Reviewerly/exHarmony huggingface dataset:
Applied Machine Learning Strategies

Recent advances in machine learning (ML), fueled by new frameworks and algorithms, more powerful computing architectures, scalable cloud-based services, and availability of large-scale data sets, have enabled scientists and engineers to tackle more complex problems than ever before. Computer hardware has made tremendous leaps in processing power, bit depth, caching, and storage. Graphics processing units, developed originally for the gaming industry, provide a parallel processing capability that is ideally suited to computer vision (CV) and the simulation of artificial neural networks. The expansion to cloud-based services allows researchers virtually unlimited scaling of resources to tackle problems having millions of input attributes, with output domains up to thousands of potentially nonexclusive classes.
An Empirical Assessment of Software Engineering Curriculum in Developing Economies and its Professional Relevance

The role or professionally trained and skilled human resource for a self-sustained IT infrastructure in any economy is very significant. The growth of IT industry in several emerging economies in last couple of decades owes mainly to mature curriculum in IT, Software Engineering and related curriculum. In one of my previous research, I had evaluated and analyzed the impact of software engineering curriculum from the perspective of students. In this work, we have expanded upon the same topic by conducting analysis of feedback from professionals and graduating students in various universities of kingdom of Saudi Arabia. The work is aimed to explore what professional areas of current software engineering curriculum are driving the professional market for students and what need further strengthening. The research as yielded some very interesting results which would be discussed in the following sections
Pseudo Conditional Regularization for Inverse Mapping of GANs

Inverse mapping of the Generative Adversarial Networks (GANs) which projects data to latent space have been recently introduced, and it is shown that the inverse mapping models trained by the bidirectional adversarial learning can enable novel and practical operations including interpolation between real data. However, existing techniques still do not ensure the consistent mapping between the data and their latent representation so that the models are hardly converged throughout training steps. Our discussion begins with empirical investigations on the inconsistency issue of the prior techniques, and we further propose a novel adversarial learning method, Pseudo Conditional Bidirectional GAN (PC-BiGAN), for training the inverse mapping of GANs with a high degree of consistency and similarity-awareness. Our models are specifically guided by the pseudo conditions defined by the proximity relationship among data in unsupervised learned feature space. We demonstrate that our novel bidirectional adversarial learning frameworks improve the performance in sample reconstruction, generation, and interpolation.
NFC Payment Security with Cloud Based Authentication System

Near Field Communication is a new medium of wireless communication. NFC technology is now widely introducing in smartphones. NFC technology in smartphone has made them capable of contact-less payment on POS terminals. The security protocol used for contact and contact-less payments is named as EMV (Europay Master Visa). EMV sets the security standards for online transactions in contact and contact-less payments. When deeply analyzed, EMV protocol has security vulnerabilities in (1) Mutual Authentication and (2) Exchange of banking information between payment device and payment terminal. As NFC payment involves exchange of sensitive data in open environment within a range of 10 cm, risks are involved for data being theft. We introduced cloud hosted security protocol to overcome vulnerabilities in EMV standards. The authenticity of this protocol is analyzed using Scyther tool. The protocol uses an authentication server hosted on cloud and asymmetric encryption in mutual authentication and exchange of banking data between payment device and payment terminal.
Mixing ICI and CSI Models for More Efficient Probabilistic Inference

Conditional probability tables (CPTs) in Bayesian Networks (BNs) have exponential space on the family size. Local models based on independence of causal influence (ICI) or context-specific independence (CSI) have been applied separately to improve the efficiency. We propose a framework to mix both local models in the same BN for improved efficiency. In particular, we show that ICI and CSI are orthogonal, and each is unable to express the other efficiently and accurately. We propose a formalism to encode both types of local models in the same BN, and to convert it into a homogenous representation to support exact inference. We report experimental evaluation where significant efficiency gain is obtained in exact inference.
Single Image Super Resolution via Deep Convolutional Dual Upscaling Branches with Different Focus

Deep Convolutional neural networks (CNN) have demonstrated that they possess outstanding reconstruction accuracy and computing efficiency on single image super resolution. However, CNN models always result in difficult training process due to its deep layers (beyond 30 sometimes) and high-resolution space computing environment. In this point, a super resolution model which has dual branches and which is based on deep CNN is proposed. Specifically, one branch reconstructs image details using recursive module and Network in Network (NIN) in low-resolution space, when another technically transmits low-frequency information by training an upscaling filter. By means of end-to-end training with a relatively shallow network structure, the proposed algorithm steps into state-of-the-art performance while achieving highly efficient computation.
Bio-inspired High-Utility Item Framework based Particle Swarm Optimization Tree Algorithms for Mining High Utility Itemset

Interestingness measures play a significant part in data mining and intended for selecting or ranking patterns relevant to the latent interest of users. Utility mining is a method of finding a high utility itemset (HUIs) from a database. Recently, bio-inspired algorithms had attracted considerable attention in many areas which led the evaluations of the new algorithm, and it has shown excellent performance. In this work, high-utility itemset framework-based particle swarm optimization (HUIF-PSO) is improved using the frequent pattern tree structure to retrieve HUIs effectively. Improved HUIF-PSO is compared with HUIF-PSO, high-utility itemset framework-based genetic algorithm (HUIF-GA) and high-utility itemset framework-based bat algorithm (HUIF-BA). The HUI’s count, memory and time usage are tested, and the outcome displays that the HUIF-PSO tree algorithm performs better than existing algorithms.
Digital and Reflexive Technologies as an Innovation in Teachers’ Professional Training

The professional education system pays special attention to the implementation and use of innovative educational technologies. This term is defined as a new generation pedagogical technology making the learning process active and interactive. We consider digital and reflexive technologies to be innovative ones. The research problem consists of justifying the effectiveness of the digital and reflective technologies use in the teachers’ professional training. The article aims to analyze the educational practice in the system of higher and additional professional education. To reach the aim, firstly, we gave our definition of digital and reflective technologies. Secondly, we showed these technologies innovativeness in the teachers’ professional training. To do this we conducted the pedagogical experiences analysis. Finally, we prove the effectiveness of digital and reflective technology in the teachers’ professional training. The system-activity approach was used to address the research objectives. The study was held on the basis of the Arkhangelsk Regional Institute of Open Education and Northern (Arctic) Federal University named after M. V. Lomonosov.
Temperature Prediction using Time Series Time-Delay Neural Networks

Having an accurate temperature predictions, provides a very positive impact on various fields such as agricultural based industries, aviation, tourism and also taking precautionary measures, in case of extreme weather conditions like heatwaves. This paper propose a temperature prediction model that covers the city of Agadir, The data used to train and test the network are real data obtained by the Moroccan Meteorological Administration (METEO MAROC), which represent temperature values on 30 minutes basis, taken from sensors based in the AGADIR EL MASSIRA Airport. The model is based on the use of Time Series Time-Delay Neural Networks. A type of networks considered to be one of the most straightforward dynamic network, which consists of a feedforward network with a tapped delay line at the input. The designed prediction network model showed good performance, as shown in the calculated mean squared errors (MSE) for the testing data set is about 0.4149, and its correlation coefficient R is about (0.99). The correlation of the predicted error with time showed that almost all the autocorrelation function values fall within the bound of the confidence interval.
Techniques for Learning to See in the Dark: A Survey

Low light imaging is one of the vital and modernistic research areas in digital image processing. Low light imaging is a complex task due to low SNR and photon count. This happens when the image signal is simply too weak to pay for the noise. There are many unfold algorithms and every approach has its assumptions, blessings, and limitations. The range of techniques such as de-mosaicking, de-noising, image enhancement, amplification for low light images has been introduced. This paper provides a review of some imperative and significant work that has been carried out in the field of low light imaging. After providing a short introduction, an outline of various algorithms and analysis, some vital approaches have been classified into different respective groups. The main intention of the technique used in this paper is to process the image so that the outcome is more compatible than the original one i.e. altering and analyzing pictorial information of images. Finally, it discusses the technique which is appropriate for low light imaging.
An Energy-Efficient Architecture for Accelerating Inference of Memory-Augmented Neural Networks

Although recurrent neural networks (RNNs) have shown excellent performance in sequence-related applications such as speech recognition and image caption, they are forceless in the domain of cognitive reasoning like question answering and algorithm learning due to the limited memory capacity. To address this issue, memory-augmented neural networks (MANNs) have been proposed to achieve excellent reasoning ability in cognitive applications by coupling neural networks (mostly RNNs) to an external memory which can be written and read. However, MANNs require numerous operations and memory accesses to interact with external memory, which hinders the deployment of MANNs in low-power devices. In this work, we propose an algorithm-hardware cooperated full-stack approach to accelerate the inference of MANNs. Firstly, we propose an operator scheduling mechanism to optimize the calculation process of MANNs for high computation parallelism and inference efficiency. Secondly, we propose a tri-mode softmax computing scheme to reduce calculation overheads for MANNs with different accuracy and latency requirements. Finally, a reconfigurable architecture is designed to efficiently implement each operator in MANNs for high inference speed and energy efficiency. Tested on bAbI dataset, the proposed optimizations and architecture achieves 1.28× improvement of energy efficiency for MANNs compared with GPU implementation.
Sub-word Embedding Auxiliary Encoding in Mongolian-Chinese Neural Machine Translation

For low-resource Mongolian-Chinese neural machine translation (NMT), the common pre-processing methods such as byte pair encoding (BPE) and tokenization, are unable to recognize Mongolian special character, which leads to the loss of complete sentence information. The translation quality of low-frequency words is undesirable due to the problem of data sparsity. In this paper, we firstly propose a process method for Mongolian special character, which can transform the Mongolian special characters into explicit form to decrease the pre-processing error. Secondly, according to the morphological knowledge of Mongolian, we generate the sub-word embedding with large scale monolingual corpus to enhance the contextual information of the representation of low-frequency words. The experiments show that 1) Mongolian special character processing can minimize the semantic loss, 2) systems with sub-word embedding from large scale monolingual corpus can capture the semantic information of low-frequency words effectively 3) the proposed approaches can improve 1-2 BLEU points above the baselines.
Alternant and Algebraic Geometric Codes

Alternant codes are subfield subcodes of a generalised Reed–Solomon code over an extension field of \({\mathbb F}_q\). This is a large class of linear codes which includes BCH codes, one of the families of cyclic codes which appeared in Chapter 5. Although BCH codes are not asymptotically good, we will prove that there are asymptotically good alternant codes. Not only are alternant codes linear, and so easy to encode, they also have an algebraic structure which can be exploited in decoding algorithms. However, as with the codes constructed in Theorem 3.7, the construction of these asymptotically good alternant codes is probabilistic. We prove that such a code must exist without giving an explicit construction.
Estimating Knowledge Category Coverage by Courses Based on Centrality in Taxonomy

The proliferation of Massive Open Online Courses has made it a challenge for the user to select a proper course. We assume a situation in which the user has targeted on the knowledge defined by some knowledge categories. Then, knowing how much of the knowledge in the category is covered by the courses will be helpful in the course selection. In this study, we define a concept of knowledge category coverage and aim to estimate it in a semi-automatic manner. We first model the knowledge category and the course as a set of concepts, and then utilize a taxonomy and the idea of centrality to differentiate the importance of concepts. Finally, we obtain the coverage value by calculating how much of the concepts required in a knowledge category is also taught in a course. Compared with treating the concepts uniformly important, we found that our proposed method can effectively generate closer coverage values to the ground truth assigned by domain experts.
ФАКТОРИ СПРИЙМАННЯ ЧИТАЧАМИ ОБРАЗІВ АВТОРІВ ХУДОЖНІХ ТВОРІВ

First the image of the author of works of art in the perception of readers by the method of semantic differential is studied together with the use a statistical analysis of competence levels of readers. Morality, stability, activity, creativity and aesthetics are the most important characteristics of writers and poets for their readers. The 3 levels of reading competence were identified, which corresponds to our classification, where we distinguish passive, active and creative readers.
Trace forms of certain subfields of cyclotomic fields and applications

In this work, we present a explicit trace forms for maximal real subfields of cyclotomic fields as tools for constructing algebraic lattices in Euclidean space with optimal center density. We also obtain a closed formula for the Gram matrix of algebraic lattices obtained from these subfields. The obtained lattices are rotated versions of the lattices $ \Lambda_9, \Lambda_{10}$ and $\Lambda_{11}$ and they are images of $\mathbb{Z}$-submodules of rings of integers under the twisted homomorphism, and these constructions, as algebraic lattices, are new in the literature. We also obtain algebraic lattices in odd dimensions up to $7$ over real subfields, calculate their minimum product distance and compare with those known in literatura, since lattices constructed over real subfields have full diversity.
A virtualized data center energy‐saving mechanism based on switching operating mode of physical servers and reserving virtual machines

Abstract The energy consumption of virtualized data centers has grown very fast in last several years. Because a large number of hosts are running in an idle state, virtualized data centers waste a large amount of electric energy. To save more energy for virtualized data centers, an energy‐saving mechanism is proposed based on switching operating mode of physical servers and reserving virtual machines (VMs). The main idea is that when the amount of idle VMs reaches twice of a specified threshold, half of these idle VMs are reserved to process the new task that is about to arrive, and the physical server which hosts the other half of the idle VMs is switched to the sleep mode. From the perspective of task arrival rate and sleep parameters, we use two‐dimensional Markov processes to analyze the proposed energy‐saving mechanisms. By using matrix geometry solutions, we theoretically estimate energy consumption and response performance. According to the numerical experiments, the proposed energy‐saving mechanism obviously cut down electric energy consumption and ensures response performance. Finally, the specified threshold for the amount of VMs is optimized by building a cost function.
Fully secure wicked identity-based encryption resilient to continual auxiliary- inputs leakage

To allow more general key derivation patterns, the concept of wicked identity-based encryption (wicked IBE) was introduced. It is an extension of hierarchical identity based encryption (HIBE). Recently, research on leakage-resilient wicked IBE has attracted much attention for its consideration on the problem of key leakage in system. However, the existing works implement leakage resistance under the bounded leakage model and fail to capture general form of leakage attacks. Through designing the generation of the master secret key and the user secret key reasonably, a wicked IBE scheme resilient to continual auxiliary-inputs leakage is proposed in this paper. The leakage of our scheme is not only from the user secret key, but also from the master secret key. We show the scheme is proven fully secure in the standard model based on the subgroup decision assumption in composite-order bilinear groups and the Goldreich-Levin theorem in Galois field. To achieve this goal, the dual system encryption technology is exploited in the security proof. Compared with existing leakage-resilient wicked IBE, our scheme has a better performance in leakage resilience and security.
Quantifying the Perceived Contrast of Complex Images

This paper addresses the problem of no-reference assessing the overall image contrast. The purpose of this work is to increase the accuracy of quantifying the perceived contrast of the multi-element image. To this end, we propose a new approach to quantify the perceived contrast of a complex multi-element image. The proposed approach is based on the analysis of the dependencies of brightness increments on the image adaptation level and the size ratio of image elements. Based on the proposed approaches, new techniques for quantifying the incomplete integral contrast also are considered. Research confirms the effectiveness of the proposed approach to quantifying the perceived contrast of complex images.
Maneuvering Reentry Target Tracking by Means of Passive Radar

A reentry target tracking problem is considered by means of passive antenna array. The target is thought to constantly emit a signal with known frequency so Doppler frequency measurements are available. IMM algorithm is proposed to take account of variable target dynamics. Several sets of EKF filters which include both MaRV and BRV dynamic models were compared through RMSE of the target position and velocity. Additionally, three linearization techniques were considered showing different accuracy and computational complexity.
Consciousness and the Brain

I.P. Pavlov wrote: "First of all it is important to comprehend psychologically and then translate into physiological language". On the examples of comparison of the evoked potentials with a theory of signal identification (A.M. Ivanitskiĭ) and need-informational theory of emotions (P.V. Simonov) with the "general low of human emotions" (D. Price, J. Barrell) is demonstrated the efficiency of the simultaneous study of the higher brain functions by neurophysiological and psychological methods. Recent experimental data are underlied by the natural-scientific bases of consciousness. From the author's point of view consciousness is knowledge, which by means of words, mathematical symbols and artistic images may be shared with other individuals, with other generations in a form of cultural monuments. Recent studies by O. Grindel' have shown that preservation of connections between the gnostic cortical areas with the verbal brain structures is a necessary condition of consciousness functioning. The subconsciousness is well automatized and because of that no longer conscious skills, well learned social norms controlling behaviour and experienced as "the voice of heart", "the call of duty" etc. Super-Ego by Freud belongs to the subconscious sphere of the psychic. The superconsciousness (creative intuition) is an initial stage of a creative venture, uncontrolled by consciousness and will: initial closing of new temporary connections (whether they conform to the reality or not is defined only later). Actual controversy between determinism and freedom of choice may be removed by the complementation principle. The man is not free (determined) from the point of view of an external observer, who considers the determination of the man by genetic factors and educational conditions.(ABSTRACT TRUNCATED AT 250 WORDS)
A Computation Model for Senior Citizen Health Self-Care

A computation model is proposed for senior citizen health self-care through adaptive multi-level computation cycles according to the principles of slow intelligence. An experimental system, called the TDR system, is being implemented on a smart phone to serve as the test bed for the proposed approach. In this paper, we describe the computation model, the basic concepts and the prototype experimental system. The main theoretical concept is centered on adaptive multi-level computation cycles, events and event icons. Further experimental as well as theoretical investigations of the proposed computation model are discussed.
Regional high-precision flight route planning method based on tracking performance of measurement and control equipment

Aiming at the external field accuracy assessment of measurement and control equipment, and based on equipment tracking performance, a regional high-precision flight route planning method is designed. This method combines the general two-dimensional XY coordinates with the latitude and longitude of the earth, which significantly improves the conversion efficiency of theoretical research and practical application. Through simulation, the validity of the two common situations of single-point constraint and two-point constraint was successfully verified, indicating that the evaluation method can provide an effective reference basis for the accuracy assessment of measurement and control equipment for route planning.