Reviewerly/exHarmony huggingface dataset:
Appropriate Information Technology for E-Government Service of Cooperatives in Sampit City Web-Based

This research is motivated by the application of information systems through e-Government which is the main problem in the development of information systems applied to the Sampit City Cooperative Service, to develop information and sites that already exist and can be used properly and are beneficial for MSMEs in using cooperative services. The type of research used in this study is data sharing, the method used is the method of data collection and system development. The research result obtained is information system which is applied to the Department of Cooperatives through the creation of a website can help p roses collection more effective and efficient related registration of members , p romosi product data and product advancements SMEs as well as web site also helps better services to SMEs and the public thereby improving the relationship between the two parties and illustrates the implementation of a good information system from the public authorities to the local area
A Study of Modality as a Stylistic Approach in the Analysis of Awareness of Iraqi EFL University Students

It was noted that normativity constitutes one of the basic areas in linguistics, especially in stylistics. The complexities of multiple meanings and the uses of regular verbs in their various functions posed a real challenge for students learning English as a foreign language. Normative refers to the speaker's attitude and responses, which range from desire to obligation, duty, and certainty or uncertainty regarding the likelihood of the proposed meaning of the utterance. The current study investigates the role of teaching standard verbs as a stylistic approach in strengthening the performance and awareness of Iraqi students learning English as a foreign language in the Department of English - University of Kufa. The study attempts to answer the following questions: 1) To what extent can students learning English as a foreign language master and distinguish standard verbs at all levels of production and recognition. 2) To what extent are methods of teaching standard verbs in relation to their function and context effective in enhancing the ability of students learning English in learning and communication. 3) Show the grammatical and semantic properties of standard verbs and how students can identify their function and uses
comparative study of Virtual Microscopy versus Traditional Microscopy for introduction into the Histology Curriculum at a Faculty of Medicine

Purpose: We currently employ traditional microscopy with glass slides for teaching purposes. Here, we performed a pilot survey on the potential implementation of virtual microscopy. Experienced peer tutors were asked to participate in the evaluation of virtual microscopy versus traditional microscopy. Our main objectives were to determine: whether there is a statistically significant difference in time to locating a structure on a glass slide versus a virtual slide, the possible advantages and disadvantages of each microscopy method, and the benefits of virtual microscopy over traditional microscopy (1-3).&#x0D; Methods: Glass slides were scanned with the Aperio ScanScope CS slide scanner for use as virtual slides. Peer tutors were asked to locate the same structures on each glass slide and the corresponding virtual slides. Times needed to locate these structures were individually measured, and a questionnaire (a modified and translated version of the College of Veterinary Medicine, North Carolina State University virtual microscopy survey) on the advantages of practical use of virtual microscopy provided at the end.&#x0D; Results: One sample t-test showed significant differences (p&lt;0.01) in mean time to locate a structure on glass (23.0s) versus virtual slides (12.5s). In commentaries, peer tutors reported a preference for a combination of traditional and virtual microscopy in practical sessions.&#x0D; Conclusions: Assessors considered virtual microscopy easy to use. Time needed to locate a structure on virtual slides was significantly shorter. After additional surveys, we envisage successful implementation of virtual microscopy in conjunction with traditional microscopy in the histology curriculum.
THE SPEED OF LEARNING CONVOLUTIONAL NEURAL NETWORKS ON THE GPU AND CPU TO DETECT SYNTHESIZED SPEECH USING SPECTROGRAMS

In this work has been investigated the possibility of using convolutional neural networks to detect synthesized speech.The Python programming language, the TensorFlow library in combination with the high-level Keras API and the ASVspoof 2019 audio database in flac format were used to create the software application.The voice signal of synthesized and natural speech was converted into mel-frequency spectrograms.The structure of a convolutional neural network with high indicators of recognition accuracy is proposed.The learning speed of neural networks on GPU and CPU is compared using the CUDA library.The influence of the batch size parameter on the accuracy of the neural network was investigated.The TensorBoard tool was used to monitor and profile the learning process of neural networks.
LOW COMPLEXITY RECURRENT NEURAL NETWORKS FOR EDGE COMPUTING

This paper is dedicated to the development of recurrent neural networks in order to supplement the AI-based devices like microcontrollers and other mist computing systems.Due to the insignificant computational power of the edge deviсes the aim of the study is to design and analyze low complexity sequence models for a basic sensory time series forecasting on an example of univariate indoor temperature data.The description of data preparation and transformation followed by the models configuration via different architectures like simple LSTM and GRU is provided.To calculate an optimal set of hyper-parameters for the multiple neural network architectures a genetic algorithm has been implemented.The results of numerical experiments conducted for each model configuration consisting of both unidirectional and bidirectional cell connections are discussed.In addition to these studies the scheme of deploying the developed low-complexity models on STM32 microcontroller joined with the highperformance hub is proposed.
Application of smartphone in recognition of human activities with machine learning

The aim of activity recognition is to determine the physical action being performed by one or more users based on a series of observations made during the user's actions in the relevant environment. Significant advancements in the field of human activity have resulted in the creation of novel ways for supporting elderly persons in doing their tasks independently. Using ambient computing, this type of service will be manageable. Many of services are provided by ambient technology, involving home automation tools, monitoring the behaviour of diseased individuals, and utility management. Numerous academics are focusing their efforts on computer software architectures, system infrastructure, and distributed applications utilising sensor devices. Aim of this project is to develop an algorithm that can perform human activity recognition (HAR) better than the existing state-of-the-art approach. Several tasks must be done to achieve this goal. To compete with an existing HAR system, this study will rely on secondary data from the cutting-edge experiment; no new data will be collected. The central experiment will be used to quantitatively identify the best classifier based on prediction accuracy. The current study entails monitoring and assessing existing literature in order to generate hypotheses that may be tested via experiment.
Speech scrambling based on multiwavelet and Arnold transformations

For communication applications where secure speech signal transmissions are a key requirement, speech scrambler is taken into consideration. To prevent someone from listening in on private conversations without their knowledge, it can transform clear speech into a signal that is unintelligible. The proposed speech scrambling system involves using two types of frequency transformation techniques: multiwavelet transform and Arnold transform. The effectiveness of the scrambling algorithm was evaluated with the help of three different measurements: the peak signal to noise ratio (PSNR), the estimated time (ET), and the mean square error (MSE). According to the final findings, the outcome of the scrambled speech signal does not have any residual intelligibility, while the quality of the descrambled speech is extremely satisfactory and has a low MSE level.
Cryptocurrencies investment framework using sentiment analysis of Twitter influencers

&lt;span&gt;In recent years, cryptocurrency technology has become an attractive area for investment due to its transparency, independence, and non-transactional nature. Many analysts and researchers talk daily on social media about the future of various cryptocurrencies. These ideas can significantly impact whether or not people are willing to invest. This paper provides a framework to help traders learn about the opinions of influential people and organizations in the field. Over the course of six months, the sentiment of more than 90 significant Twitter users was extracted for the proposed framework. In this study, we used the Vader open-source tool for sentiment analysis. This paper provides an excellent opportunity for investment through sentiment analysis&lt;em&gt; &lt;/em&gt;of lesser-known or emerging cryptocurrencies. Also in this paper, we introduce the user importance factor to calculate the value of each tweet based on the number of retweets and comments. This factor shows the importance of their opinions instead of considering the number of followers of the authors. This factor causes a lower coefficient to be assigned to an author's opinion if it decreases in importance over time. The results show that in the short and long term, users' opinions are very effective in the market for cryptocurrencies and in predicting its price &lt;/span&gt; trend.
Comparison hybrid techniques-based mixed transform using compression and quality metrics

Image quality plays a vital role in improving and assessing image compression performance. Image compression represents big image data to a new image with a smaller size suitable for storage and transmission. This paper aims to evaluate the implementation of the hybrid techniques-based tensor product mixed transform. Compression and quality metrics such as compression-ratio (CR), rate-distortion (RD), peak signal-to-noise ratio (PSNR), and Structural Content (SC) are utilized for evaluating the hybrid techniques. Then, a comparison between techniques is achieved according to these metrics to estimate the best technique. The main contribution is to improve the hybrid techniques. The proposed hybrid techniques are consisting of discrete wavelet transform (W), multi-wavelet transform (M), and tensor product mixed transform (T) as 1-level W, M, and T techniques. WT and MT are the 2-level techniques, while WWT, WMT, MWT, and MMT are the 3-level techniques. For each level of each technique, a reconstructed process is applied. The simulation results using MATLAB 2019a indicated that the MMT is the best technique with CR=1024, R(D)=4.154, and PSNR=81.9085. Also, it is faster than the other techniques in the previous works as compared with them. Further research might investigate whether this technique can benefit image and speech recognition.
An innovativefractal architecture model for implementing MapReduce in an open multiprocessing parallel environment

One of the infrastructure applications that cloud computing offers as a service is parallel data processing. MapReduce is a type of parallel processing used more and more by data-intensive applications in cloud computing environments. MapReduce is based on a strategy called "divide and conquer," which uses regular computers, also called "nodes," to do processing in parallel. This paper looks at how open multiprocessing (OpenMP), the best shared-memory parallel programming model for high-performance computing, can be used with the proposed fractal network model in the MapReduce application. A well-known model, the cube, is used to compare the fractal network model and its work. Where experiments demonstrated that the fractal model is preferable to the cube model. The fractal model achieved an average speedup of 2.7 and an efficiency rate of 67.7%. In contrast, the cube model could only reach an average speedup of 2.5 and an efficiency rate of 60.4%.
Using support vector machine regression to reduce cloud security risks in developing countries

The use of the cloud by governments throughout the world is being aggressively investigated to increase efficiency and reduce costs. The majority of cloud computing risk management programs prioritize addressing cloud security issues that government organizations may face when they choose to adopt cloud computing systems, but these programs lack evidence of security risks, and problems with using cloud computing in developing nations are uncommon, so they called for more research in this area. The objective of this paper is to use quantitative models namely Spearman's Rank correlation coefficient, simple regression, and support vector machine regression (SVMR) for estimating cloud security issues based on cloud control factors for improving the mitigation of cloud computing security issues based on control factors using intelligent models in a government organization. Identify the proper cloud control factors for every cloud security issue from estimation errors using a standard for performance measurement like mean square error (MSE) and root mean square error (RMSE), performance measurement to evaluate and validate proposed models. SVMR is an approach to enhance practices for cloud security platforms to mitigate risks and infrastructure for cloud adoption in developing countries in this paper.
Query-based image tagging model using ensemble learning with enhanced artificial bee colony optimization

Digital images make up most multimedia data and are analysed in computer vision applications. Daily uploads of millions of pictures to Internet archives such as satellite image repositories complicate multimedia content and image graphs. As feature vectors, content based image retrieval (CBIR) and image classification models represent high-level image viewpoints. Observing photos recognizes objects and evaluates their significance for image enhancement. To access the visual information of big datasets, efficiently retrieve and query picture graphs. The artificial bee colony (ABC) algorithm is inspired by the foraging behaviour of honeybee swarms. ABC is susceptible to laziness in convergence and local optimums, just like other optimization methods. This study created an enhanced ABC (EABC) model to enhance precision. This study presents query-based image tagging model using ensemble learning with EABC (QbITM-ELEABC) for CBIR for appropriately tagging images based on the query image. We examine a number of convolutional neural network (CNNs) with varying topologies that can be trained on the dataset with varying degrees of similarity. As representations, each network extracts class probability vectors from images. The final image representation is created by combining the ensemble's class probability vectors with image.
Imbalanced dataset classification using fuzzy ARTMAP and computational intelligence techniques

Recently, fuzzy adaptive resonance theory mapping (ARTMAP) neural networks are applied to solving complex problems due to their plasticity-stability capability and resonance property. An imbalanced dataset occurs when there is the presence of one class containing a greater number of instances than other classes. It is skewed representation of data. Many standard algorithms have failed in mitigating imbalanced dataset problems. There are four paradigms used-data level, algorithm level, cost-sensitive, and ensemble method in solving imbalanced dataset problems. Here we put forward a method to solve the imbalanced dataset problem by a brain-neuron framework and an ensemble of a special type of artificial neural network (ANN) called fuzzy ARTMAP thereafter we applied a clustering algorithm known as fuzzy C-means clustering to handle missing value and also propose to make fuzzy ARTMAP cost-sensitive. Results indicate 100% accuracy in classification.
Analysis study of the bee algorithms as a mechanism for solving combinatorial problems

Combinatorial optimization problems are problems that have a large number of discrete solutions and a cost function for evaluating those solutions in comparison to one another. With the vital need of solving the combinatorial problem, several research efforts have been concentrated on the biological entities behaviors to utilize such behaviors in population-based metaheuristic. This paper presents bee colony algorithms which is one of the sophisticated biological nature life. A brief detail of the nature of bee life has been presented with further classification of its behaviors. Furthermore, an illustration of the algorithms that have been derived from bee colony which are bee colony optimization, and artificial bee colony. Finally, a comparative analysis has been conducted between these algorithms according to the results of the traveling salesman problem solution. Where the bee colony optimization (BCO) rendered the best performance in terms of computing time and results.
Costs and benefits of authentication advice

Authentication security advice is given with the goal of guiding users and organisations towards secure actions and practices. In this paper, we demonstrate that security advice can be ambiguous, contradictory and at times may not even have any clear benefits. We expand on current work by defining a formal approach to identifying costs of security advice and instigate a user study to identify the costs that apply to a large range of authentication advice. We also apply a simple framework for analysing the authentication related security benefits of advice. This allows us to identify costs and benefits for all classes of security advice.
REPRESENTATION OF IRONY IN WHATSAPP GROUP CHAT

This article explores the representation of irony in WhatsApp Group chats, aiming to identify the types of irony and narrate the meaning of the irony used. A case study was geared under Perrine's (1966) and Leech's (1981) analysis of the type of irony. Moreover, the result disclosed that touches of sarcasm were the favorable choice of irony to express the participants’ feelings. Sarcasm was detected by using uppercase characters, exclamation marks, and teasing words. Hyperbole and understatement were the second participants’ favorite to use to exaggerate reality and make a dramatic situation. Furthermore, implementing irony in daily conversation with decent knowledge is significant to flourish and enrich their linguistic adequacy to avoid misunderstanding and know the appropriate situation manual students' exploitations of irony will.
MOORA METHOD (MULTI OBJECTIVE OPTIMIZATION ON THE BASIC OF RATIO ANALYSIS) FOR THE BEST MOTORCYCLE SELECTION RECOMMENDATIONS

Motorcycles are one of the main choices of transportation that are of interest to the public, especially Indonesia, because they are simple in size, fast and cheaper than other means of transportation such as cars. Now many emerging types / types of motorcycles complete with advantages and disadvantages. This of course will make it difficult for consumers to make the right choice, according to the desired criteria. To make it easier for buyers to choose the type of motorcycle that suits their wishes, a decision support system is designed to recommend the appropriate type of motorcycle. This system was built with accurate calculations using the MOORA (Multi Objective Optimization on The Basic of Ratio Analysis) method so that the accuracy of calculations is guaranteed which is applied using PHP MySQL software. With this system, buyers will have no difficulty choosing the type of motorbike that suits their needs and finances so that a comfortable and fast buying and selling process will be created.&#x0D;
Linear optical logical Bell state measurements with optimal loss-tolerance threshold

Quantum threshold theorems impose hard limits on the hardware capabilities to process quantum information. We derive tight and fundamental upper bounds to loss-tolerance thresholds in different linear-optical quantum information processing settings through an adversarial framework, taking into account the intrinsically probabilistic nature of linear optical Bell measurements. For logical Bell state measurements - ubiquitous operations in photonic quantum information - we demonstrate analytically that linear optics can achieve the fundamental loss threshold imposed by the no-cloning theorem even though, following the work of Lee et al., (Phys. Rev. A 100, 052303 (2019)), the constraint was widely assumed to be stricter. We spotlight the assumptions of the latter publication and find their bound holds for a logical Bell measurement built from adaptive physical linear-optical Bell measurements. We also give an explicit even stricter bound for non-adaptive Bell measurements.
RADIUS: Risk-Aware, Real-Time, Reachability-Based Motion Planning

Deterministic methods for motion planning guarantee safety amidst uncertainty in obstacle locations by trying to restrict the robot from operating in any possible location that an obstacle could be in. Unfortunately, this can result in overly conservative behavior. Chance-constrained optimization can be applied to improve the performance of motion planning algorithms by allowing for a user-specified amount of bounded constraint violation. However, state-of-the-art methods rely either on moment-based inequalities, which can be overly conservative, or make it difficult to satisfy assumptions about the class of probability distributions used to model uncertainty. To address these challenges, this work proposes a real-time, risk-aware reachability-based motion planning framework called RADIUS. The method first generates a reachable set of parameterized trajectories for the robot offline. At run time, RADIUS computes a closed-form over-approximation of the risk of a collision with an obstacle. This is done without restricting the probability distribution used to model uncertainty to a simple class (e.g., Gaussian). Then, RADIUS performs real-time optimization to construct a trajectory that can be followed by the robot in a manner that is certified to have a risk of collision that is less than or equal to a user-specified threshold. The proposed algorithm is compared to several state-of-the-art chance-constrained and deterministic methods in simulation, and is shown to consistently outperform them in a variety of driving scenarios. A demonstration of the proposed framework on hardware is also provided.
ANSEL Photobot: A Robot Event Photographer with Semantic Intelligence

Our work examines the way in which large language models can be used for robotic planning and sampling, specifically the context of automated photographic documentation. Specifically, we illustrate how to produce a photo-taking robot with an exceptional level of semantic awareness by leveraging recent advances in general purpose language (LM) and vision-language (VLM) models. Given a high-level description of an event we use an LM to generate a natural-language list of photo descriptions that one would expect a photographer to capture at the event. We then use a VLM to identify the best matches to these descriptions in the robot's video stream. The photo portfolios generated by our method are consistently rated as more appropriate to the event by human evaluators than those generated by existing methods.