Reviewerly/exHarmony huggingface dataset:
Foldscope Image Enhancement in HSV Space by PSO Optimization Technique

This work focuses on detail and contrast enhancement of microscopic images generated by Foldscope and captured by simple smart phone. Being light-weight, portable and affordable, the Foldscope can be used in place of traditional microscope which is bulky, expensive and complex. Indeed, the Foldscope generated images are of low contrast and illumination which provide less cell details than the traditional microscopic images. Therefore, it is significant to enhance Foldscope image quality using advanced image processing techniques. In this direction, an image enhancement technique using LoG filter and linear contrast stretching method has been proposed. It enhances an image in HSV space by separating the V-Component into high and low frequencies. Further, better enhancement has been achieved by employing population-based optimization (PSO) technique. For experimental validation, in total, 10 Foldscope images have been considered. The quality of the enhanced images has been examined based on Entropy, PSNR and SSIM scores. Furthermore, a comparative study with traditional GHE and CLAHE technique shows that the proposed method is superior. Such results reveal that the proposed method can be used for low quality image enhancement.
Factors of the Variability of sociocultural Dynamics in the Context of Intercultural Communication

The article analyses the main factors determining the socio-cultural dynamics of nowadays and affects the processes of intercultural communication . The key factor is the fourth industrial revolution being the convergence of the digital, physical and biological worlds, urbanisation and the widespread introduction of information and communication technologies . The author noted that the development of new technologies generates “digital inequality” . The widespread of the Internet the author considers not only as a means of free communication but also as a new paradigm of consciousness — as a means of digitalisation of cognitive processes . The author also noted that widespread digitalisation generates new forms of labour and social relations . It contributes to the leaching of the middle class . It has been revealed that a significant factor in the sociocultural dynamics of nowadays is super mobility and reactivity, what generate instability, uncertainty about the future, as well as elimination and deformation of traditional value paradigms . The author concluded that it is necessary to build new models of intercultural communication, as well as to consolidate efforts at all levels — from governments and international organisations to public organisations, all civil society and individuals . There is need for constructive cooperation to create not only an effective system of international law but also of the formation of voluntarily accepted ethical norms and mechanisms, established on an equal level by equal participants — a constant, regular dialogue and cooperation at the local, national or supranational level .
Test Evaluation of Emergency Communication Network Planning Based on Intelligence

At present, Tactical Internet has become an important part of the new generation mobile communication support system in the battlefield of our army. Network planning is the core link of the organization and application of Tactical Internet equipment. It is not only an important guarantee to achieve efficient and smooth command ,but also an urgent problem to be solved when mobile communication equipment is equipped with troops. Therefore, paper attempts to introduce artificial intelligence technology to design a scientific and reasonable experimental evaluation method. Developing the test appraisal and evaluation activities based on intelligent Emergency Communication Network planning and an attempt is made to explore the technical approach of artificial intelligence test appraisal and evaluation under the current military intelligence prospects.
Analysis of Factor Based Data Mining Techniques to Determine Agripreneurial Skills

Shifting behavior of farmers from agriculture to other fields is alarming. The study undertaken is focused on inculcating agripreneurial skills for better farming environment. For this, the major factors affecting farming were identified. Farmers residing in rural areas have been approached by a team of management graduates based on convenience and judgment. Initiatives taken by central and state government from time to time in this context were also taken into consideration. Factor Analysis, a regression based data mining technique, has been applied to find major dimensions causing farming distress. Based on these dimensions, innovative ways to manage agripreneurship have been analysed. Besides this, other areas such as farm tourism have also been explored. An attempt has been made to motivate youth and women in family of farmers to provide support in this initiative.
Sidecar: Augmenting Word Embedding Models with Expert Knowledge

This work investigates a method for enriching pre-trained word embeddings with domain-specific information using a small, custom word embedding. For a classification task on text containing out-of-vocabulary expert jargon, this new approach improves the prediction accuracy when using popular models such as Word2Vec (71.5% to 76.6%), GloVe (73.5% to 77.2%), and fastText (75.8% to 79.6%). Furthermore, an analysis of the approach demonstrates that expert knowledge is improved in terms of discrimination and inconsistency. Another advantage of this word embedding augmentation technique is that it is computationally inexpensive and leverages the general syntactic information encoded in large pre-trained word embeddings.
Robust Variants of Dictionary Learning Exploiting M-Estimators

We propose a robust alternative the well known dictionary learning technique K-SVD. Specifically, we exploit the theory behind M-Estimators to incorporate robustness into the sparse coding stage of K-SVD, and hence, decrease the estimation bias that might be introduced when outliers are present. Five different M-Estimators are introduced alongside their optimal hyperparameters in order to avoid parameter tuning by the user. In this way, the proposed framework has the same number of free parameters as K-SVD with the added feature of robustness and improved performance in non-Gaussian environments. We thoroughly demonstrate the superiority of the proposed algorithms via recovery of generating dictionaries for synthetic data and image denoising under two types of non-homogenous noise-salt and pepper noise, and impulsive noise.
Automatic Recovery of Unit Tests after Code Refactoring

Unit testing allows developers to refactor their code confidently, these tests act as a safety net against producing bugs and provide immediate feedback during the refactoring process and furthermore help developers overcome the fear of change. When performing refactoring, the design of code is changed or restructured according to a predefined plan, after refactoring is applied, the alignment between source code and its corresponding unit tests could be broken which creates a problem that needs to be solved.This paper introduces an approach in which code refactoring can maintain the integrity of the previous unit tests; the tool called GreenRef demonstrates this work. This tool supports an automatic recovery for the unit tests after performing of three particular refactoring types for Java programming language: Rename Method, Add Parameter and Remove Parameter.The achieved results indicate that GreenRef facilitates consistent use of refactoring and unit tests, and save about 43% of the time required to recover broken unit tests manually.
Modeling Missing Data Based on Neural Fuzzy Inference for Implicit Recommendation

As implicit feedback can be tracked automatically and is easy to collect, the implicit recommendation attracts more researcher attentions. However, the uncertainty of the implicit feedback meaning poses a great challenge to the implicit recommendation. Especially for the missing data, we are not sure whether the users dislike or just have not seen the items. It may lead to bias of predictions. In this paper, we propose Neural Fuzzy Inference based on User preference and Item popularity (UI-NFI) algorithm to model the missing data in implicit recommendation. First, we use fuzzy set theory to represent user preference and item popularity that get from the history interactions and side information. Furthermore, neural fuzzy inference is proposed to predict the exposure possibility of missing data. Based on the fuzzy inference model, UI-NFI and matrix factorization model perform joint learning to predict. Experimental results show that our model has better performance compared to the other implicit recommendation algorithms.
Facial Image Classification Using Rotation, Illumination, Scale and Expression Invariant Dense Features and ENN

Face Recognition is immensely proliferating as a research area in the paradigm of Computer Vision as it provides an extensive choice of applications in surveillance and commercial domains. This paper throws light upon the comparison of various dense feature descriptors (Dense SURF, Dense SIFT, Dense ORB) with each other and also with their classical counterparts (SURF, SIFT, ORB) using a novel technique for recognition. This proposed technique uses Laplacian of Gaussian filter for enhancement of the image. It applies various dense and classical feature descriptors on the enhanced image and outputs a feature vector. In order to achieve high performance, this feature vector is given to Fisher vector since Fisher Vector is a feature patch-aggregation method. Finally, extended nearest neighbor Classifier is used for classification over the orthodox k-nearest classifier. Experiments were carried out on three diverse datasets—ORL, Faces94, and Grimace. On scrutinizing the results, Dense SIFT and Dense ORB were found to be preeminent as measured by various performance metrics. 98.44 on Grimace, 98.15 on Faces94.
Real-Time Rehabilitation and Fitness System using Depth Sensor

This paper presents an action verification system for rehabilitation of patients with limited mobility. In this regard, a kinect sensor is used to capture the image-sequences of patients for identification of different actions in real time during rehabilitation exercisers at home. Then, a set of most discriminating features is extracted from a human skeleton. These features are fed to the classifier for action recognition. In order to evaluate the performance of the proposed system, we build a data set for patients with frozen shoulders and Overhead shoulder issues. The results confirm an excellent performance of the proposed system. In this paper, our contribution is three fold. 1) we have constructed a data set of overhead shoulder exercise performed by 8 different actors. 2) A novel set of features has been proposed for verification of these two exercises in real time. 3) we have employed 5 different classifiers for classification and presented a comparative analysis.
Path Planning of a Networked Robot Using Camera Feedback to Minimize Computational Time

Networked Control System (NCS) is a feedback control system with control loops closed via a communication network. Now-a-days, NCS is being used to simplify various tasks in space exploration, aviation, transportation, disaster management and healthcare sector etc. NCS provides us the greater autonomy of remote monitoring in geographically distributed mobile robots. In the field of robotics, NCS facilitated robots are being used to execute various challenging tasks. In this paper various camera monitored optimum path planning for Networked Control Robot have been presented. It has been observed that performance of these tasks highly depends upon the computational time taken by the controller. The computational time is the time required by a controller to execute the path planning of the robot from the images captured by camera-sensors. Therefore, in order to minimize total end-to-end delay between camera-sensor and moving robot, the computational time taken by the controller at remote end must be as minimum as possible for the real-time performance. The simulations of various image processing approaches have been performed using Matlab 2019a. In order to decide a suitable path planning approach, experimental results have been illustrated and discussed in Section III.
Online Communication with Natural Language

Nowadays all the University and Organization have a dedicated website of their own. The most challenging part in maintaining the website is to effectively handle the user doubts and queries. Often, users wish to interact with the authoritative person. Answering every customer is tiresome and leads to loss of time and human resource. It is impossible to handle lot of queries simultaneously. In order to make the work of the organization simple, Chatbots can be used. A Chatbot simulates the conversation with user in mobile applications, web applications and messaging applications. The proposed system will be able to read a piece of text, that is being asked as a question by the user and it will provide the appropriate response by text categorization and keyword matching. The screenshots illustrate the successful implementation of chatbot application.
Computer Technology of Pedagogical Monitoring of Foreign Language Training Based on Modular Approach

This chapter presents the structure and content of foreign language communicative competence in the form of a set of competencies (linguistic; speech; sociocultural; strategic; self-education), each of which includes invariant and variable elements. Pedagogical monitoring of foreign language training is considered as a specially organized continuous process of selective monitoring to develop a control action based on the analysis of the results that is multifunctional, and implements information, analytical, diagnostic, monitoring, and corrective functions. The authors' computer program implements informational, analytical, diagnostic, and monitoring functions, and opens up fundamentally new opportunities for testing to assess the success of the development of language disciplines. In the future, the functionality of the authors' patented computer program can be expanded through the development of an intelligent core, which will make it possible to effectively use computer technology for monitoring, evaluation, and forecasting in the process of foreign language training.
Efficient Scalable Three Operand Multiplier Over GF(2^m) Based on Novel Decomposition Strategy

It is expected that efficient scalable three operand multiplier (STOM) over GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ) (polynomial basis) generally can provide quite a number of superior benefits such as low-complexity and flexibility on processing bits and thus is very ideal to many applications like elliptic curve cryptography and pairing cryptography. The actual efficient hardware implementation of STOM, however, is still not covered in the literature. Based on this consideration, in this paper, we propose a novel decomposition strategy based design scheme to obtain efficient STOM on hardware platforms. First of all, a novel decomposition strategy (summarized as Toeplitz Matrix Oriented Karatsuba Algorithm, TMOKA) based STOM is presented with detailed mathematical derivation. Then, the proposed STOM structure is introduced along with a number of optimization techniques. Finally, the complexity analysis and comparison have been given to confirm the efficiency of the proposed STOM, e.g., the proposed structure with scalable digit-size of 64 has at least 50.8% less area-delay product (ADP) than the TOM employing a newly reported finite field multiplier ([8]) on the FPGA platform. The proposed STOM can thus be extended and employed in many cryptographic applications.
General Characteristics of the Financial Markets in the People’s Republic of China

In this article the author presents the main characteristics of the economy of the People’s Republic of China. First of all, the Chinese government has a prudent policy of gradual opening and liberalisation of the economy. However, significant impact on economic development and the state of international settlements in the Asian region is influenced by currency and monetary policy. Therefore, for Chinese state authorities maintenance of stable national currency becomes especially important. In the early 1980s, the government began opening the banking system and the four state-owned specialised banks to accept deposits and perform banking activities. Also, the author analysed development of financial markets of the People’s Republic of China. Finally, the author discussed the emerging markets of bitcoin and cryptocurrencies in China.
The Impact of Transitive Class Relations on Measuring the Degree of Class Cohesion

Measuring software quality attributes helps in determining the degree of the quality of the software system. Among the various software attributes, cohesion is considered one of the most important software design concerns. In this study, the focus was on investigating the impact of transitive or indirect relations between classes on measuring cohesion. It was assumed that software metrics which take into account transitive relations would provide cohesion values equal or greater than metrics which consider direct relations. Four metrics are covered in this study, including: TCC (Tight Class Cohesion), LCC (Loose Class Cohesion), LCC-D (Lack of Class Cohesion - Direct), and LCC-I (Lack of Class Cohesion-Indirect). Several programs in C# were selected and a tool was developed for calculating these metrics and their values were compared and correlated. The findings of the study show that the assumptions of this study were not valid for certain cases. The values of TCC and LCC were identical in most cases and so is the case the values of LCC-D and LCC-I. Nevertheless, both LCC-D and LCC-I which take into account relations based on method invocations besides instance variable-sharing show higher values than those of TCC and LCC.
Research on Modeling Method of Cooperative Lifting of Multiple Crawler Cranes

Cooperative lifting of multiple crawler cranes is researched. Based on the modeling technology of single crane and double cranes, the multi-closed chain among the lifted object and cranes is analyzed, and the corresponding mathematical model of multi-crane system is established. It is a statically indeterminate problem because there are more than 3 cranes to lift one object. So, deformation condition is introduced to change this problem into a statically determinate problem. The method is proposed to obtain the load of each crane according to deformation condition. By case analysis and comparison of results, this proposed method is verified to have good applicability for mechanics analysis of multi-crane lifting system when the lifted object has strong rigidity.
Ontology-based materialized views for reverse engineering: Toward a set of conceptual guidelines

Data reuse and metadata handling remain tricky problem for both the designers and managers, especially when schemas reverse engineering is on demand and that some data are stored in materialized views. In this paper, we tackle such problem by using ontology-based meta-materialized views. Indeed, ontologies, which are semantics-based, ensure the stability of the underlying schemas of the data repositories and to ease the overall access and processing of the data and metadata. Our proposal is sustained by a set of conceptual guidelines and outlined through a case study example.
New Bounds for GLD Lattices and Codes

We prove that the ensemble of random Generalized Low-Density (GLD) lattices can attain the Poltyrev limit for an alphabet size increasing polylogarithmically with the lattice dimension. Our main theorem imposes no constraints on the normalized minimum distance of the code associated to the lattice ensemble, any asymptotically good code is suitable. This is a great improvement with respect to the first theorem on Poltyrev goodness of GLD lattices (2015). Our new bound is based on a new method referred to as the buckets approach where we employ the asymptotics of the restricted compositions of the Hamming weight. The new bound has applications in many coding areas beyond the specific lattice ensemble considered in this paper.
Enhanced Face Recognition System Using Age Invariant Features

Automatic face recognition system poses a various difficult problem using different age faces. Mostly face recognitions have addressed variation in ages problems like age simulation or age estimation. Age invariant is a complex procedure that influences both face texture and the shape of face. In automatic face recognition system these texture and shape changes degrade the performance. The problem is designing an effective matching framework of face recognition system using different images of aging. In this paper to develop a model for automatic age invariant face recognition model using facial features. In the model, extract the facial features like eye, mouth and nose in the given image based on the illumination method. Once the eye, nose, and mouth are detected, need for aligning the face. The face alignment depends on the angle between the eye and mouth coordinates. Once the face alignment completed, calculate the eigenvalue and eigenvectors of co-variance matrix. Then find difference between all features to get a difference vector. The eigenfaces calculates the eigenvalues and eigenvectors. Compare these distance vectors for all faces to get a most match value or minimum mismatch value. Give the face with the most matches or minimum mismatch value as the output. The results of various experiments shows that this method outperforms the usage of the face aging data sets of IFDA. A fusion of age invariant face recognition using facial features model further improves the accuracy of the face matching in the presence of aging.
An Efficient Approach for Image Resolution Enhancement Using Multi Step Magnification Method

The low-resolution image is viewed as down sampled version of a high-resolution image, whose patches are assumed to have a sparse representation with respect to an over-complete dictionary of prototype signal-atoms. The principle of compressed sensing ensures that under mild conditions, the sparse representation can be correctly recovered from the down sampled signal. We will demonstrate the effectiveness of sparsity as a prior for regularizing the otherwise ill-posed super-resolution problem. We further show that a small set of randomly chosen raw patches from training images of similar statistical nature to the input image generally serve as a good dictionary, in the sense that the computed representation is sparse and the recovered high resolution image is competitive or even superior in quality to images produced by other SR methods.