Miracle-dz/UniFAR-FTUs huggingface dataset:
AMBIENT-ORIENTED MODELING OF INTELLIGENT CONTEXT-AWARE SYSTEMS

The paper presents an approach for modeling intelligent IoT context-sensitive systems. Ambient-oriented modeling allows for the tracking of processes in the area under consideration, taking into account dynamic environmental changes. The main concepts and characteristics of the approach are considered. Described is the formal System of Context-aware Ambients (CCA) supporting the approach. The application of CCA modeling in a Virtual Physical Space (ViPS) is demonstrated.
Evaluation and comparison of multiangle implementation of the atmospheric correction algorithm, Dark Target, and Deep Blue aerosol products over China

Abstract. A new multiangle implementation of the atmospheric correction (MAIAC) algorithm has been applied in the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and has recently provided globally high-spatial-resolution aerosol optical depth (AOD) products at 1 km. Moreover, several improvements have been modified in the classical Dark Target (DT) and Deep Blue (DB) aerosol retrieval algorithms in MODIS Collection 6.1 products. Thus, validation and comparison of the MAIAC, DT, and DB algorithms are urgent in China. In this paper, we present a comprehensive assessment and comparison of AOD products at a 550 nm wavelength based on three aerosol retrieval algorithms in the MODIS sensor using ground-truth measurements from AErosol RObotic NETwork (AERONET) sites over China from 2000 to 2017. In general, MAIAC products achieved better accuracy than DT and DB products in the overall validation and accuracy improvement of DB products after the QA filter, demonstrating the highest values among the three products. In addition, the DT algorithms had higher aerosol retrievals in cropland, forest, and ocean land types than the other two products, and the MAIAC algorithms were more accurate in grassland, built-up, unoccupied, and mixed land types among the three products. In the geometry dependency analysis, the solar zenith angle, scattering angle, and relative azimuth angle, excluding the view zenith angle, significantly affected the performance of the three aerosol retrieval algorithms. The three products showed different accuracies with varying regions and seasons. Similar spatial patterns were found for the three products, but the MAIAC retrievals were smaller in the North China Plain and higher in Yunnan Province compared with the DT and DB retrievals before the QA filter. After the QA filter, the DB retrievals were significantly lower than the MAIAC retrievals in south China. Moreover, the spatiotemporal completeness of the MAIAC product was also better than the DT and DB products.
Two-factor Authentication Algorithm Implementation with Additional Security Parameter Based on Mobile Application

— The article describes the results obtained during the development of the two-factor authentication (2FA) model to protect data based on the application using your smartphone. The following example was considered - two-factor authentication using a smartphone as identifiers and one-time password generation based on the hash function of encryption standards. Software implementation was performed in the programming language JavaScript and the software implementation of the proposed algorithm was analysed.
Predicting the Spread of Malware Outbreaks Using Autoencoder Based Neutral Networks

Malware Outbreaks are pervasive in today's digital world. However, there is a lack of awareness on part of general public on how to safeguard against such attacks and a need for increased cooperation between various national and international research as well as governmental organizations to combat the threat. On the positive side, cyber security websites, blogs and newsletters post articles outlining the working and spread of a malware outbreak and steps to recover from the same as well. In this project, an effective approach to predicting the spread of malware outbreaks is presented. The scope of the project is 15 Malware Outbreaks and the approach involves collecting these cyber aware articles from the web, assigning them to the 15 Malware Outbreaks using Topic Modeling and Similarity Analysis and along with Spread information of the Malware Outbreaks, this is input to auto encoder neural network for learning latent space representations which are further used to predict the spread of malware outbreak as either high or low spread outbreak, achieving a prediction accuracy of 75.56. This work can be used to process large amount of cyber aware content for effective and accurate prediction in the era of much-needed cyber security.
Text filtering based on support vector machine of semantic space

Traditionally, text filtering based on support vector machine uses the vector space model to represent the text and user profile. Vector space model draws the noise into the system because it assumes that the word in the text is independent and it influences the performance of the filtering. The proposed method was based on vector support machine of semantic space in which text and user profile were represented by the semantic space. The proposed approach used the singular-value decomposition to derive a latent semantic space. User profile and filtering threshold could been got by training the support vector machine in the semantic space. And the similarity between the user profile and new text was computed by cosine measure, after the new text was mapped into the semantic space. Experimental results show that the filtering rate of our approach can get 98.67%.
Online Auction-Based Resource Allocation for Service-Oriented Network Slicing

Future mobile communication networks (5G) are envisioned to support a wide range of use cases with diverse service requirements. Network slicing as a key architectural enabling technology for 5G can provide users with customized services and meet the requirement of economic efficiency, where resource allocation is of vital importance. In this paper, we propose an online auction-based resource allocation scheme for network slicing (ORANS) to meet the diverse service requirements and improve network economic efficiency. We first propose a bidding information construction method to provide customized bidding information for users based on service types. Next, we model the slicing resource allocation problem as an online winner determination problem, with aim to maximize the social welfare of auction participants. We then implement a resource allocation strategy and payment rule to accomplish the service-oriented optimal resource allocation. Extensive simulation experiments are conducted to evaluate the performance of the proposed ORANS and the numerical results show that ORANS significantly outperforms the greedy algorithm and the primal-dual approximation algorithm in network economic efficiency while satisfying the diverse service requirements of users.
Mechanical and biochemical response of human skin to diverse loading conditions

Introduction: Skin mechanics is of importance in various fields of research when accurate predictions of the mechanical response of skin is essential. This study aims to develop a new constitutive model for human skin that is capable of describing the heterogeneous, nonlinear viscoelastic mechanical response of human skin under shear deformation. Methods: This complex mechanical response was determined by performing large amplitude oscillatory shear (LAOS) experiments on ex vivo human skin samples. It was combined with digital image correlation (DIC) on the cross-sectional area to assess heterogeneity. The skin is modeled as a one-dimensional layered structure, with every sublayer behaving as a nonlinear viscoelastic material. Heterogeneity is implemented by varying the stiffness with skin depth. Using an iterative parameter estimation method all model parameters were optimized simultaneously. Results: The model accurately captures strain stiffening, shear thinning, softening effect and nonlinear viscous dissipation, as experimentally observed in the mechanical response to LAOS. The heterogeneous properties described by the model were in good agreement with the experimental DIC results. Conclusion: The presented mathematical description forms the basis for a future constitutive model definition that, by implementation in a finite element method, has the capability of describing the full 3D mechanical behavior of human skin. 2.
A NOVEL KEY GENERATION BASED RSA FOR PERFORMING ENCRYPTION AND DECRYPTION OF DATA

- The backbone of the modern world is electronic communication. Data is transferred from one place to another in almost no time using the electronic medium. But it also exposes the confidential data to the intruder. RSA is the most common and efficient cryptography technique that is used for the purpose of encrypting the content and then sending it over the channel, then than at receivers end the content is decrypted and converted in to original form. Although there are many security mechanisms are available. But there is a continuous need to improve the existing methods. Cryptography is a security mechanism which caters the security services of world in perfect manner. This paper will propose a novel key generation technique for both sender and receiver. This proposed technique results will be compared with existing techniques.
EEG ELECTRODE LOCALIZATION FOR READING-WRITING NEUROPATHWAY: SPECTRAL ANALYSIS APPROACH

Writing is a survival skill in schools and many children are reported to have suffered from writing disorder. Emerging technology enables this disorder detectable through monitoring of EEG signals. However, in working with EEG, the lack of methodological approach would lead to gargantuan of data and thus wastage in resources and time. Furthermore, placing of a large number of electrodes on the recording cap will cause discomfort to the subject, with data being more liable to false readings and artifacts. Recognizing the number of electrodes through proper localization plays a fundamental role in improving the overall performance of an EEG acquisition system, thus the objective of this research. This study involves the following phases: Data Collection, Data Acquisition and Data Analysis. Target population are normal and healthy subjects of age between 18 and 25. The EEG signals are recorded with electrodes at activation areas along the documented signal pathway of the brain (C3, C4, P3, P4, O1, O2, T7, FC5) during reading and writing. Fast Fourier Transform (FFT) is applied to transform the EEG in time domain into frequency domain so that signature features in frequency content during relaxation and sentence writing can be extracted. Results showed that relaxation drew on one dominant peak and the frequency content resides in the alpha sub-band while writing activity drew on two dominant peaks, one in alpha and the other in beta sub-band. The frequency range of EEG recorded during relaxation is 8-13 Hz while that during writing is 13-29 Hz, well within the alpha and beta sub-band for the different neuro-activity accordingly. Hence, it can be concluded from experimental results and findings from previous works that electrodes C3/C4, P3/P4, O1/O2, T7 and FC5 are suitable as optimal localized EEG electrode placement for neuro-pathway for reading-writing.
A Review of Software Quality Methodologies

Pervasive systems and increased reliance on embedded systems require that the underlying software is properly tested and has in-built high quality. The approaches often adopted to realize software systems have inherent weaknesses that have resulted in less robust software applications. The requirement of reliable software suggests that quality needs to be instilled at all stages of a software development paradigms, especially at the testing stages of the development cycle ensuring that quality attributes and parameters are taken into account when designing and developing software. In this respect, numerous tools, techniques, and methodologies have also been proposed. In this chapter, the authors present and review different methodologies employed to improve the software quality during the software development lifecycle.
Analysis and design of a novel 4-axis hybrid machine tool based on a 2-DOF parallel mechanism

Based on the characters of simple mechanics analysis, calibration and structure, the parallel or hybrid machine tool of less than 6 degrees of freedom (DOF) become one of the development trends of manufacturing equipment. In this paper, a planar two degrees of freedom parallel mechanism is investigated. Closed-form solutions are developed for both the inverse and direct kinematics. The singularity and workspace for the mechanism are analyzed systematically. The velocity output ratio and the global velocity output ratio about the mechanism are defined. And the dimensional design based on workspace and velocity indices is presented. In the end, a concept design for a new 4-axis hybrid machine tool is proposed.
Modeling and Detectability of Full Open Gate Defects in FinFET Technology

FinFET technology is an attractive candidate for high-performance and power-efficient application and is currently used for several electronic products. FinFET technology incorporates new technologies in the manufacturing processes that may generate new defect topologies which need to be considered during test generation. This paper analyzes the electrical behavior of full open gate defects, i.e., a transistor gate with infinite resistance. It is shown that classical models, called single open (SO) and interconnect open (IO), that have been proposed in the past for CMOS technology are not sufficient in FinFET technology. The modern FinFET-based logic cells using multifin and multifinger design techniques give rise to new specific defect topologies called “subset full open gate defect. ” The static and dynamic electrical behaviors of the two new topologies, subset SO (SOsub) and subset IO (IOsub), are analyzed, and the detectability of the defect in the context of Boolean testing and delay testing is derived. The detectability is analyzed taking into account process variation, temperature, and power supply control.
Evaluating the Hardware Cost of the Posit Number System

The posit number system is proposed as a replacement of IEEE floating-point numbers. It is a floating-point system that trades exponent bits for significand bits, depending on the magnitude of the numbers. Thus, it provides more precision for numbers around 1, at the expense of lower precision for very large or very small numbers. Several works have demonstrated that this trade-off can improve the accuracy of applications. However, the variable-length exponent and significand encoding impacts the hardware cost of posit arithmetic. The objective of the present work is to enable application-level evaluations of the posit system that include performance and resource consumption. To this purpose, this article introduces an open-source hardware implementation of the posit number system, in the form of a C++ templatized library compatible with Vivado HLS. This library currently implements addition, subtraction and multiplication for custom-size posits. In addition, the posit standard also mandates the presence of the "quire", a large accumulator able to perform exact sums of products. The proposed library includes the first open-source parameterized hardware quire. This library is shown to improve the state-of-the-art of posit implementations in terms of latency and resource consumption. Still, standard 32 bits posit adders and multipliers are found to be much larger and slower than the corresponding floating-point operators. The cost of the posit 32 quire is shown to be comparable to that of a Kulisch accumulator for 32 bits floating-point.
Human Resource Scheduling Model and Algorithm with Time Windows and Multi-Skill Constraints

In situations where an organization has limited human resources and a lack of multi-skilled employees, organizations pay more and more attention to cost control and personnel arrangements. Based on the consideration of the service personnel scheduling as well as the routing arrangement, service personnel of different skills were divided into different types according to their multiple skills. A mathematical programming model was developed to reduce the actual cost of organization. Then, a hybrid meta heuristic that combines a tabu search algorithm with a simulated annealing was designed to solve the problem. This meta heuristic employs several neighborhood search operators and integrates the advantages of both the tabu search algorithm and the simulated annealing algorithm. Finally, the stability and validity of the algorithm were validated by the tests of several kinds of examples.
Emotion Recognition on Twitter Using Neural Networks

Deep learning has recently revolutionised many fields of natural language processing but has not yet been applied to emotion recognition. Most recent studies of emotion recognition on tweets used simple classifiers on a combination of bag-of-words and human-engineered features. Hence, we worked on improving emotion-recognition algorithms using neural networks. To this end, we created three large emotion-labelled data sets corresponding to Ekman's, Plutchik's, and POMS's emotions by exploiting Twitter's popular self-annotation mechanism — hashtags. We compared the performance of bag-of-words and latent semantic indexing models with the performance of neural networks. We trained several word- and character-based, recurrent and convolutional neural networks. Further, we investigated the transferability of final hidden state representations of neural networks: how appropriate is the representation trained on one classification for recognising another one? Finally, we developed a single model for recognising all three emotion classifications from a shared representation. We show that neural networks can surpass traditional text classification approaches for emotion recognition. Recurrent neural network working directly on characters without any text preprocessing in a completely end-to-end fashion was the most successful architecture. Although models trained on single data sets have revealed poor transferability, we improved the generality of final hidden state representation in the unison model. When training the unison model, the standard training heuristic yielded unbalanced performance, due to the vast difference in data set sizes. However, the newly proposed training strategy produced a unison model with performance comparable to that of single models.
Early humans: tools, language, and culture

Given the state of the art of scholarship dealing with the evolution of world history, this chapter provides a balanced perspective between elite and other interpretations of the global past. Christian universal histories were repeatedly written in a spirit that sought to divide divine truth from heretical viewpoints. Starting from the late fifteenth century, the European conquests began having massive impacts on entire world regions, particularly the Americas and the coastal regions of Sub-Saharan Africa. The growing knowledge about different world regions fed into the epistemological crises of European historiography. During the early modern period, many societies experienced their own "culture wars" or "history wars", for example between religious and proto-secular narratives. The Eurocentric orientation of historiographical cultures in general and world history in particular continued during much of the twentieth century. Despite its limited impacts, university-based historical scholarship has a strong influence on general education systems as well as, to a certain extent, on the media.
A proposal to act on Theory of Mind by applying robotics and virtual worlds with children with ASD

The article proposes an intervention framed under a single-subject research design where robotics and a 3D virtual environment are used jointly to improve the development of Theory of Mind in children with ASD. The project aims at verifying if the use of a humanoid robot, with high interactive abilities and responses, along with a virtual robot in a social virtual world can enable an improved comprehension of emotions and perspective taking. Specifically the planned activities are designed to gradually support the subjects with ASD in interactional settings in order to make them acquire the needed self-confidence to finally interact with a classmate in the virtual environment.
Characteristics of Carbonated Hydroxyapatite Based on Abalone Mussel Shells (Halioitis asinina) Synthesized by Precipitation Method with Aging Time Variations

Carbonated Hydroxyapatite (CHAp) is a substituted-hydroxyapatite (HAp) with carbonate ions that are similar to natural inorganic bone constituents. The CHAp can be synthesized from natural material that has high calcium carbonate, such as abalone shells (Halioitis asinina), with a calcium carbonate content of 90-95% of the total abalone shell weight. In this study, the precipitation method has succeeded in synthesizing CHAp. The effect of aging time variations (0, 24, and 48 hours) during the synthesis process on CHAp characteristics was investigated. Characterization of the samples were conducted using X-Ray Diffraction (XRD), Energy Dispersive X-Ray Spectroscopy (EDX), and Fourier Transform Infrared (FTIR). Based on XRD data analysis, the diffraction peaks at32.02° were observed in all variations of aging time, i.e., the characteristics of B-type CHAp phase, in which the increasing of aging time reduces its crystallinity. B-type CHAp was formed when carbonate ions substitute the phosphate ions in the structure of HAp, as confirmed by EDX analysis. This means that the increasing of aging time improves its Ca/P mole ratio (1.71-1.81), which is greater than the Ca/P mole ratio of stoichiometric HAp (1.67). This is due to an increase in the number of carbonate ions that replace the position of phosphate ions as aging time increases. This is also confirmed by FTIR analysis, which showed that the increasing of aging time cause stronger absorption of carbonate ion in 872.9 cm−1, characteristics to B-type CHAp. From data analysis, it can be concluded that 48 hours aging time was the optimum time to produce B-type CHAp which has similar characteristics to natural bone apatite with low crystallinity.
Review On Secure Anti-Collusion Data Sharing For Dynamic Group In The Cloud

Benefited from cloud computing, users can achieve an effective and economical approach for data sharing among group members in the cloud with the characters of low maintenance and little management cost. In multiuser cloud computing there may be a major problem to securely share documents. Frequent change of membership, challenging issues to prevent the system from collusion attack, to secure the system from the revoked user. In this paper we propose a secure data forwarding mechanism for dynamic member. Firstly, we propose a cloud system in which no of server should be present any user must store the file in any server. Secondly, the file must upload in no of blocks in the same server. Thirdly, the data forwarding; the uploading user may forward the data to the requested user in the cloud. If the member of cloud should exchange the information to one another they forward the data to the id of the members. Other member can’t access file in the cloud without permission of file up loader. By this scheme the revoked user can’t access the original data. in this scheme all the time the cloud member get permission from the up loader member for access the information at the time of file transfer up loader know the requested id he provide the server id and no of block to the downloader. The file should store in the server in maximum four no of block i.e. file is splitting in four parts. Each two block should be encrypted and store in the server. RSA and MD-5 encryption algorithm should be used for encrypting four blocks. In multiuser environment user doesn’t know which encryption will used for which block.