Reviewerly/exHarmony huggingface dataset:
Development of an image segmentation model based on a convolutional neural network

This paper has considered a model of image segmentation using convolutional neural networks and studied the process efficiency based on models involving training the deep layers of convolutional neural networks. There are objective difficulties associated with determining the optimal characteristics of neural networks, so there is an issue related to retraining the neural network. Eliminating retraining by determining the optimal number of epochs only would not suffice since it does not provide high accuracy. The requirements for the set of images for training and model verification were defined. These requirements are best met by the image sets PASCAL VOC (United Kingdom) and NVIDIA-Aerial Drone (USA). It has been established that AlexNet (Canada) is a trained model and could perform image segmentation while object recognition reliability is insufficient. Therefore, there is a need to improve the efficiency of image segmentation. It is advisable to use the AlexNet architecture to build a specialized model, which, by changing the parameters and retraining some layers, would allow for a better process of image segmentation. Five models have been trained using the following parameters: learning speed, the number of epochs, optimization algorithm, the type of learning speed change, a gamma coefficient, a pre-trained model. A convolutional neural network has been developed to improve the accuracy and efficiency of image segmentation. Optimal neural network training parameters have been determined: learning speed is 0.0001, the number of epochs is 50, a gamma coefficient is 0.1, etc. An increase in accuracy by 3 % was achieved, which makes it possible to assert the correctness of the choice of the architecture for the developed network and the selection of parameters. That allows this network to be used for practical tasks related to image segmentation, in particular for devices with limited computing resources
The Effects of the Covid-19 Pandemic on Kuwaiti Kindergarten Staff to Adopt E-Learning System

E-learning has been implemented on a mass scale during the COVID-19 crisis although many schools faced huge problems during the system implementation and roll-out. This paper has used the technology acceptance model (TAM) to investigate the teachers and administrative staff’s prospective on the use of e-learning in the education process. The study has implemented a quantitative method to collect data through a questionnaire. The study had 204 participants which included teachers and administrative staff in kindergartens under the Kuwaiti Ministry of Education (MOE). The study showed that the majority of the participants were not in favor of e-learning as the system was neither clear nor it was understandable or flexible, which hindered teachers work. The Ministry of educations strategy to bring e-learning to the mass users was undermined due to unclear strategic objective and lack of proper training and support that stood as a barrier to users’ acceptance of e-learning.
General Purpose Image Tampering Detection using Convolutional Neural Network and Local Optimal Oriented Pattern (LOOP)

Digital image tampering detection has been an active area of research in recent times due to the ease with which digital image can be modified to convey false or misleading information. To address this problem, several studies have proposed forensics algorithms for digital image tampering detection. While these approaches have shown remarkable improvement, most of them only focused on detecting a specific type of image tampering. The limitation of these approaches is that new forensic method must be designed for each new manipulation approach that is developed. Consequently, there is a need to develop methods capable of detecting multiple tampering operations. In this paper, we proposed a novel general purpose image tampering scheme based on CNNs and Local Optimal Oriented Pattern (LOOP) which is capable of detecting five types of image tampering in both binary and multiclass scenarios. Unlike the existing deep learning techniques which used constrained pre-processing layers to suppress the effect of image content in order to capture image tampering traces, our method uses LOOP features, which can effectively subdue the effect image content, thus, allowing the proposed CNNs to capture the needed features to distinguish among different types of image tampering. Through a number of detailed experiments, our results demonstrate that the proposed general purpose image tampering method can achieve high detection accuracies in individual and multiclass image tampering detections respectively and a comparative analysis of our results with the existing state of the arts reveals that the proposed model is more robust than most of the exiting methods.
Several Methods to Teach Reading to Early Childhood

One of the challenges now faced by the parents of young children, especially at the early childhood level of education is to teach reading. This paper aims to find out some more effective ways to teach reading to the children. The method discussed in this paper include: using children’s songs and books, using game card , through guessing game, using variety of study applications, and the parent’s patience. The results of the study show that in order to teach reading to early childhood, the parent or teacher must be able to choose a method that suits the child's situation, abilities and interests. Thus, it will able to increase children's ability and interest in reading and avoid boredom.
Competency Model Of A Teacher Of Professional Education: The Need For And Conditions Of Development

This article provides information on the content, necessity and conditions of development of the model of competencies of teachers of vocational education institutions.
School Of The Future: View Of The "Digital Natives" Of The Present

The purpose of the study was to identify ideas of adolescents of 14-15 years old /age about the likelihood of school transformations in the future.It was suggested that these ideas would be based more on knowledge and personal experience of interacting with new technologies than on fiction images.The theoretical part of the study provides a brief analysis of the works of literature, cinema and animation, which present the image of the school in the past, present and future.In a few works containing the image of the school of the future, it contains the main features of a traditional school.To test this assumption, the author developed a methodology containing 8 categories characterizing the traditional school and 6 probable options for changes for each of the categories; the survey participant should evaluate the likelihood of a specific change in the future.Adolescents of 14-15 years old /age of an average Russian city showed that the image of school in its substantive and organizational foundations is little changed: most of them do not support the idea of no lessons, homework, marks, discipline and communication with classmates and teachers, but admit their technological change.Most assessments of future school changes are based on the experience of schoolchildren or their ideas about the use of new technologies in education and everyday life.The options for changing the school in the future in the understanding of participants are diverse, they can be described as quite realistic.
Semi-Supervised Temporal Segmentation of Manufacturing Work Video by Automatically Building a Hierarchical Tree of Category Labels

Nowadays, many industrial companies visually record workers' activities for the purposes of streamlining their work processes. However, since untrimmed raw videos are hard to use, it is desired to automatically divide the videos into segments and recognize which kind of operation is performed on each segment. This task is called temporal video segmentation. We propose a method for achieving it, particularly targeting videos of manufacturing work with a specialized vehicle such as a hydraulic excavator. To make the performance of temporal video segmentation high, it is quite essential to extract good visual features from input videos. This can be hardly achieved by unsupervised methods, whereas supervised methods have another drawback that collecting a sufficient amount of training data is difficult due to its labor-intensiveness. To overcome these drawbacks, the proposed method employs a semi-supervised approach. We assume that a set of weakly-labeled videos whose frames only sparsely have a category label are given as input, where the labeled frames are used as training data to train a desirable feature extractor. Under this assumption, the proposed method first divides the input videos into short segments called primitive segments having the fixed length and then clusters them using visual features extracted by the above feature extractor. To achieve higher performance, we also use a hierarchical tree of the category labels and recursively perform the above process at each branch in the tree, where the tree is automatically built by the proposed method. In our experiments, we achieved a segmentation performance of 0.947 on the F-measure, even when only 1.25% of all the frames in the input videos are labeled.
An Improved NID Steganography Method for Low Bitrate Speech on VoIP

In the procedure of encoding process on low bitrate speech, fixed codebook division is an efficient and promising embedding method for steganography. An improved neighbor index division (NID) steganography method based on the high bitrate frame of G.723.1 codec (6.3kbit/s) is proposed, which employs the parity and low distortion of neighbor indices for G.723.1 fixed codebooks. Differing from previously NID method which performs quantized index modulation (QIM) beforehand, the proposed method divides codeword indices into separate sub-codebooks according to the secret message bits dynamically in the original G.723.1 codec quantization period. Compared with existing NID method, our proposed method doesn’t need to divide the codebook before the encoding starts. The embedding and codebook dividing happen simultaneously, which utilizes the characteristics of specific secret message bits. The experiment results show that the proposed method has a much lower quality degradation for the decoding speech and still fulfills the low latency requirement for communication.
Detection of fraud in financial statements using Beneish ratios for companies listed at Nairobi Securities Exchange

Purpose: To determine and analyze financial statement fraud amongst Nairobi securities exchange listed companies.Methodology: The research design that was used in this study was descriptive research design in collecting the data from the respondents. Descriptive research is a fact-finding approach generating a cross sectional study of the present situation. The population of this study will consist of all firms listed at the NSE. There are 62 companies listed at the NSE and this study will seek to determine earnings manipulation and fraudulent financial reports using the Beneish M- Score for these companies. The study will adopt a census for the companies listed at the NSE as at 31st December 2018Findings: The study found out that the 8 varibales used by beneish capture all possible pain points for earnings managers. Thus by evaluating them and subjecting them to the formula a reliable Z -score of earnings manipulation existence is developed, also named M-score by its developer Messod Beneish. The probability is thus obtained  from the Z-score tables. The 8 Variables  studied are generally classified as  manipulation signals  and motivation signals. Manipulation signals as stated by Mcleavy (2013) are the days sales in receivables index DSRI ,asset quality index AQI ,depreciation index, DEPI  and total accruals to total assets ,TATA. Whereas motivational signals are gross margin index  GMI, sales growth index SGI, selling, general, and administrative index SGA and leverage index LEVI. From  the M-scores ,the companies that were likely manipulators all had peculiarities in their financials as measured by the variables at more than one year over the period of study.Implications: The M-score cannot identify with full certainty whether a firm is a manipulator however at the -2.22 cut-off ,read from the cumulative normal table this z-score gives a probability of 0.0132, a firm has a probability of 0.986 to be classified innocent. 74 times the chances of being labeled a manipulator .Value: The findings could be of use to policymakers as well as academics as a literature source as they seek to build watertight methods of discovery. Furthermore, policy-making institutions and accounting regulators such as Institute of Certified Public Accountants (ICPAK) can also benefit from the findings of the study by determining whether to come up with the decision of developing more rules to advance the quality of financial information reported by companies. Other intended beneficiaries are institutional and retail investors while analyzing the financial performance of a firm should holistically consider both financial, non-financial performance and the cash position specifically training their antennae to detect fraud presented as truth.
Elaborating the Characteristics that Affect Buyers in Online Shopping: The Case of Generation Z Girls in Kurdistan Region of Iraq

Online shopping is one of the most growing means of purchasing daily needs. The purpose of this research is to find the main factors affecting young girls in Sulaymaniyah city in Iraq to buy their needs online. The purpose of this research is to understand the factors that affect young girls in Sulaymaniyah city in their online purchasing activities. The sample size taking for this study is 100 young girls using convenience sampling. A survey was done to get the participants' responses about the issue and SPSS software is used for data analysis, thus; this research utilizes quantitative data to obtain the results. Findings of this research shows the main factors affecting young girls in their online shopping are the unavailability of certain products in the shops, and the availability of the product review online. Another finding of the study is showing something quite different from other studies. Even though most of the participants are unemployed, many of them are having online shopping at least once a month. Thus, marketing managers and supervisors can profit from the findings of this study in their online marketing strategies.
Digital English teaching resource sharing system based on logical database

The traditional English teaching resource sharing system has problems of low distributed computing performance and low data steady-state transmission rate. This paper proposes a design method of digital English teaching resource sharing system based on logical database. The hardware part of the system constructs the logical database of the system, designs the business layer of the logical database in the system, and reads and writes the resources. Establish a database resource search engine, reduce the calculation process and realise the effective use of digital English teaching resources and course recommendation. Using multi-type system development software, the system file configuration and service address are developed to complete the system design. The experimental results show that compared with the traditional teaching resource sharing system, the distributed computing performance of the designed system is improved by 37%, and the data steady-state transmission rate is increased by 29%, which has distinct advantages.
Attention Capsule Network for Aspect-Level Sentiment Classification

As a fine-grained classification problem, aspect-level sentiment classification predicts the sentiment polarity for different aspects in context.To address this issue, researchers have widely used attention mechanisms to abstract the relationship between context and aspects.Still, it is difficult to effectively obtain a more profound semantic representation, and the strong correlation between local context features and the aspect-based sentiment is rarely considered.In this paper, a hybrid attention capsule network for aspect-level sentiment classification (ABASCap) was proposed.In this model, the multi-head self-attention was improved, and a context mask mechanism based on adjustable context window was proposed, so as to effectively obtain the internal association between aspects and context.Moreover, the dynamic routing algorithm and activation function in capsule network were optimized to meet the task requirements.Finally, sufficient experiments were conducted on three benchmark datasets in different domains.Compared with other baseline models, ABASCap achieved better classification results, and outperformed the state-of-the-art methods in this task after incorporating pre-training BERT.
IMAGE TEXT EXTRACTION AND ITS LANGUAGE TRANSLATION

Nowadays paperless offices and digitizing document is becoming ordinary for every kind of business or work.It is a good idea to find an easy way to create, store, and protect important documents.Document scanning can be the way Unlike the traditional manual method of creating and preserving document which comes with many benefits such as more office space, information storing, sharing, better data security and recovery.In This paper we have proposed about document scanning in terms of a software interface i.e. web application that does an automated digitization of document with various features such as image enhancement, perspective view, text recognition and translation.Also, we have discussed about various Python scripts and web frameworks used for achieving optimal document scanning.
Addressing Cyber-Attacks on Agriculture Critical Infrastructure: Could the Defense Production Act be Used to Take Over Private Cybersecurity Operations

This paper addresses a potential legal avenue using the Defense Production Act so the federal government may assist and/or take over private cyber security operations when there is intelligence of an imminent cyber attack on private, critical infrastructure. The hypothetical centers on an attack which would cripple or severely negatively affect our economy and national security. The paper is narrowly focused on agriculture critical infrastructure, but can also be applied to any area of critical infrastructure.
Semantic integration of traditional and heterogeneous data sources (UML, XML and RDB) in OWL2 triplestore

With the success of the internet and the expansion of the amount of data in the web, the exchange of information from various heterogeneous and classical data sources becomes a critical need. In this context, researchers must propose integration solutions that allow applications to simultaneously access several data sources. In this perspective, we propose a semi-automatic integration approach of classical data sources via a global schema located in database management systems of RDF or OWL data, called triplestore. Our contribution is subdivided into three axes: 1) an automatic mapping solution that converts classical data sources such as UML, XML and RDB to local ontologies based on OWL2 language; 2) an alignment system of local ontologies based on syntactic, semantic and structural similarity measurement techniques in order to increase the probability of having real correspondences and real differences; 3) a fusion system of pre-existing local ontologies into a global ontology based on the alignment found in the previous step.
The Effect of Hidden Words Game on the EFL Students' Vocabulary Mastery

Teaching vocabulary to EFL students in this era can be a challenging task regarding their characteristic as the millenial generation. That’s why, the proper choice of media to teach is needed that will enable EFL students to learn vocabulary effectively. This study aims to find out if hidden words game application can increase English vocabulary mastery of the EFL students. The design of the research was quasi experimental using a single group model of pre-test and post-test. The population of the study was the seventh grade students of SMPN 2 Kudus with class D as the sample consisting of 32 students. The instrument of the research was multiple choice test. The research finding revealed that hidden words game application can increase the English vocabulary mastery of seventh graders.
Application of the Level-2 Quantum Lasserre Hierarchy in Quantum Approximation Algorithms.

The Lasserre Hierarchy is a set of semidefinite programs which yield increasingly tight bounds on optimal solutions to many NP-hard optimization problems. The hierarchy is parameterized by levels, with a higher level corresponding to a more accurate relaxation. High level programs have proven to be invaluable components of approximation algorithms for many NP-hard optimization problems. There is a natural analogous quantum hierarchy, which is also parameterized by level and provides a relaxation of many (QMA-hard) quantum problems of interest. In contrast to the classical case, however, there is only one approximation algorithm which makes use of higher levels of the hierarchy. Here we provide the first ever use of the level-$2$ hierarchy in an approximation algorithm for a particular QMA-complete problem, so-called Quantum Max Cut. We obtain modest improvements on state-of-the-art approximation factors for this problem, as well as demonstrate that the level-$2$ hierarchy satisfies many physically-motivated constraints that the level-$1$ does not satisfy. Indeed, this observation is at the heart of our analysis and indicates that higher levels of the quantum Lasserre Hierarchy may be very useful tools in the design of approximation algorithms for QMA-complete problems.
SANTé: A Light-Weight End-to-End Semantic Search Framework for RDF Data

Natural language interfaces are one of the most powerful technologies to enable content access. It is a diverse and thriving topic that tackles a multitude of challenges ranging from designing better ranking models to user interfaces. Developing or adapting search engines is a very time-demanding and resource-consuming task. We present SANTé, a semantic search framework that facilitates publishing, querying, and browsing RDF data sets. We show the different interfaces implemented by SANTé through guided steps from raw RDF data to the search result using keyword queries. We demonstrate how SANTé can be used to publish and consume RDF data.Repository: http://github.com/AKSW/santeLicense: https://www.apache.org/licenses/LICENSE-2.0FOAF demo: http://foaf.aksw.org/Pokémon demo: http://pokemon.aksw.org/