Reviewerly/exHarmony huggingface dataset:
BANK CREDIT ACCESSIBILITY AND PERFORMANCE OF SMES IN KWARA STATE, NIGERIA: A PLS-SEM ANALYSIS

This study examines the effect deposit money banks credit accessibility on SMEs performance in Kwara State, Nigeria. The population of the study consists of three hundred and eighty-two (382) respondents and one hundred and ninetyeight (198) were randomly selected as the sample size of the study. Data were drawn from the primary source to elicit responses from SME owners/managers. Descriptive statistics and Partial least square - Structural Equation Model (PLS-SEM) estimation techniques were employed to analyse the data collected. The study revealed that deposit money banks credit accessibility has a positive significant effect on SME performance (T=10.795, β=0.043) at 5% significance level and credit related charges (interest) also has a positive significant effect on SME performance (T=10.690, β =0.458). This implies that provision of finance by deposit money banks at relatively low cost play an important role in boosting the performance of SMEs. The study therefore concluded that SMEs in Kwara State are faced with the problem of access to finance as they are not fully benefiting from the credit facilities of the deposit money banks. The study therefore recommended that deposit money banks should put in place a more SME friendly credit administration system for SMEs to enable them access fund easily and affordably.
Sequential Feature Selection Using Hybridized Differential Evolution Algorithm and Haar Cascade for Object Detection Framework

Intelligent systems an aspect of artificial intelligence have been developed to improve satellite image interpretation with several foci on objectbased machine learning methods but lack an optimal feature selection technique.Existing techniques applied to satellite images for feature selection and object detection have been reported to be ineffective in detecting objects.In this paper, differential Evolution (DE) algorithm has been introduced as a technique for selecting and mapping features to Haarcascade machine learning classifier for optimal detection of satellite image was acquired, pre-processed and features engineering was carried out and mapped using adopted DE algorithm.The selected feature was trained using Haarcascade machine learning algorithm.The result shows that the proposed technique has performance Accuracy of 86.2%, sensitivity 89.7%, and Specificity 82.2% respectively.
A Study on the Influence of Strategic Human Resource Management in the Airline Industry on Job Satisfaction, Organizational Performance and Customer Orientation

Service industry requires more interaction between individuals and departments than other industries. The importance of corporate strategic human resource management is further emphasized as a key source of competitive advantage that generates management performance and ensures the permanence of the company. This study confirmed that among the strategic human resource management sub-elements, education training, career management and compensation management directly affect job satisfaction and job satisfaction affects organizational performance and customer orientation. The study found that strategic human resource management increases the job satisfaction of service employees and enables companies to improve service quality. Ultimately, not only customer satisfaction but also the company
Saliency Cuts: Salient Region Extraction based on Local Adaptive Thresholding for Image Information Recognition of the Visually Impaired

In recent years, there has been an increased scope for assistive software and technologies, which help the visually impaired to perceive and recognize natural scene images. In this article, we propose a novel saliency cuts approach using local adaptive thresholding to obtain four regions from a given saliency map. The saliency cuts approach is an effective tool for salient object detection. First, we produce four regions for image segmentation using a saliency map as an input image and applying an automatic threshold operation. Second, the four regions are used to initialize an iterative version of the Grab Cut algorithm and to produce a robust and high-quality binary mask with a full resolution. Lastly, based on the binary mask and extracted salient object, outer boundaries and internal edges are detected by Canny edge detection method. Extensive experiments demonstrate that the proposed method correctly detects and extracts the main contents of the image sequences for delivering visually salient information to the visually impaired people compared to the results of existing salient object segmentation algorithms
Recursive Expressions for SPARQL Property Paths

Regular expressions are used in SPARQL property paths to query RDF graphs. However, regular expressions can only define the most limited class of languages, called regular languages. Context-free languages are a wider class containing all regular languages. There are no context-free expressions to define them, so it is necessary to write grammars. We propose an extension of regular expressions, called recursive expressions, to support the definition of a subset of context-free languages. The goal of our work is therefore to provide simple operators allowing the definition of languages as close as possible to context-free languages.
An Efficient Cooperative Smearing Technique for Degraded Historical Document Image Segmentation

Segmentation is one of the critical steps in historical document image analysis systems that determines the quality of the search, understanding, recognition and interpretation processes. It allows isolating the objects to be considered and separating the regions of interest (paragraphs, lines, words and characters) from other entities (figures, graphs, tables, etc.). This stage follows the thresholding, which aims to improve the quality of the document and to extract its background from its foreground, also for detecting and correcting the skew that leads to redress the document. Here, a hybrid method is proposed in order to locate words and characters in both handwritten and printed documents. Numerical results prove the robustness and the high precision of our approach applied on old degraded document images over four common datasets, in which the pair (Recall, Precision) reaches approximately 97.7% and 97.9%.
Optimality of the pretty good measurement for port-based teleportation

Port-based teleportation (PBT) is a protocol in which Alice teleports an unknown quantum state to Bob using measurements on a shared entangled multipartite state called the port state and forward classical communication. In this paper, we give an explicit proof that the so-called pretty good measurement, or square-root measurement, is optimal for the PBT protocol with independent copies of maximally entangled states as the port state. We then show that the very same measurement remains optimal even when the port state is optimized to yield the best possible PBT protocol. Hence, there is one particular pretty good measurement achieving the optimal performance in both cases. The following well-known facts are key ingredients in the proofs of these results: (i) the natural symmetries of PBT, leading to a description in terms of representation-theoretic data; (ii) the operational equivalence of PBT with certain state discrimination problems, which allows us to employ duality of the associated semidefinite programs. Along the way, we rederive the representation-theoretic formulas for the performance of PBT protocols proved in Studziński et al. (Sci Rep 7(1):1–11, 2017) and Mozrzymas et al. (N J Phys 20(5):053006, 2018) using only standard techniques from the representation theory of the unitary and symmetric groups. Providing a simplified derivation of these beautiful formulas is one of the main goals of this paper.
An Efficient Content-Based Image Retrieval System Using kNN and Fuzzy Mathematical Algorithm

The implementation of content-based image retrieval (CBIR) mainly depends on two key technologies: image feature extraction and image feature matching. In this paper, we extract the color features based on Global Color Histogram (GCH) and texture features based on Gray Level Co-occurrence Matrix (GLCM). In order to obtain the effective and representative features of the image, we adopt the fuzzy mathematical algorithm in the process of color feature extraction and texture feature extraction respectively. And we combine the fuzzy color feature vector with the fuzzy texture feature vector to form the comprehensive fuzzy feature vector of the image according to a certain way. Image feature matching mainly depends on the similarity between two image feature vectors. In this paper, we propose a novel similarity measure method based on k-Nearest Neighbors (kNN) and fuzzy mathematical algorithm (SBkNNF). Finding out the k nearest neighborhood images of the query image from the image data set according to an appropriate similarity measure method. Using the k similarity values between the query image and its k neighborhood images to constitute the new k-dimensional fuzzy feature vector corresponding to the query image. And using the k similarity values between the retrieved image and the k neighborhood images of the query image to constitute the new k-dimensional fuzzy feature vector corresponding to the retrieved image. Calculating the similarity between the two kdimensional fuzzy feature vector according to a certain fuzzy similarity algorithm to measure the similarity between the query image and the retrieved image. Extensive experiments are carried out on three data sets: WANG data set, Corel-5k data set and Corel-10k data set. The experimental results show that the outperforming retrieval performance of our proposed CBIR system with the other CBIR systems.
Quantum Electronic Contract Scheme Based on Single Photon

An electronic contract is a contract signed by electronic means, which is widely used in electronic commerce activities. In recent years, with the rapid development of quantum cryptography technology, the quantum electronic contract has been widely studied by researchers. Supported by the basic principles of quantum mechanics, a quantum electronic contract scheme based on the single photon is proposed in this paper. In this scheme, two copies of the same contract are signed by both parties involved, and then a copy of each contract is sent to a trusted third party. The trusted third party verifies the signatures of both parties and compares the signed copies to determine whether the contract is valid. Compared with the previous scheme, this scheme is based on the quantum electronic contract signed by the single photon. Because the single photon is easy to prepare and operate, this scheme is simple and easy to implement. At the same time, the scheme does not need to exchange signatures between the two parties, which reduces the complexity of communication. Nevertheless, it requires both parties and the third party to be honest and trustworthy.
STUDY OF CYBERSICKNESS ON NON-IMMERSIVE VIRTUAL REALITY USING SMARTPHONE

Cybersickness is one of the psycho-physiological responses affecting human performances when it is interacting with Virtual Environment. Virtual Environment (VE) is an advanced technological model generated by computer which allows users feel the real-life presence like that in the real environment. However the repetitive exposure to VE causes sickness, especially the eyestrain when the eyes are forced to make an accommodation and focus on the virtual object. This symptom can be identified on the eye’s movement and the eyes muscle contraction. Thus, it is considered significant to evaluate the contraction of eye’s muscle. The objective of this study is to analyse the eyestrain in the constant use of smartphone for playing the war game. Empirical study was conducted to gather the relevant data. Ten subjects, who were familiar with the war game, were involved in this study. Electromyography (EMG) was used to record the signal of muscle contraction on lateral eyes muscle. Statistical analysis was conducted to test the hypothesis. The result of this study showed that there was a significant difference in visual acuity between normal brightness and 0% brightness and 100% brightness of smartphone while sitting in operating. There was also significant difference in visual acuity between the normal brightness 0%, 50%, and 100% of brightness depending on the lying down position. As for the higher contraction on lateral rectus muscle of the eyes occurred in lying down position than in sitting position at any level of brightness.
Customized Access Technology for Children using Head Movement Recognition

Children with cerebral palsy and complex communication needs face limitations in their access technology (AT) usage. Speech recognition software and conventional ATs (e.g., mechanical switches) can be insufficient for those with speech impairment and limited control of voluntary motion. Automatic recognition of head movements represents a promising pathway. Previous studies have shown the robustness of head pose estimation algorithms on adult participants, but further research is needed to use these methods with children. An algorithm for head movement recognition was implemented and evaluated on videos recorded in a naturalistic environment when children were playing a videogame. A face-tracking algorithm was used to detect the main facial landmarks. Head poses were then estimated using the Pose from Orthography and Scaling with Iterations (POSIT) algorithm and three head movements were classified through Hidden Markov Models (HMMs). Preliminary classification results obtained from the analysis of videos of five typically developing children showed an accuracy of up to 95.6% in predicting head movements.
An efficient concept generation approach to identifying most influential node in a Terrorist Network using Weighted Formal Concept Analysis

In recent years the Terrorist network research community has collected huge information for the appearance of composite and disparate communication patterns in complex terrorist social network. The identification of most centralized node(s) is crucial to understand the information flow and their communicable spread. Strategies for identifying influential nodes in complex terrorist network have been of interest. Techniques have been proposed from different perspectives, each with its own particular favourable circumstances and deficiency. Current study based on Formal Concept Analysis with Weight, under Terrorist Network Mining to identify the node(s) in a Terrorist Network, who influenced other members of the terrorist group. It may help to destabilize the terrorist network more effectively by removing or destroying the all communication link of higher ranking node(s). First we construct the adjacency matrix of 26/11 Mumbai Terrorist Attack network as the formal context. Next, we calculate the all possible concepts of the formal context. Subsequently the weight of every node within the terrorist network calculated and ranked each node accordingly. A comparison has been made of result with other well-known centrality algorithms like closeness centrality, node betweenness centrality, flow betweeness, PageRank, Katz, Reach centrality and PN centrality
AR-Based Visual Aids for sUAS Operations in Security Sensitive Areas

The use of UAVs has recently been proposed for services in civil airports and other sensitive areas. Besides specific regulations from authorities, the situation awareness of the UAV operator is a key aspect for a safe coexistence between manned and unmanned traffic. The operator must be provided in real-time with contextually relevant information, in order to take the proper actions promptly based on the notified contingency. Augmented reality can be adopted to superimpose such additional information on the real-world scene. After the definition of an architecture for the integration of drone operations in the airspace, in this paper the interface and the layout of an augmented reality application for the situation awareness of the pilot are designed and discussed.
Tunable Nonlinear Activation Functions for Optical Neural Networks

We introduce an electro-optic hardware platform for realizing optical nonlin earities and demonstrate that, as a nonlinear activation function, it can substantially improve the classification performance of optical neural networks.
Research on Emergency Response Processing Model of Thermal Power Enterprise Based on Epidemic Situation

As the population grows and the demand for social productivity increases, humanity's demand for energy is increasing day by day, and the emergency management of thermal power generation companies during the epidemic has become a significant research content to ensure the safety of social power supply and people's lives as well as property. Therefore, taking the power plant responding to the epidemic as an example for example analysis, the index system and evaluation model constructed in the previous chapter are adopted in the paper to conduct an emergency capability assessment during epidemic of the power plant with the assistance of relevant experts. Through analyzing and explaining the evaluation results, it is proved that the result is in line with reality.
A static multiple detector solar radiation sensor

A small static solar radiation sensor comprising of 17 photodiode detectors deployed evenly on a hemispherical-member is proposed. The sensor is capable of measuring the intensity and time dependent of the solar radiation components on any desired inclined and oriented plane, including the distribution of the diffuse radiation in the sky. The sensor may also be used in solar tracking systems for pointing to the highest radiation intensity at any time. An inverted sensor may also measure reflected radiation from different objects. As a first design, the accuracy obtained is about 10 to 15 percent, depending of the solar radiation intensity, and the aiming is to improve the accuracy to about 5 percent. The objective was to construct a static sensor with small dimensions and of light-weight, detectors and hemispherical-dome hermetically sealed and machined close to a prototype product.
An Experience with the Application of Three NLP Tools for the Analysis of Natural Language Requirements

We report on the experience made with three Natural Language Processing analysis tools, aimed to compare their performance in detecting ambiguity and under-specification in requirements documents, and to compare them with respect to other qualities like learnability, usability, and efficiency. Two industrial tools, Requirements Scout and QVscribe, and an academic one, QuARS, are compared.
A User Study of the Incremental Learning in NMT

In the translation industry, human experts usually supervise and post-edit machine translation hypotheses. Adaptive neural machine translation systems, able to incrementally update the underlying models under an online learning regime, have been proven to be useful to improve the efficiency of this workflow. However, this incremental adaptation is somewhat unstable, and it may lead to undesirable side effects. One of them is the sporadic appearance of made-up words, as a byproduct of an erroneous application of subword segmentation techniques. In this work, we extend previous studies on on-the-fly adaptation of neural machine translation systems. We perform a user study involving professional, experienced post-editors, delving deeper on the aforementioned problems. Results show that adaptive systems were able to learn how to generate the correct translation for task-specific terms, resulting in an improvement of the user’s productivity. We also observed a close similitude, in terms of morphology, between made-up words and the words that were expected.
The Frankfurt Latin Lexicon. From Morphological Expansion and Word Embeddings to SemioGraphs

In this article we present the Frankfurt Latin Lexicon (FLL), a lexical resource for Medieval Latin that is used both for the lemmatization of Latin texts and for the post-editing of lemmatizations. We describe recent advances in the development of lemmatizers and test them against the Capitularies corpus (comprising Frankish royal edicts, mid-6th to mid-9th century), a corpus created as a reference for processing Medieval Latin. We also consider the post-correction of lemmatizations using a limited crowdsourcing process aimed at continuous review and updating of the FLL. Starting from the texts resulting from this lemmatization process, we describe the extension of the FLL by means of word embeddings, whose interactive traversing by means of SemioGraphs completes the digitally enhanced hermeneutic circle. In this way, the article argues for a more comprehensive understanding of lemmatization, encompassing classical machine learning as well as intellectual post-corrections and, in particular, human computation in the form of interpretation processes based on graph representations of the underlying lexical resources.
SLK-NER: Exploiting Second-order Lexicon Knowledge for Chinese NER

Although character-based models using lexicon have achieved promising results for Chinese named entity recognition (NER) task, some lexical words would introduce erroneous information due to wrongly matched words. Existing researches proposed many strategies to integrate lexicon knowledge. However, they performed with simple first-order lexicon knowledge, which provided insufficient word information and still faced the challenge of matched word boundary conflicts; or explored the lexicon knowledge with graph where higher-order information introducing negative words may disturb the identification. To alleviate the above limitations, we present new insight into second-order lexicon knowledge (SLK) of each character in the sentence to provide more lexical word information including semantic and word boundary features. Based on these, we propose a SLK-based model with a novel strategy to integrate the above lexicon knowledge. The proposed model can exploit more discernible lexical words information with the help of global context. Experimental results on three public datasets demonstrate the validity of SLK. The proposed model achieves more excellent performance than the state-of-the-art comparison methods.
Four-directional quantum controlled teleportation using a single quantum resource

In this paper, a quantum teleportation protocol has been proposed that can simultaneously transmit quantum states in four directions using a single entangled channel. This means that there are four senders who want to transmit state information, and they are Alice, Bob, Charlie, and David. In order to ensure the security of information transmission, the information state of the four is transmitted to four receivers Fancy1, Fancy2, Fancy3, and Fancy4 under the control of the controller Elle. Through unitary transformation and entanglement property, receivers can recover the original quantum state from the four senders, which is easy to implement. The teleportation protocol is perfect.
Rancang Bangun Aplikasi E-Commerce Untuk Meningkatkan Pendapatan UMKM

The development of information technology is currently causing increase fierce competition between industries. The competition has an impact on the development of the micro small and medium business sector (MSME). The MSMEs sector are often less aware on the importance of information technology in this case the use of online trading system (e-commerce) which used to increase the scope of product sales and can speed up existing business processes. Especially MSMEs in Malang District are limited to self-taught sales without using information technology support. This has an impact on the products sold that are not widely known by the public in general which causes MSME revenues to tend to be low. Therefore, in this study trying to design an e-commerce application that aims to increase the income of Micro Small Medium Enterprises in Malang Districts.