonant circuit achieves soft switching. The converter is modelled to instantaneous power management of photovoltaic (PV) panel, battery and load. The proposed MPPT based on FLC is capable of achieving maximum power from PV panel when solar irradiance is available. The charge and discharge controller of the battery works when there is surplus energy and power shortage with respect to the load, respectively. The recital of fuzzy logic with several membership function is analysed to improve the MPPT. Simulation results show that performance of FLC-based MPPT is better than that of conservative perturb and observe (P&O) MPPT.
Grab mobile application: A usability evaluation

This study attempts to evaluate the mobile usability of a Car Grab application. This car Grab application is prominently used in South East Asia to assist commuters seeking for transportation services. The usability evaluation was carried out at Universiti Utara Malysia, UUM, using 20 student participants as users. The results of the evaluation show that the Grab mobile application was usable but with some usability issues observed.
Translation from Event-B into Eiffel

Formal modelling languages play a key role in the development of software: they enable users to specify functional requirements that serve as documentation as well; they enable users to prove the correctness of system properties, especially for critical systems. However, there is still an open question on how to map formal models to a specific programming language. In order to propose a solution, this paper presents a source-to-source mapping between Event-B models, a formal modelling language for reactive systems, and Eiffel programs, an Object Oriented (O-O) programming language. The mapping not only generates an actual Eiffel code of the Event-B model, but also translates model properties as contracts. The contracts follow the Design by Contract principle and are natively supported by the programming language. The mapping is implemented in the freely available Rodin plug-in EB2Eiffel. Thus, users can develop systems (i) starting with the modelling of functional requirements (properties) in Event-B, then (ii) formally proving the correctness of such properties in Rodin and finally (iii) by using EB2Eiffel to translate the model into Eiffel. In Eiffel, users can extend/customise the implementation of the model and formally prove it against the initial model. This paper also presents different Event-B models from the literature to test EB2Eiffel and its limitations. The article is published in the authors’ wording.
Dual Connectivity for LTE-NR Cellular Networks: Challenges and Open Issues

The Dual Connectivity (DC) technology has gained a lot of momentum in the LTE Release 12 as a means to enhance the per-user throughput and provide mobility robustness. Some studies in the literature have discussed the interworking between the LTE and the air interface of the upcoming Fifth Generation (5G) in a DC scenario. That integration may provide some benefits to meet the high throughput, reliability and availability requirements of the 5G networks. This work firstly presents a brief overview of the DC technology considering the integration between 4G e 5G. Then, we highlight some open issues and challenges for future investigation involving Radio Resource Management (RRM) in such a scenario, which includes: (i) user-cell association, (ii) interaction between base stations and (iii) resource allocation. After that, we propose an extension of a utility-based resource allocation algorithm for DC scenarios. Finally, a performance evaluation is conducted in multi-Radio Access Technology (RAT) LTE-NR scenarios considering the bearer split configuration of the DC technology, where the gains provided by the proposed algorithm are demonstrated.
Hybrid-chain: An Innovative and Efficient Mixed Blockchain Architecture

— The blockchain architecture with distributed and decentralized as the main features often face problems such as long transaction confirmation time, low system throughput, and waste of computing resources. These problems are exactly what the traditional centralized architecture can solve. At the same time, in order to improve the distributed consensus efficiency, consensus algorithms such as Proof of stake, delegated proof of stake, proof of space, proof of authority, and PBFT have emerged from Bitcoin's Pow consensus mechanism. These consensus algorithms have their own advantages and Disadvantages. Disadvantages, such as Pos, DPos can improve transaction efficiency, PBFT can improve the fairness of voting, but at the same time POS also bring benefits and voting rights to hold a lot of user tilt issues to a small number of tokens, PBFT brings about a low cost of malicious user attacks and other issues. In order to effectively take advantage of the high security, high reliability of the blockchain, and high efficiency of traditional centralized architectures, and fully utilizing existing consensus mechanisms, this paper presents an innovative hybrid blockchain approach, which mixed the blockchain architecture, Hybrid-chain, consists of the following three type nodes in the architecture: service center nodes responsible for efficient transaction confirmation, dynamic transaction verification nodes participating in transactions, and random verification nodes responsible for transaction fairness verification. On the consensus mechanism, the consensus algorithm selects the corresponding consensus mechanism according to the transaction characteristics. Experiments show that compared with the traditional blockchain architecture, hybrid chain has obvious performance advantages in transaction sending, transaction confirmation and block information storage, which can greatly reduce the transaction sending time by 96%, shorten the confirmation time by 95%, and reduce the number of 79. % Block information storage space, etc.
Dose assessment for space radiation using a proton differential dose spectrum

A Monte Carlo method using a voxel-based human model was used to evaluate radiation doses for astronauts.The model used cubic voxels 4 mm on each side using magnetic resonance imaging.The model was segmented into 23 radiosensitive anatomic regions and compressed using a look-up table.The doses in each organ and tissue due to incident monoenergy protons were calculated using the GEANT4 Monte Carlo code combined with this model.The human dose assessment for space radiation was obtained from the proton differential spectrum and the integral of the differential dose energy spectrum.The results for the skin equivalent dose for the Aug.1972 solar particle event is 9 256 cSv which agrees well with the accepted value of 9 350 cSv in other references.
Toward a Full-Flexible and Fast-Prototyping TOF-PET Block Detector Based on TDC-on-FPGA

Typically, a time-of-flight (TOF) positron emission tomography (PET) block detector is built using application-specific integrated circuits (ASICs), since they integrate a high number of channels at a reasonable power consumption and into a small area. However, ASICs’ flexibility is limited and prototyping times are long because a semiconductor fabrication process is required in every design iteration. Alternatively, fast terminal (FT) silicon photomultipliers (SiPMs) require a simplified analog front-end in order to achieve TOF accuracy. In addition, field-programmable gate arrays (FPGAs) can allocate time-to-digital converters as well as complex digital readout logics. In this paper, we propose building TOF-PET block detectors based on FPGAs, FT-SiPMs, and minimal amount of off-the-shelf components. In this way, TOF-PET accuracy is achieved with a full-flexible and fast prototyping solution. We evaluated the coincidence resolving time, performance degradations due to channel multiplexing, energy resolution, and scintillator pixel encoding performance of SiPM arrays utilizing the proposed approach. Experimental results show minimal timing degradations, when multiplexing FTs. Moreover, simulation results show a low reduction in the singles count rate of multiplexed channels at typical brain-PET radioactive doses.
MODTRAN 5: a reformulated atmospheric band model with auxiliary species and practical multiple scattering options: update

The MODTRAN5 radiation transport (RT) model is a major advancement over earlier versions of the MODTRAN atmospheric transmittance and radiance model. New model features include (1) finer spectral resolution via the Spectrally Enhanced Resolution MODTRAN (SERTRAN) molecular band model, (2) a fully coupled treatment of auxiliary molecular species, and (3) a rapid, high fidelity multiple scattering (MS) option. The finer spectral resolution improves model accuracy especially in the mid- and long-wave infrared atmospheric windows; the auxiliary species option permits the addition of any or all of the suite of HITRAN molecular line species, along with default and user-defined profile specification; and the MS option makes feasible the calculation of Vis-NIR databases that include high-fidelity scattered radiances.
Cognitive Characteristics for Learning C++

This research investigated the cognitive characteristics for learning C++. The two cognitive characteristics investigated in this research were 1) cognitive development, as measured by the Proposition Logic Test (PLT) and 2) cognitive hemispheric style as measured by the Hemispheric Mode Indicator (HMI). Prior research has shown that programming involves logic, a high level of cognitive development and that programmers are left brain thinkers. The findings from this study support prior research for logic and cognitive development characteristics for programming. What was different was cognitive hemispheric style. This study found C++ as being cognitive hemispheric style friendly. Right and whole brain thinkers can learn C++. This contradicts prior research. The only difference between this study and prior research is that C++ uses object-oriented concepts. Prior research used procedural programming languages. Further research with the cognitive characteristics of object-oriented programming is warranted.
Performance Analysis of Feature Selection for Twitter Sentiment Analysis: Classification approach

: The social media platform is important as a means to spread information, opinions or awareness about real events. Twitter is prominent because of the huge amount of messages on all kinds of topics posted every day. Text classification of Twitter is never a trivial task that can be handled by common natural language processing techniques. There is also no agreement as to the definition of what type of operation will be performed in the event identification and classification of Tweets. Existing methods are difficult to reproduce and compare because they are often focused on certain types of events according to certain assumptions. This approach is in case of distinct nature. Previous work neglects impactful words due to the use of Bag-of-Words model. The contribution is for feature selection, Information Gain (IG) with consideration of adjectives as most impactful feature in POS tagger is used. Similarity Model, Relevance, Specificity & Span of relationship computation is also carried out later. SVM classification approach is implemented to construct positive and negative sentiments terms. Further this trained dataset is used for SVM classification of test dataset. Results show promising improvements.
Thermodynamics and criticality of supersymmetric spin chains with long-range interactions

We study the thermodynamics and critical behavior of su(m|n) supersymmetric spin chains of Haldane–Shastry type with a chemical potential term. We obtain a closed-form expression for the partition function and deduce a description of the spectrum in terms of the supersymmetric version of Haldane’s motifs, which we apply to obtain an analytic expression for the free energy per site in the thermodynamic limit. By studying the low-temperature behavior of the free energy, we characterize the critical behavior of the chains with 1⩽m,n⩽2, determining the critical regions and the corresponding central charge. We also show that in the su(2|1), su(1|2) and su(2|2) chains the bosonic or fermionic densities can undergo first-order (discontinuous) phase transitions at T = 0, in contrast with the previously studied su(2) case.
Automatic GPU Offloading Technology for Open IoT Environment

Recently, Internet of Things (IoT) technologies have been progressed. To overcome of the high cost of developing IoT services by vertically integrating devices and services, open IoT enables various IoT services to be developed by integrating horizontally separated devices and services. For open IoT, we have proposed Tacit Computing technology to discover the devices that have data users need on demand and use them dynamically. However, existing Tacit Computing does not consider performance. Therefore, in this paper, we propose an automatic graphics processing unit (GPU) offloading technology as a new elementary technology of Tacit Computing that uses genetic algorithm to extract appropriate offloading areas from parallelizable loop statements automatically. This can improve performance of IoT applications. We evaluate our proposed GPU offloading technology by applying it to five C/C++ applications of image processing, matrix manipulation, and so on to verify its effectiveness and find that it can process them more than ten times as quickly as only using central processing units within 1 h tuning time.
Estimation of length scales from mesoscale networks

This paper reports on the spatial scales of meandering motions using recent data from three mesoscale networks. Although the mesoscale motions extend over a wide range of scales, and time series do not reveal a spectral peak, the examination of the spatial coherence does identify a preferred spatial scale. Several independent methods reveal the same preferred spatial scale for horizontal coherence for a given network. However, the spatial scale differs by a factor of two between the different networks examined here, presumably due to the different topography and surface conditions. The preferred spatial scale increases roughly linearly with range of time scales included in the evaluation. However, the details of this increase do not seem to be predictable, again partly due to site-specific conditions. The preferred spatial scales are of the order of a few kilometres for time scales less than an hour, but may reach tens of kilometres for time scales of several hours. The preferred horizontal length scale determines the area over which the flow features are statistically coherent. Measurements at a single location can be considered as representative for an area comparable to or smaller than the preferred scale.
Generalized Surfaces in the Calculus of Variations

This note deals with an extension of the idea of surface, which is similar to the writer's generalization' of the notion of curve in the Calculus of Variations. The primary object of such generalizations is to ensure, as far as possible, that every problem of the Calculus of Variations has at least one solution. Their need has been apparent ever since the illuminating remark made by Hilbert:2 Eine jede Aufgabe der Variationsrechnung besitzt eine Losung sobald hinsichtlich der Natur der Grenzbedingungen geeignete einschrankende Annahmen erfillt sind, und, nitigenfalls, der Begriff der Losung eine sinngemdsse Erweiterung erfdhrt. In the existence theorems for variational problems concerning an unknown curve, the customary restriction to regular problems can now be removed by using generalized curves, and applications of these notions have lately been made by McShane.3 Problems of minima of variational problems concerning surfaces are essentially far more complicated than those concerning curves. In this note, however, the additional complication appears only in the proof of certain theorems, not in their statement. The definition of generalized surface given here, and the proof of the existence of a (generalized) solution, are framed for a problem of minimum originally stated in a class I of ordinary surfaces
Big Data Analytics and Network Calculus Enabling Intelligent Management of Autonomous Vehicles in a Smart City

Artificial intelligence (AI) and big data analytics enable autonomous vehicles (AVs) to dramatically change future intelligent transportation in smart cities. AVs are envisaged to evolve to a service rather than a product in the future. To provide best user experience of such services, three primary factors, namely, waiting time, travel time, and supply of AV services, are taken into consideration in a multiobjective optimization. Conventional optimization of services relies on traffic flow analysis over a queuing network model. However, due to the mobility of vehicles and the transfer uncertainty of road networks, the queuing network analysis is too complicated and practically intractable. For accuracy and convenient processing, network calculus (NC) is extended to model the queueing problem in this paper. The optimal number of available AVs can be identified by guaranteeing the waiting time of customers. The satisfaction of AV services can be viewed as a supply and demand problem, and optimized by bipartite graph matching. In order to reduce the average travel time, especially for rush hours with heavy traffic, we further propose a new online AVs fleet management scheme with congestion control for smart cities. It is shown that the intelligent management of AV fleet can be efficiently achieved, outperforming the cases of traditional vehicles. NC-assisted AI enables an efficient intelligent transportation paradigm in smart cities, while achieving substantial energy saving.
Co-evolution of morphology and behavior in self-organized robotic swarms

By co-evolving both morphology and controller, the evolutionary process is expected to converge faster than by doing each evolution at separate times, as seen in [2], which came to these conclusions using a single robot in a simulated environment. There is very little work on any kind of coevolution in the field of swarm robotics, as it will be discussed in the Related Work; therefore, by providing exclusive data, this research will help solidify the scarce knowledge in the area or even contest it, as the field is still not well established and the state of the art could be restrained to specific scenarios. For example, no research could be found about the impact of evolving the robot’s body in a swarm, and consequently, specially at the same time as evolving their controllers.
Two Layers Trust-Based Intrusion Prevention System for Wireless Sensor Networks

Security of a wireless sensor network is aimed at ensuring information conﬁdentiality, authentication, integrity, availability and freshness is an important factor considering the criticality of the information being relayed. Hence, the need for an intrusion detection/prevention system. Conventional intrusion avoidance measures, such as encryption and authentication are not sufficient because they become useless in the event of a sensor node being compromised, hence, can only be seen as a first line of defence in the network after which intrusion detection schemes follow. In this paper, two layers trust-based intrusion detection scheme was developed for wireless sensor networks. A trust-based model is presented to detect intrusions to the network. Scenarios were created by using different set of weights. By injecting 2%, 5% and 10% malicious nodes from the 100 nodes considered, the results obtained were carefully observed. For scenario 2 (S2) with 2% and 5% malicious nodes injected, the model achieved the best result in all cases with an average detection accuracy of 97.8% while scenario 3 (S3) with 10% of malicious nodes introduced recorded the best performance with an average accuracy of 96%. Hence, the model will be suitable with combination of weights in S2 with small networks but when the scale of the network increases, the set of weights in S3 are best with the model.
The relationship between Personality Traits, Learning Styles and Academic Performance of E-Learners

The rapid growth of e-learning has greatly influenced the educational system across the globe. Personality traits and learning styles are both likely to play considerable roles in influencing academic achievement of e-learners. Based on this foundation, a study was designed that attempts to establish the missing links between personality traits, learning styles, and academic performance of students enrolled in various e-learning courses. University students (N=144) completed the Big Five Inventory (BFI), Index of Learning Style (ILS) and reported their grade point average (GPA). One of the Big Five traits i.e. extraversion was positively related with all four learning styles whereas neuroticism was negatively related with all four learning styles. It has also been revealed that GPA was positively correlated with three personality traits and was negatively correlated with neuroticism. Similarly GPA was positively correlated with three learning styles. Finally, there were no significant differences in learning styles and personality traits of e-learners in terms of gender. Implications of these results are expected to help academics, managers, and policy makers for implementation of future e-learning strategies in Pakistan.
An adaptive machine learning-based QoE approach in SDN context for video-streaming services

In data service applications over the Internet, user perception and satisfaction can be assessed by quality of experience (QoE) metrics. QoE depends both on the users’ perception and the used service, which together form end-to-end metrics. While network optimization has traditionally focused on optimizing network properties such as QoS, we focus in this work on optimizing end-to-end QoE metrics with the aim to deliver to the client a good QoE that can be monitored in real time. We argue that end-user QoE is a relevant measurement for network operators and service providers. In this paper, we present a machine learning approach combined with adaptive video delivery service in order to provide a better QoE for video streaming services. This solution will be established using SDN architecture. The first part of the paper deals with a brief introduction of SDN networks, QoE requirement, and ML algorithms. Secondly, we expose the rating of the web application that we developed. This will help in conducting a subjective study to collect MOS on real-time as well as objective parameters SSIM, VQM, and PSNR. At the end, we expose our QoE-aware monitoring approach and explain what it is based on.