Reviewerly/exHarmony huggingface dataset:
Software Cost Estimation Using LSTM-RNN

Any project, however, astronomically immense or minute, or regardless of the industry that is undertaken, needs to be performed and distributed under certain constraints. Cost is one of those constraints that project management needs to efficaciously control. Cost may be the driving force or an obstacle in deciding project’s future. Consequently, a trustable and meticulous effort estimation model is a perpetual challenge for project managers and software engineers. Long short term memory (LSTM) and recurrent neural networks (RNN) are utilized in this paper to suggest the incipient model for calculating the effort needed to develop a software. The result of the model is evaluated on COCOMO’81, NASA63 and MAXWELL datasets. The experimental results showed that LSTM-RNN with linear activation function (LAF) amends the precision of cost estimation in comparison with other models utilized in the study.
Autunite-Group Minerals and Their Paragenesis from the Sheared Granite of Gabal El Sela, South Eastern Desert, Egypt

G. El Sela is located in the Southern Eastern Desert of Egypt cropping as two parts, occupied by monzogranites that were categorized as biotite granite, muscovite granite and two mica granites.The northern part is more significant according its high concentrations of uranium that controlled by complicated structure regime; faulting, infrastructures and shearing are the most common structural criteria of this part.The Egyptian Nuclear Materials Authority (NMA) mined this part to produce the uranium ore.The previous mineralogical studies indicated that this granite was dominated by primary uranium minerals (pitchblende and coffinite) and secondary minerals belong to the autunite group (autunite, metautunite, phurcalite) in addition to uranophane.In the present work, petrographic and mineralogical studies are applied for the granites using the polarized and stereo microscopes and followed by electron microscope and XRD.The result of the microscopic examinations revealed the tectonic regime controlling the radioactivity and recognized the sodic autunite (meta-natroautunite) beside the pre-mentioned autunite group minerals completing the paragenetic sequrnce of these minerals.In this study, it is concluded that the sheared biotite granite is monzogranite originated during the episode of the continental plate collision (syncollision).The study finished to presence of two main types of the alteration corresponding to the two high levels of radioactivity (moderate and anomalous).The first is the thermal alteration (saussiritization, sericitization, kaolinization, silicification and hematization) and the second is the chemical transformation (oxidation, dehydration, ion substitutions and confusion) responsible for formation of the secondary uranium minerals.The temperature needed for the thermal alteration is sourced by the hydrothermal solutions, while the temperature needed for the uranium minerals transformation may be generated during the episode of the continental plate collision (syncollision).Paragenesis of these minerals indicates that they represent a series of uranyl phosphate min-How to cite this paper:
Soft Subexponentials and Multiplexing

Linear logic and its refinements have been used as a specification language for a number of deductive systems. This has been accomplished by carefully studying the structural restrictions of linear logic modalities. Examples of such refinements are subexponentials, light linear logic, and soft linear logic. We bring together these refinements of linear logic in a non-commutative setting. We introduce a non-commutative substructural system with subexponential modalities controlled by a minimalistic set of rules. Namely, we disallow the contraction and weakening rules for the exponential modality and introduce two primitive subexponentials. One of the subexponentials allows the multiplexing rule in the style of soft linear logic and light linear logic. The second subexponential provides the exchange rule. For this system, we construct a sequent calculus, establish cut elimination, and also provide a complete focused proof system. We illustrate the expressive power of this system by simulating Turing computations and categorial grammar parsing for compound sentences. Using the former, we prove undecidability results. The new system employs Lambek’s non-emptiness restriction, which is incompatible with the standard (sub)exponential setting. Lambek’s restriction is crucial for applications in linguistics: without this restriction, categorial grammars incorrectly mark some ungrammatical phrases as being correct.
Blockchain in Agribusiness Supply Chain Management: A Traceability Perspective

The demand for agricultural products for export is increasing every year. Thus, there is a need for a traceable and more communicative agricultural supply chain among its stakeholders. In addition, the increase in controls, verifications and communications in each SC agent makes agility and chain difficult, generating distrust among those involved. To overcome this issue, we consider Blockchain. Blockchain is a disruptive technology to decentralize data with this state-of-the-art technology, we develop a model that solves the traceability problem of the agricultural product. The model also improves transparency and security within the SC, increasing trust between the suppliers, collaborators and consumers.
Logical Probabilistic Biologically Inspired Cognitive Architecture

We consider a task-oriented approach to AGI, when any cognitive problem, perhaps superior to human ability, has sense given a criterion of its solution. In the frame of this approach, we consider the task of purposeful behavior in a complex probabilistic environment, where behavior is organized through self-learning. For that purpose, we suggest cognitive architecture that relies on the theory of functional systems. The architecture is based on the main notions of this theory: goal, result, anticipation of the result. The logical structure of this theory was analyzed and used for the control system of purposeful behavior development. This control system contains a hierarchy of functional systems that organizes purposeful behavior. The control system was used for modeling agents to solve the foraging task.
Shrinking generators based on <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e402" altimg="si82.svg"><mml:mi>σ</mml:mi></mml:math>-LFSRs

The word-based LFSRs called σ-LFSRs are very attractive as they take advantage of the modern word-based processor and thus increase the throughput. Secondly, the bitstream produced by σ-LFSR has excellent statistical properties with a high period except for low linear complexity. In order to increase the linear complexity, the concept of both bit-oriented shrinking and self-shrinking generators is introduced in case of σ-LFSRs. In both the cases, the lower bound for the period as well as for the linear complexity of the bitstream are shown to be exponential. Further, we have experimented and investigated more results on the periodicity and statistical properties of the bitstream in self-shrinking σ-LFSRs. This helps to find and prove the exact period of the bitstream produced by self-shrinking generators.
A novel IVS procedure for handling Big Data with Artificial Neural Networks

In recent times, thanks to the availability of a large quantity of data coming from the industrial process, several techniques based on a data-driven approach could be developed. Between all the data-driven techniques, as Principle Component Regression, Support Vector Machines, Artificial Neural Networks, Neuro-Fuzzy Systems, and many others, the data on which they rely should be analyzed to find correlations and dependencies that could improve their design. For this reason, the Input variable Selection (IVS) process has become of great interest in the recent period. The classical IVS relies on classical statistics, as Pearson coefficients, able to discover linear dependencies among data; today, due to the significant amount of data available, the challenge of also discovering non-linear dependencies appears to be a necessary skill, mainly for the design and development of a neural network. This paper proposes the use of a novel statistical tool named Maximal Information Coefficient (MIC) for developing an IVS procedure able to discover dependencies in a considerable dataset and guide the IVS designer to the selection of input variables in a data-driven application. As a case study, the procedure will be applied to a real application developed in the context of the Swedish forest industry, in order to choose the input variables of a neural network able to estimate the timber bundles volume, which represents an expensive parameter to measure in this context.
Queue length-based load balancing in data center networks

Summary Traffic load balancing in data centers is an important requirement. Traffic dynamics and possibilities of changes in the topology (e.g., failures and asymmetries) make load balancing a challenging task. Existing end‐host–based schemes either employ the predominantly used ECN or combine it with RTT to get congestion information of paths. Both congestion signals, ECN and RTT, have limitations; ECN only tells whether the queue length is above or below a threshold value but does not inform about the extent of congestion; similarly, RTT in data center networks is on the scale of up to few hundreds of microseconds, and current data center operating systems lack fine‐grained microsecond‐level timers. Therefore, there is a need of a new congestion signal which should give accurate information of congestion along the path. Furthermore, in end‐host–based schemes, detecting asymmetries in the topology is challenging due to the inability to accurately measure RTT on the scale of microseconds. This paper presents QLLB, an end‐host–based, queue length–based load balancing scheme. QLLB employs a new queue length–based congestion signal that gives an exact measure of congestion along the paths. Furthermore, QLLB uses relative‐RTT to detect asymmetries in the topology. QLLB is implemented in ns‐3 and compared with ECMP, CONGA, and Hermes. The results show that QLLB significantly improves performance of short flows over the other schemes and performs within acceptable level, of CONGA and Hermes, for long flows. In addition, QLLB effectively detects asymmetric paths and performs better than Hermes under high loads.
Secondary vocational education: possible future scenarios and development prospects

Long-term socio-economic development forecast of the Russian Federation for the period till 2030 specifies that the main barriers in the implementation of innovative and accelerated scenarios are caused by the shortage of world-competitive professional personnel both at the level of corporations and public administration, the inefficiency of coordination mechanisms. This study is aimed at studying the readiness and possibilities of the system of vocational education and training of the Russian Federation to respond to external challenges, taking into account scenarios of the long-term forecast of the social and economic development of the Russian Federation for the period up to 2035. The study is also aimed at creating scientific, organizational and methodological conditions for the positive modernization of the professional education and training system of the Russian Federation on the basis of regional development strategies, which will allow implementing measures to develop infrastructure, personnel potential, create modern conditions for the implementation of professional educational programs. The study presents predictive scenarios of the development of secondary vocational education, gives their characteristics, and identifies risks and advantages. The presented scenarios allow us to determine the socio-economic potential of vocational education, to determine the goals and objectives in its development.
Streamlining Choice of CNNs and Structure Framing of Convolution Layer

Dahiya, Sonika Tyagi, Rohit Gaba, NishchalConvolutional neural networks (CNNs) are a kind of deep neural networks which were designed from the biologically driven models. Researchers focused on how humans perceive an image in the brain. As an image is passed through different layers in the human brain, in the same way, CNNs have many layers. In this paper, the structure of CNN is described, guidelines on the design of the convolution layer and decision making on when to use a pre-trained CNN model with transfer learning and when to design our custom architecture CNN model. This will help future researchers in a quick start with CNN modeling. Experimentation is done on two popular image datasets, i.e., CIFAR-100 and Stanford clothing attribute dataset, where CIFAR-100 is a clean dataset of 60,000 images belonging to 100 classes and Stanford clothing attribute dataset is highly noisy and imbalanced data as it has uneven distribution of samples for different attributes and many of the samples do not have a clear distinction between the classes resulting in overlapping training data. Four CNNs were designed, where two models were pre-trained CNN models and two were customized CNN models and compared their performance on image classification task and treatment of the missing data in the dataset. Based on this comparison and related study, we framed the guidelines for designing a convolution layer and making choice between using a pre-trained (transfer learning) or customized CNNs.
IoT Security

The usage of the internet of things (IoT) devices is growing for the ease of life. From smart homes to smart cars, from smart transportation to smart cities, from smart hospitals to smart highways, these IoT devices send and receive highly sensitive data regarding the privacy of users or other information regarding the movement of users from one location to another location. The timing traces users when present at home and out of the home. But how does one secure this information from the attacker? There is a need for IoT devices security. As there are three layers of IoT devices—the application layer, network layer, and perception layer—three layers to be secure. IoT devices are heterogeneous and constrain energy consumption. The proposed solution in this chapter is three-way authentication of IoT devices by generating tokens from the device serial number and from the few configuration devices at the network layer. For high availability of IoT device services, the protection against distributed denial of service attack is implemented at the network layer.
Image Quality Assessment based on Dual Domains Fusion

Image quality assessment is an important and necessary task in the field of image processing. It can simulate human visual perception accurately and effectively to ensure the credibility of information. Although the existing IQA algorithm based on CNN has achieved excellent success, the generalization and robustness of the algorithm are limited due to the loss of image information during feature extraction. The research shows that the phase and amplitude of image frequency domain will change with the quality, so we proposes an image quality assessment algorithm based on dual domains fusion (DualD-IQA). Using the frequency domain and spatial domain as multiple inputs of convolutional neural network, we can complement each other to represent image quality related information. Moreover, the input of any scale can be accepted by adding bilinear pooling, so as to ensure the reliability and robustness of the quality evaluation results. Experimental results show that the algorithm in this paper achieves higher consistency and accuracy in two commonly used public databases, and has higher robustness in different distortion types and cross databases.
A Knuth-Bendix-Like Ordering for Orienting Combinator Equations

We extend the graceful higher-order basic Knuth-Bendix order (KBO) of Becker et al. to an ordering that orients combinator equations left-to-right. The resultant ordering is highly suited to parameterising the first-order superposition calculus when dealing with the theory of higher-order logic, as it prevents inferences between the combinator axioms. We prove a number of desirable properties about the ordering including it having the subterm property for ground terms, being transitive and being well-founded. The ordering fails to be a reduction ordering as it lacks compatibility with certain contexts. We provide an intuition of why this need not be an obstacle when using it to parameterise superposition.
Abstractive Text Summarization Using LSTMs with Rich Features

Abstractive text summarization using sequence-to-sequence networks have been successful for short text. However, these models have shown their limitations in summarizing long text as they forget sentences in long distance. We propose an abstractive summarization model using rich features to overcome this weakness. The proposed system has been tested with two datasets: an English dataset (CNN/Daily Mail) and a Vietnamese dataset (Baomoi). Experimental results show that our model significantly outperforms recently proposed models on both datasets.
An Adaptive Feature Selection Algorithm for Fuzzy Clustering Image Segmentation Based on Embedded Neighbourhood Information Constraints

This paper addresses the lack of robustness of feature selection algorithms for fuzzy clustering segmentation with the Gaussian mixture model. Assuming that the neighbourhood pixels and the centre pixels obey the same distribution, a Markov method is introduced to construct the prior probability distribution and achieve the membership degree regularisation constraint for clustering sample points. Then, a noise smoothing factor is introduced to optimise the prior probability constraint. Second, a power index is constructed by combining the classification membership degree and prior probability since the Kullback-Leibler (KL) divergence of the noise smoothing factor is used to supervise the prior probability; this probability is embedded into Fuzzy Superpixels Fuzzy C-means(FSFCM) as a regular factor. This paper proposes a fuzzy clustering image segmentation algorithm based on an adaptive feature selection Gaussian mixture model with neighbourhood information constraints. To verify the segmentation performance and anti-noise robustness of the improved algorithm, the fuzzy C-means clustering algorithm Fuzzy C-means (FCM), FSFCM, Spatially Variant Finite Mixture Model(SVFMM), EGFMM, extended Gaussian mixture model (EGMM), adaptive feature selection robust fuzzy clustering segmentation algorithm (AFSFCM), fast and robust spatially constrained Gaussian mixture model (GMM) for image segmentation (FRSCGMM), and improve method are used to segment grey images containing Gaussian noise, salt-and-pepper noise, multiplicative noise and mixed noise. The peak signal-to-noise ratio (PSNR) and the error rate (MCR) are used as the theoretical basis for assessing the segmentation results. The improved algorithm indicators proposed in this paper are optimised. The improved algorithm yields increases of 0.1272-12.9803 dB, 1.5501-13.4396 dB, 1.9113-11.2613 dB and 1.0233-10.2804 dB over the other methods, and the Misclassification rate(MSR) decreases by 0.32-37.32%, 5.02-41.05%, 0.3-21.79% and 0.9-30.95% compared to that with the other algorithms. It is verified that the segmentation results of the improved algorithm have good regional consistency and strong anti-noise robustness, and they meet the needs of noisy image segmentation.
The Implementation of Augmented Reality in Increasing Student Motivation: Systematic Literature Review

Abstract The purpose of this article is to analyze the implementation of augmented reality in increasing student motivation. A systematic literature review was conducted in March 2018 and updated in January 2019. This paper includes a literature review of relevant research of motivation in augmented reality since in year 2013. The object of research is the augmented reality, and the subject is the student motivation. As a result of the study, it has been found that augmented reality gives a positive result in increasing student motivation. The study show that augmented reality has a potential to have a significant demand to increase student motivation in learning and therefore the need to develop an appropriate tool to keep up student motivation in learning at schools and universities. Results have shown that students that used augmented reality is more confidence and show higher learning motivation in learning.
EFFICIENT OPERATIONS IN LARGE FINITE FIELDS FOR ELLIPTIC CURVE CRYPTOGRAPHIC

An efficient method to compute the finite field multiplication for Elliptic Curve point multiplication at high speed encryption of the message is presented. The methods of the operations are based on dynamic lookup table and modified Horner rule method. The modified Horner rule method is not only to finite field operations but also to Elliptic curve scalar multiplication in the encryption and decryption. By comparison with using Russian Peasant method and in the new proposed method, one of the advantages of utilizing the proposed algorithm is that in the Elliptic Curve point addition are reduced by a factor of three in GF (2163). Therefore, using the Algorithm 1 running on Intel CPU, computation cost of the multiplication method is above 70% faster than using standard multiplication by Russian Peasant method. Ultimately, the proposed Algorithm 1 for evaluating multiplication can be made regular, simple and suitable for software implementations.&#x0D;
Observer Design for Fractional-Order Chaotic Neural Networks With Unknown Parameters

Unlike the observer design for a conventional system, designing observer for a fractional-order one is a challenging work due to the different operational properties between the traditional calculus and the fractional calculus. In this paper, two observers for fractional-order neural networks (FONNs) with and without parametric uncertainties are designed, respectively. By using fractional stability criteria, it is shown that the observe errors converge to an arbitrary small region eventually. By using a new sliding term in the synchronization controller desgin, the proposed observers have good robustness. Simulation studies are given to verify the theoretical derivation at last.
Compartmented Secret Sharing Schemes and Locally Repairable Codes

Multipartite secret sharing is an important research object in the area of secret sharing schemes. Compartmented access structures are an interesting class of multipartite access structures. The constructions of ideal linear schemes realizing compartmented access structures are studied by codes in this paper. We find that compartmented secret sharing is related to a class of codes called locally repairable codes which are being used widely in distributed and cloud storage systems. We study secret sharing schemes for compartmented access structures with upper bounds, compartmented access structures with lowers bounds and compartmented access structures with upper and lower bounds. We establish the relationships between secret sharing schemes for these compartmented access structures and locally repairable codes. Based on the relationships and some locally repairable codes, we obtain ideal linear schemes realizing these compartmented access structures by efficient methods.