Reviewerly/exHarmony huggingface dataset:
Amharic Text Complexity Classification Using Supervised Machine Learning

Amharic documents tremendously increase after the proliferation of the internet. It uses a variety of lexicons to organize the document. Some of them may not be familiar to second language learners and low literacy readers which can cause difficulty to comprehend the idea. Text complexity is focused on how difficult or easy a text is to read and understand based on the reader’s level of knowledge. The appropriateness of text for a certain learner group needs to be in line with their proficiency level. A document that contains complex lexicons can also reduce the performance of NLP tasks such as machine translation. Studying the complexity classification model for the Amharic text helps in solving text complexity for a target population and NLP applications. In this paper, we have developed a complexity classification model for Amharic texts using supervised machine learning. For the experiment, 5126 sentences are used. TFIDF and BOW with bigram language modeling are applied for vectorizing the text document and, Support Vector Machine (SVM), Random forest (RF), and Naïve Bayes (NB) algorithms are used for the experiment. SVM has better classification accuracy with a result of 87.1% using bag-of-words (BOW) feature extraction and 10-fold cross-validation. The RF and NB algorithms score an accuracy of 83% and 80.3% respectively. For error analysis, we have used Mean Square Error (MSE) and Root Mean Square Error (RMSE) metrics. In this study, we have addressed the classification of Amharic text complexity. The simplification process of such identified complex texts is our recommendation for future research works.
Deep Complex-Valued Neural Networks for Massive MIMO Signal Detection

In the fifth generation (5G) and future mobile networks, the design of efficient detectors for massive multiple-input multiple-output (MIMO) is essential. The main challenge in designing detectors is the trade-off between performance and computational complexity; that is, efficient detectors incur higher computational costs, while computationally cheaper detectors have lower efficiency. Recently, many deep learning-based detectors have been proposed in the literature to fill in such gaps. However, most of the existing MIMO detectors work only with real-valued parameters. First, they transform the complex received MIMO signal into an equivalent real-valued parameter by concatenating the real and imaginary parts and then train a network based on the real-valued data. Such an approach has several disadvantages. On one hand, the number of trainable parameters will be doubled; on the other hand, the phase information, which is important in the communication signals, might be lost or distorted. In this work, we aim to investigate the application of complex-valued neural networks for MIMO signal detection based on Wirtinger Calculus. To do so, we propose a simple feedforward architecture that directly works with the complex-valued QPSK and 16-QAM modulation signals. Our method is simple and computationally cheaper. Simulation results show that the proposed approach can improve the performance of the existing detectors while providing a lower computational cost.
THE EFFECT OF MOBILE BRANDS ON CONSUMER PURCHASE DECISION IN JORDAN

This study aims to explore the different brand dimensions that affect consumer purchase decisions. The researcher distributed 470 questionnaires for participants with Samsung- branded cellular devices. The research hypotheses were tested using the SPSS version 17.0 Program and AMOS 20.0 software- structural equation modeling SEM, which was used to analyze the data collected from the respondents. The study found that there is a significant positive effect of Perceived brand name and Perceived brand quality on consumer purchase decisions, while there is an insignificant effect for perceived brand price and perceived brand value on a consumer purchase decision. The recommendations were developed to enhance the role of the brand dimensions to benefit from it in the marketing of cellular devices both for companies and customers, perceived brand name and the Perceived brand quality are the ones that have the strongest effect, and therefore, companies that produce cellular devices should focus more on these dimensions to encourage consumers to make a purchasing decision. Furthermore, to improve the dimensions that got the lowest effect. Keywords : Mobile Brand, Consumer Purchase Decision, Brand Quality, Brand Name, Jordan DOI： https://doi.org/10.35741/issn.0258-2724.58.1.73
Amharic Character Recognition Using Deep Convolutional Neural Network

Amharic is the working language in the Federal Democratic Republic of Ethiopia. The Amharic alphabet has a large number of symbols and there is a close resemblance among shapes of the different symbols available in the language which challenged the task of machine-based optical character recognition systems in the language. The absence of a standardized labeled dataset for the Amharic language created additional barriers for different researchers. Our aim in this paper is to design a deep convolutional neural network based architecture that could extract features and classify Amharic characters with significant confidence of accuracy that could be utilized for real-world applications. A total of 90,000 characters are prepared for training the proposed architecture and an additional of 25,000 characters are reserved for testing purpose. Due to the occurrence of a large number of symbols and a close resemblance in the shapes of the different characters available in the language, a relatively complex convolutional Neural Network is utilized to capture those features and categorize them into the correct characters. Dropout layers are utilized to avoid overfitting. The character recognition system proposed in this paper achieved an accuracy of 99.27% on the testing dataset which is a significant improvement for the Amharic language. The implementation was done using Tensorflow on Keras neural network layers and Opencv in python to pre-process image data which enables us to make the system readily available for software developers as an API.
A Study into the Potential of IOT for Smart Healthcare

This study reflects the importance of IoT connected devices in the healthcare industry. Currently, with the development of certain technologies; this particular sector concentrates on improving the entire treatment procedure provided to patients. In this case, IoT devices have a vast importance in the field of aiding doctors and patients to keep daily track of health conditions. Medical tools that are used to cure serious diseases work quite faster in case IoT is connected. IoT devices help both doctors and patients in the entire treatment process. Not only patients or doctors, these devices help health care workers in many factors such as tracking wheelchairs, nebulizers, pacemakers and many more. Most importantly, no one needs to remember the records of every patient as it saves each and every single data of every patient. Doctors can check the past history of patients while checking the patient. It is obvious that these devices work faster than human hands. The IoT system has done a great job on saving time for both patients and doctors. More or less every healthcare unit is connecting the tools with the IoT system. Keyword : Doctor, Healthcare, IoT, Patients, System.
Composition Algorithms for Conditional Distributions

This chapter is about two recently published algorithms that can be used to sample from conditional distributions. We show how the efficiency of the algorithms can be improved when a sample is required from many conditional distributions. Using real-data examples from educational measurement, we show how the algorithms can be used to sample from intractable full-conditional distributions of the person and item parameters in an application of the Gibbs sampler.
Identification of Fire Disaster Vulnerability in Karang Rejo Sub-District, Balikpapan Central District

At least 617 cases of fire disasters were recorded in Balikpapan City from 2010 to 2020. In response, fire disaster mitigation is still a problem, especially in dense residential areas and unclean environments. One of the sub-districts with the characteristics of dense settlements and a history of fire incidents is Karang Rejo Village. This condition can be an obstacle in disaster mitigation. Therefore, it is necessary to identify appropriate mitigation efforts for this sub-district. Vulnerability assessment can be an essential first step to reducing the impact and losses of a fire disaster. The purpose of this study is to formulate fire disaster mitigation based on the level of vulnerability in Karang Rejo Village, Central Balikpapan District, using the spatial multi-criteria analysis method. The research results obtained are structural fire disaster mitigation at low, medium, and high vulnerability by mitigating aspects of building density, percentage of built-up areas, building materials, the width of access roads, availability of water reservoirs, availability of vacant land, the portion of households working in the informal sector, and the ratio of poor people.
GIS BASED INVENTORY OF SPORTS INFRASTRUCTURE

Geographic Information System (GIS) technology is a powerful tool that can systematically manage and analyze various types of geographic and attribute data.With its diverse functionalities such as query, display, analysis, statistics, and graphic output, GIS can provide comprehensive insights into complex data sets.The integration of GIS technology with sports information has revolutionized the way we understand and describe regional sports information.By leveraging the power of GIS, we can accurately map and analyze data related to sports activities and events in a specific geographic area.This article aims to explore and analyze the current state of GIS application in sports Informaionization.Through this study, we hope to provide practical suggestions for strengthening and deepening the application of GIS in sports Informa ionization.By utilizing GIS technology, we can enhance our understanding of sports information and optimize decision-making processes in the sports industry.
Prediction of Hourly Global Solar Radiation: Comparison of Neural Networks / Bootstrap Aggregating

This research work explores the use of single neural networks and bootstrap aggregated neural networks for predicting hourly global solar radiation.A database of 3606 data points were from the Renewable Energies Development Center, radiometric station 'Shems' of Bouzareah.The single neural networks and bootstrap aggregated neural networks were built together.The precision and durability of neural network models generated with an incomplete quantity of training datasets were improved using bootstrap aggregated neural networks.To produce numerous sets of training data points, the training data was re-sampled utilising bootstrap resampling by replacement.A neural network model was built for each of the data points.The individual neural network models were then combined to produce the bootstrap aggregated neural networks.The experimental and predicted values of global solar radiation were compared, and lower root mean squared errors (68.3968 and 62.4856 Wh m -2 ) were discovered during the testing phases for single neural networks and bootstrap aggregated neural networks, respectively.The results of these models show that the bootstrap aggregated neural networks model is more accurate and robust than single neural networks.
Zero watermarking scheme for privacy protection in e-Health care

E-health care is an emerging field where health services and information are delivered and offered over the Internet. So the health information of the patients communicated over the Internet has to protect the privacy of the patients. The patient information is embedded into the health record and communicated online which also induces degradation to the original information. So, in this article, a zero watermarking scheme for privacy protection is proposed which protects the privacy and also eliminates the degradation done during embedding of patient information into the health record. This method is based on simple linear iterative clustering (SLIC) superpixels and partial pivoting lower triangular upper triangular (PPLU) factorization. The novelty of this article is that the use of SLIC superpixels and PPLU decomposition for the privacy protection of medical images (MI). The original image is subjected to SLIC segmentation and non-overlapping high entropy blocks are selected. On the selected blocks discrete wavelet transform (DWT) is applied and those blocks undergo PPLU factorization to get three matrices, L, U and P, which are lower triangular, upper triangular and permutation matrix respectively. The product matrix L×U is used to construct a zero-watermark. The technique has been experimented on the UCID, BOWS and SIPI databases. The test results demonstrate that this work shows high robustness which is measured using normalized correlation (NC) and bit error rate (BER) against the listed attacks.
Semantic Similarity of Product and Service Names in Portuguese

The problem of conceptual comparison of names plays an important role in the field of natural language processing. In this task, the goal is to choose, among a set of names, which one refers to the same concept or object as a given input name. In this paper, we propose an algorithm for comparing names of products and services in Portuguese that takes account of the semantic information contained in the names. The semantic similarity between two names is calculated using information from Onto.PT, the largest public lexical ontology for the Portuguese language. Experiments were conducted on a dataset composed of 5,000 pairs of names of products and services in Portuguese. Our experimental results show that the algorithm based on Onto.PT is more effective than other well-known algorithms for name comparison, producing the highest recall and precision. Moreover, results also provide interesting insights into the advantages and disadvantages of using Onto.PT for assessing the semantic similarity of names and other kinds of short texts.
Blockchain-Based- E-Tender Management System

The tendering or contracts are used by governments and companies to procure goods or services providers. E-tendering being the mostly used for procurement method, there are various security implications present. To solve these this issue, we use Blockchain technology. We use of blockchain technology to secure transaction-based documents along with transactions such as to provide a completely transparent tendering process, tender documents, applications, bid proposals, company profiles, past records, approving officer details, and rejections details are all required. In these paper, how smart contracts can be employed to design a distributed e-tendering system is explored. The project is divided into four sections, 1. Tender creations and publishing process, 2. Bidding process on the tender, 3. Evaluations and Negotiation of the bid and 4. Selections of the Winning bid.
Correlation-aware probabilistic data summarization for large-scale multi-block scientific data visualization

Abstract In this paper, we propose a correlation-aware probabilistic data summarization technique to efficiently analyze and visualize large-scale multi-block volume data generated by massively parallel scientific simulations. The core of our technique is correlation modeling of distribution representations of adjacent data blocks using copula functions and accurate data value estimation by combining numerical information, spatial location, and correlation distribution using Bayes’ rule. This effectively preserves statistical properties without merging data blocks in different parallel computing nodes and repartitioning them, thus significantly reducing the computational cost. Furthermore, this enables reconstruction of the original data more accurately than existing methods. We demonstrate the effectiveness of our technique using six datasets, with the largest having one billion grid points. The experimental results show that our approach reduces the data storage cost by approximately one order of magnitude compared to state-of-the-art methods while providing a higher reconstruction accuracy at a lower computational cost.
Retinex decomposition and fusion dehazing network

Image dehazing has been widely used in vision-based fields, such as detection, segmentation, traffic monitoring, and automated vehicle system. However, most of the existing end-to-end dehazing networks are fully data driven without physical constraints or prior information guidance, leading to difficulties in exploring latent structures and statistical characteristics of hazy images. We propose a novel Retinex decomposition-fusion dehazing network consisting of a dual-branch decomposition module and a fusion optimization module. Different from existing solutions, we decompose the clear images in the commonly used RESIDE dataset based on Retinex theory to construct the clear illumination map and reflection map datasets to drive the network training, equivalently imposing reasonable constraints on the network and achieving impressive dehazing performances. The dual-branch decomposition module is developed to estimate the illumination map and the reflection map, respectively. The illumination map mainly contains the global features of the image, whereas the reflection map reflects the inherent color properties of the image and contains rich details, with which we explore the latent structures and statistical characteristics of hazy images. In addition, the dual-branch structure avoids the error accumulation and information cancellation existing in current methods. Subsequently, the estimated illumination map and reflection map are fused and refined via the fusion optimization module to access the dehazed image. Experiments show that the proposed network has better generalization and visual effects than existing fully data-driven methods and can be applied successfully to real-world scenarios.
Blind turbulent image deblurring through dual patch-wise pixels prior

Deblurring turbulent images is an active topic in image processing and low-level vision research. Existing methods usually use the parametric physical model for nonblind image restoration, which lacks adaptability to different turbulent scenes. To overcome this challenge, a dual patch-wise pixels (DPP) prior is proposed for effective blind deblurring of turbulent images. A DPP-based turbulent image deblurring model was established based on the fact that the value of the DPP decreases through the turbulent blurring process, which has been proven both mathematically and experimentally. To solve the nonlinear DPP in the model, a linear mapping operator was constructed. Additionally, half-quadratic splitting and threshold methods were used to solve the L0 regularization term. Experimental results showed that the proposed algorithm performs well on various types of turbulent scenes as well as real images and outperforms state-of-the-art algorithms in terms of computational efficiency and effectiveness.
Priority-2030: Risk or Success

The work is devoted to assessing the opinion of employees of Southwestern State University on the implementation of the strategic academic leadership program “Priority-2030”. The author’s sociological research was carried out, which allowed to identify new features of the educational process and the features of the communication space of the Southern State University within the framework of the implementation of the Priority-2030 program. The advantages of this program and possible risks are considered.
Fixations in Agile Software Development Teams

Abstract Cognitive biases influence every human being, including the individuals that take part in the software development process. Fixation is a cognitive bias that occurs when one focuses too much on certain items, events, obstacles or activities. In this study, we examine whether agile team members fixate on any particular agile practices. Through a set of semi-structured interviews, we investigated the source of these fixations, their consequences, and then propose possible countermeasures. We found that practitioners tend to fixate on practices that give them a sense of being in control over the project (such as meetings or Scrum events), while neglecting the Agile Principles of self-organising teams and working at a sustainable pace. This resulted in a series of problems, such as futile attempts to control team members, oversharing information with the client, meetings becoming a form of interrogation, and others.
What Should a Good Software Requirements Specification Include? Results of a Survey

Abstract Software requirements specification is an important foundation of the software development process. It documents the requirements, expectations, and restrictions for a system to be developed. It should describe what the produced system will offer in detail and unambiguously. It should also provide a realistic basis for estimating product costs, risks, and schedules. Still, it is difficult to prepare a useful specification effectively. We want to propose an approach to creating a good specification. We started the work from scratch by determining what the scope of the specification should be. We asked people who work on IT projects what information is and should be included in the specifications with which they work. This paper presents results from a survey conducted with 163 participants who have experience working on commercial software development projects. The main observation is that the content of requirements specification differs with respect to project characteristics, such as industry or financing method. We also noticed that the information about integration with external systems and functionalities most often appears in the SRS.
Comparing the understandability of iteration mechanisms over Collections in Java

Abstract Source code understandability is a desirable quality factor affecting long-term code maintenance. Understandability of source code can be assessed in a variety of ways, including subjective evaluation of code fragments (perceived understandability), correctness, and response time to tasks performed. It can also be assessed using various source code metrics, such as cyclomatic complexity or cognitive complexity. Programming languages are evolving, giving programmers new ways to do the same things, e.g., iterating over collections. Functional solutions (lambda expressions and streams) are added to typical imperative constructs like iterators or for-each statements. This research aims to check if there is a correlation between perceived understandability, understandability measured by task correctness, and predicted by source code metrics for typical tasks that require iteration over collections implemented in Java. The answer is based on the results of an experiment. The experiment involved 99 participants of varying ages, declared Java knowledge and seniority measured in years. Functional code was perceived as the most understandable, but only in one case, the subjective assessment was confirmed by the correctness of answers. In two examples with the highest perceived understandability, streams received the worst correctness scores. Cognitive complexity and McCabe’s complexity had the lowest values in all tasks for the functional approach, but – unfortunately – they did not correlate with answer correctness. The main finding is that the functional approach to collection manipulation is the best choice for the filter-map-reduce idiom and its alternatives (e.g., filter-only). It should not be used in more complex tasks, especially those with higher complexity metrics.