Reviewerly/exHarmony huggingface dataset:
ACTIVITY APPROACH AS A PRACTICE-ORIENTED BASIS FOR THE FORMATION OF PROFESSIONAL QUALITIES OF CADETS OF AVIATION NAVIGATORS

The article discusses the issue of the formation of professionally important qualities (PIQ) in future military aviation navigators in the educational environment of a military university. The goal is to substantiate the activity approach as a practice-oriented basis for the formation of professional qualities in cadets-navigators. A review and analysis of scientific research, publications is carried out, allowing to discover the significance of the activity approach as a methodological direction of research. The methodological basis of the research is the provisions of the activity approach, which are used as the basis for the development and formation of the PIQ among cadets-navigators in the process of a practice-oriented educational process. The description of the results is associated with the characteristics of the content and organization of the professional activity of cadets-navigators in the process of implementation of industrial practice in the form of flight training. Using the activity approach, the main components of the activities of flight instructors, teachers and cadets-navigators are considered, from a unified methodological point of view, and thus the nature of their interaction is revealed. Emphasis have been determined that need to be paid attention to in the course of pedagogical activity on the formation of professional qualities among cadets-navigators. A definition is given and pedagogical technologies are proposed for the formation of professional experience and professional qualities in a future specialist in the context of a practice-oriented pedagogical process. The relationship between the experience gained by the trainee in the practice-oriented pedagogical process and the formation of professionally important personality traits of a specialist is considered. The main goals and objectives for the formation of the necessary professional qualities of cadetsnavigators during flight training have been determined. At the entrance of each stage of flight training, technologies are determined aimed at rational and purposeful impact on the student in order to develop competencies and personal qualities. Recommendations are offered to teachers and instructors to improve the efficiency of development and formation among cadets-navigators of the PIQ.
Wisdom stadium construction based on face recognition technology (badminton direction)

The stadium is a special building. Its main goal is to organize sports events. How to ensure the safety of athletes, staff and spectators is the primary consideration. Therefore, the construction of stadium security facilities is particularly important. To ensure the smooth progress of various events, it is necessary to improve the stadium security system, which is an integral part of the intelligent construction of stadium. The most important security system of the stadium is access control. Therefore, this paper proposes an access control system based on face recognition technology to ensure that every badminton match in the stadium can be completed smoothly.
Stiffness modelling of flexible support module for Large-aperture laser transmission unit

Abstract Large-aperture laser transmission unit (LLTU) is a device that focuses the laser beam to the center of the target, which are often designed as compliant mechanisms to achieve micro displacement adjustment. In the traditional mechanisms, they are designed as integrated micromanipulation systems, and driven by piezoelectric ceramics. However, most of these researches only focuses on motion accuracy, due to the lack of consideration of large load problems, the application is greatly limited. To this end, a flexible support module (FSM), as well as its stiffness model, was presented in this paper. Combined with finite element method (FEM) of FSM, structure size optimization was also completed, successfully solved the problems of stress concentration and load of FSM in engineering application. Moreover, a dual vision-based measurement method was introduced, to verify the stiffness model and analyze the repetitive error of FSM. From this result, the prototype enabled 5 mm and 0.007 rad of working area with average error of 0.3192 mm and -0.0036 rad. The repeatable error is within 7%, and will decreased to 4% with internal stress released in 5~15min.
Efficient Multiplier Architecture for ECC Core

High significance of elliptic Curve Cryptography (ECC) has been seen in many applications over the years since its development in 1986. ECC is widely utilized for security academies, design protocols, device security, arithmetic algorithms and structures etc. Due to its shorter key length, ECC has becomes first choice for many IoT and electronic devices. However, enormous enhancement of cyber-attacks and crimes in digital market has provided a necessary reason to make ECC more robust and stable to handle this kind of threats i.e. a major enhancement in ECC core is a necessity. Therefore, this paper introduces a High Throughput Concurrent Computation (HTCC) technique for Elliptic Curve point multiplier architecture. The synthesis using the proposed HTCC technique is performed on Xilinx Virtex – 5 and Xilinx Virtex – 7 FPGA over GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">163</sup> ). The efficiency is increased by 75.31% for Xilinx Virtex – 5 and 47.75% using proposed model for Xilinx Virtex – 7. The resource utilization is decreased by 65.80% for Xilinx Virtex – 5 and 67.59% using proposed model for Xilinx Virtex – 7. The performance results are satisfactory for the factors like timing, resource utilization, frequency and latency of the architecture.
Impact Of Covid-19 On Education Using Twitter Data

With the rapid spread of Covid-19 cases throughout the world, all the nations have been sentenced to partial or complete lockdowns for their safety. Throughout this one section which has been almost completely under lockdown was the Education sector where students were hardly allowed to attend physical classes around 1.5 years now. Our aim in this paper is to use the social media tweets in understanding how the people have felt throughout this course of time, what difficulties they have faced, what are the advantages, disadvantages they feel are of this new normal. We will be extracting data from the social media using natural language processing, text mining techniques, sentiment analysis to determine various insights as to what they have faced and can this New Normal be actually normal someday. We have used Tableau Software to highlight the countries where concerned tweets have been posted more. Also using Word cloud we have tried to map the main concerns. We see that there have mixed concerns related to Education from fear of letting the students attend physical mode lectures to concerns as to what side-effects would be caused due to late conduction of exams and reaction to various government laws regarding the same.
Dynamic Texture Recognition Using PDV Hashing and Dictionary Learning on Multi-Scale Volume Local Binary Pattern

Spatial-temporal local binary pattern (STLBP) has been widely used in dynamic texture recognition. STLBP often encounters the high-dimension problem as its dimension increases exponentially, so that STLBP could only utilize a small neighborhood. To tackle this problem, we propose a method for dynamic texture recognition using PDV hashing and dictionary learning on multi-scale volume local binary pattern (PHD-MVLBP). Instead of forming very high-dimensional LBP-histogram features, it first uses hash functions to map the pixel difference vectors (PDVs) to binary vectors, then forms a dictionary using the derived binary vector, and encodes them using the derived dictionary. In such a way, the PDVs are mapped to feature vectors of the size of the dictionary, instead of LBP histograms of very high dimension. Such an encoding scheme could extract the discriminant information from videos in a much larger neighborhood effectively. The experimental results on two widely-used dynamic textures datasets, DynTex++ and UCLA, show the superior performance of the proposed approach over the state-of-the-art methods.
Innovative Approaches for Customer Satisfaction and Quality Service Evaluation in Matera 2019

This paper shows some important innovative approaches patented and implemented in three most important luxury hotel located in Matera 2019, as innovative example of sperimental development applied in inusual contexts as hotels. Hotels and all accommodation company need to improve their services and the customer service, care and satisfaction, particularly in a luxury context, then research and development approach can be the right key to reach interesting results and improve also the tourism in the area where are located. Palazzo Gattini Luxury Hotel (5 star luxury hotel located in front of Matera’s Cathedral), Palazzo Viceconte (4 star hotel in historical palace of Matera) and Hotel Degli Ulivi (3 star hotel near Matera) developed tailor-made patented approaches for luxury compliance, customer service and event management, with the use of modern technologies as Internet of Things and Blockchain approach, with the objective to a better service, notoriety and to improve the word of mouth tourism around Matera. Scope of this work is to evaluate this approaches to provide new guidelines for an improvement of tourism in all the southern Italy.
Temporal Regular Path Queries

In the last decade, substantial progress has been made towards standardizing the syntax of graph query languages, and towards understanding their semantics and complexity of evaluation. In this paper, we consider temporal property graphs (TPGs) and propose temporal regular path queries (TRPQs) that incorporate time into TPG navigation. Starting with design principles, we propose a natural syntactic extension of the MATCH clause of popular graph query languages. We then formally present the semantics of TRPQs, and study the complexity of their evaluation. We show that TRPQs can be evaluated in polynomial time if TPGs are time-stamped with time points, and identify fragments of the TRPQ language that admit efficient evaluation over a more succinct interval-annotated representation. Finally, we implement a fragment of the language in a state-of-the-art dataflow framework, and experimentally demonstrate that TRPQ can be evaluated efficiently.
A Faster Hardware Implementation of the AES S-box

In this paper, we propose a very fast, yet compact, AES S-box, by applying two techniques to a composite field <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$GF((2^{4})^{2})$</tex> fast AES S-box. The composite field fast S-box has three main components, namely the input transformation matrix, the inversion circuit, and the output transformation matrix. The core inversion circuit computes the multiplicative inverse over the composite field <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$GF((2^{4})^{2})$</tex> and consists of three arithmetic blocks over subfield <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$GF(2^{{4}})$</tex> , namely exponentiation, subfield inverter, and output multipliers. For the first technique, we consider multiplication of the input of the composite field fast S-box by 255 nonzero 8-bit binary field elements. The multiplication constant increases the variety of the input and output transformation matrices of the S-box by a factor of 255, hence increasing the search space of the logic minimization algorithm correspondingly. For the second technique, we reduce the delay of the composite field fast S-box, by combining the output multipliers and the output transformation matrix. Moreover, we modify the architecture of the input transformation matrix and re-design the exponentiation block and the subfield inverter for lower delay and area. We find that 8 unique binary transformation matrices could be used to change from the binary field <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$GF(2^{8})$</tex> to the composite field <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$GF(({2}^{{4}})^{2})$</tex> at the input of the composite field S-box. We use Matla <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{b}$</tex> ® to derive all <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(255\times 8=2040)$</tex> new input transformation matrices. We search the matrices for the fastest and lowest complexity implementation and the minimal one is selected for the proposed fast S-box. The proposed fast S-box is 24% faster (with 5% increase in area) than the composite field fast design and 10% faster (with about 1% increase in area) than the fastest S-box available in the literature, to the best of our knowledge.
Faster RCNN based Vehicle Detection and Counting Framework for Undisciplined Traffic Conditions

With recent development in deep learning and computer vision techniques, Intelligent Transportation System (ITS) has emerged as a useful tool for traffic management, congestion control and building a sound traffic infrastructure. For effective monitoring of traffic conditions, there is a need to detect and count moving vehicles. Previously, several computer vision techniques have been proposed for vehicle recognition. However, majority of the techniques were not able to cover undisciplined traffic conditions. Moreover, these frameworks do not include the local vehicles of South Asian nations like Pakistan, Bangladesh and India. Keeping in view the limitations of existing frameworks, this paper presents an efficient vehicle detection and counting model for undisciplined traffic to enhance the efficiency of ITS. A dataset of more than 1200 images of vehicles has been collected comprising of 6 categories of local vehicles considering undisciplined traffic conditions to ensure robustness in vehicle detection and counting system. Faster RCNN algorithm has been used to detect moving vehicles. The experimental results show precision of 82.14% in terms of mAP.
Making personnel selection smarter through word embeddings: A graph-based approach

This paper employs techniques and algorithms from the fields of natural language processing, graph representation learning and word embeddings to assist project managers in the task of personnel selection. To do so, our approach initially represents multiple textual documents as a single graph. Then, it computes word embeddings through representation learning on graphs and performs feature selection. Finally, it builds a classification model that is able to estimate how qualified a candidate employee is to work on a given task, taking as input only the descriptions of the tasks and a list of word embeddings. Our approach differs from the existing ones in that it does not require the calculation of key performance indicators or any other form of structured data in order to operate properly. For our experiments, we retrieved data from the Jira issue tracking system of the Apache Software Foundation. The evaluation results show, in most cases, an increase of 0.43% in the accuracy of the proposed classification models when compared against a widely-adopted baseline method, while their validation loss is significantly decreased by 65.54%.
Corpus Design Challenges and Analysis of Acoustic Information for Automatic Dialect Classification

The primary challenge across the field of automatic speech communication relies upon the understanding of creation of such acoustic models that are capable of representing distinct differences throughout the spoken languages. The design of such systems is undoubtedly affected by the parameters such as an Individual’s age, gender, speaking style, dialectal variability, recording environment etc. This work investigates on the various challenges faced during designing an enriching corpus and analyzing the dialectal variations by tracking the variance of different acoustic parameters such as pitch contour, formant frequency, and intensity for different toneme sounds. The article also makes an attempt to discuss the process and problem of tone assimilation (tone sandhi) in di/multi-syllable words of Punjabi language where the occurrence of more than one tone is being anticipated. The pitch variations of the input signal are performed using PRATT toolkit, to establish a relation between the pitch variations across the various tonemes. The model is further evaluated using the ANOVA test for the comparative classification of dialects based on acoustic features. The entire work is based upon the four dialects of Punjabi language; classified as the 10th most spoken language across the world, with over 105 million native speakers.
ALiSE: Non-wearable AR display through the looking glass, and what looks solid there

With the Augmented Reality mirror display method using a half-mirror, there is a difference in the focal length between the mirror image and the AR image. Therefore, the observer perceives a mismatch in depth perception, which impairs usability. In this study, we developed an optical-reflection AR display, ALiSE (Augment Layer interweaved Semi-reflecting Existence), which enhances the depth perception experience of AR images by adding a gap zone with the same depth as the target depth between the display and the half-mirror. We conducted an experiment to view 3D objects and achieve virtual fitting using the existing AR with video synthesis and the proposed ALiSE method. As a result of the questionnaire survey, although the comfort of wearing virtual objects was below existing methods, we confirmed that the presence and solidity were superior with the proposed method to other approaches. This is an attempt to create a sense of the stereoscopic effect despite the 2D projection, as the object to be projected is simultaneously reflected in the mirror along with the observer themselves.
Noise-Induced Barren Plateaus in Variational Quantum Algorithms

Variational Quantum Algorithms (VQAs) may be a path to quantum advantage on Noisy Intermediate-Scale Quantum (NISQ) computers. A natural question is whether noise on NISQ devices places fundamental limitations on VQA performance. We rigorously prove a serious limitation for noisy VQAs, in that the noise causes the training landscape to have a barren plateau (i.e., vanishing gradient). Specifically, for the local Pauli noise considered, we prove that the gradient vanishes exponentially in the number of qubits n if the depth of the ansatz grows linearly with n. These noise-induced barren plateaus (NIBPs) are conceptually different from noise-free barren plateaus, which are linked to random parameter initialization. Our result is formulated for a generic ansatz that includes as special cases the Quantum Alternating Operator Ansatz and the Unitary Coupled Cluster Ansatz, among others. For the former, our numerical heuristics demonstrate the NIBP phenomenon for a realistic hardware noise model.
Development of new deep neural network architecture based on hodgkin huxley model

Abstract There have been many studies to simulate the functions of the brain from the neural level to the cognitive function. Since the deep neural network has shown high performance for many tasks, researches for artificial intelligence are focused on deep neural networks. In terms of neurophysiology, the configuration of nodes used for a deep neural network is too simplistic to represent real neurons. Therefore, simulation of the brain activities in response to stimuli from the external environment using the deep neural networks is inappropriate. For that reason, our group makes a new deep neural network configuration based on the Hodgkin Huxley model. The Hodgkin Huxley model has high biological plausibility for representing the firing pattern of real neurons. Therefore, the deep neural network architecture based on the Hodgkin Huxley model successfully mimics human cognitive process and is suitable for simulation assuming external stimuli. The most important point for constructing the new architecture is setting the proper learning rule that updates the synaptic information for solving cognitive problems. We used the idea of reinforcement learning so that each neuron composing the neural layer does the synaptic information update as an agent. This learning method enables to increase the computing time linearly proportional to the number of neurons, unlike convolution neural networks. Also, we introduce an algorithm that optimizes the firing rate of neurons to generate synaptic information using learning. The algorithm represents the characteristics of brain waves found in brain imaging techniques. We trained the new architecture to classify 10 class mnist input data. The classification performance is very lower than other existing deep neural network architecture. However, the new architecture shows the potential to connect from neuronal level to cognitive level. Keywords: deep neural network, Hogkin Huxely, single neuron model, cognitive model
Blockchain

A blockchain is a specific database stored in an electronic form. The databases stored in a block are put in a chain. When new data is added, it will be put in a new block. The blockchain may be created for storing different kind of information in which the most popular use of blockchain is ledger for transactions. Anything of value can be put in a blockchain, and this will reduce risk factors and cost. The blockchain is a chain of blocks used to store public databases. The blockchain can be a powerful tool in business applications for sharing and updating data. The blockchain may be used for the business process for handling transaction-related problems in an effective manner. The blockchain is also helpful in developing an ecosystem between various stakeholders. The policies, benefits, and cost are serious risk factors.
Estimating the Size of Web Apps Created Using the CakePHP Framework by Nonlinear Regression Models with Three Predictors

We have performed estimating the size (in KLOC, a thousand lines of code) of Web apps created using the CakePHP framework by nonlinear regression models with three predictors based on the normalizing transformations. We have used two univariate transformations (the decimal logarithm and the Box-Cox transformation) and the Box-Cox four-variate transformation. The nonlinear regression model constructed by the Box-Cox four-variate transformation, in comparison with other regression ones, has better size prediction results.