Reviewerly/exHarmony huggingface dataset:
Application for Image Processing in a Receiver Operating Character-Based Testing Review

Abstract The image processing technology is currently developing quite rapidly. Implementation of technology based on artificial intelligence systems has entered various sectors and platforms, mostly in the health and agriculture sectors. There are challenges in developing image processing applications, especially on mobile platforms, including processing a large amount of data that involves millions of pixels that must be recognized. To measure the quality of the results of the process certainly requires a reliable framework. In testing the quality of training data in the database and case studies, many image processing techniques utilize the Receiver Operating Character (ROC) approach’s reliability. In some cases, this ROC approach is very different depending on the data model, output, and algorithm used. This study aims to review ROC’s use as an approach used in testing systems on developed mobile applications. The case studies used in this research are image processing in agriculture cases for pests and disease recognition in cocoa fruits and chili leaves, which is then implemented in a mobile application. Testing entities in the ROC approach are analyzed based on actual and predicted conditions. The measurement results show that the ROC approach is applied in both test cases. It can only produce accuracy measurements on the value of accuracy, sensitivity, and precision. The values on chili leaves are accuracy 60%, sensitivity 60%, and precision 100%. The accuracy is 66.7% for cacao fruit objects, sensitivity 66.7%, and precision 100%.
Research activities: logic and methodology

Introduction. The paper reveals the peculiarities and interrelation of such categories as science, scientific activities, research activities. Research activity is defined as a purposeful cognitive process, which has its own logic and methodology, a system of concepts, laws and categories. This activity is associated with justifying the need for scientific inquiry and solving theoretical and practical tasks, as well as with conducting research and experiments in order to expand existing and obtain new knowledge. Aim . To develop professional competences related to the understanding of theoretical and methodological foundations of research activities. The study revealed the essence and content of research activities, their compliance with established standards and rules. The main content characteristics of research activities were analyzed: relevance of scientific research, its object, subject, aim, objectives, validity of results, evidence of generalizations and conclusions of scientific research. Materials and methods . The paper analyzes the applied meaning of the concept of methodology as a system of approaches and principles of practical and theoretical research activities, on which the researcher relies in applying the research methods. As an example, the content of practical methods – observation and experiment – is revealed. Observation is defined as a research-focused, deliberate and purposeful perception of phenomena and processes without direct intervention in their course. Experiment as a method that involves research under controlled and managed conditions.
Utilizing different machine learning methods to accurately predict density, temperature, velocity, and thermal conductivity of hydrophilic, hydrophobic, and compound materials

A learning algorithm is a set of instructions used in machine learning that enables a computer program to imitate how humans improve at describing specific types of data. As programming is exposed to more data, math and logic that support a learning algorithm can be updated (without human intervention) over time. In this study, different machine learning regression methods were used to predict the thermal conductivity (knf) -related physical properties of tested samples. Different parameters were considered such as density, temperature, rate, velocity, and knf is the most significant to improve heat transfer. Gaussian regression, linear regression, support vector regression, generalized linear regression, regression tree, artificial neural network (ANN), LSBoost Ensemble, and bag Ensemble were used. Gaussian regression method was produced the best results based on research comparing these methods. This method produced more accurate results in four fields (density, temperature, rate, and knf) than other methods. However, the best results in velocity field via LSBoost regression, which entails calculating the RMSE and PLCC. After selecting the optimal regression, the results of this regression should be validated. All of trained models had a strong correlation with real-world values, based on the findings of study, which are positive. Finally, it was determined that the covariance function was the only parameter that affected the RMSE results in Gaussian regression. This study illustrates the effect of using distinct covariance functions for each RMSE physical properties. Density is expressed over exponential function, and velocity and rate are expressed in mother32. The temperature was expressed due to the exponential square, and knf was expressed in terms of exponential.
FAMG: A flow-aware and mixed granularity method for load-balancing in data center networks

In current Data center Networks (DCNs), Equal-Cost Multipath (ECMP) is a default load-balancing scheme. However, using ECMP may result in the rapid growth of Flow Completion Time (FCT) due to its well-known drawbacks. In order to solve the problems of ECMP, load balancing schemes with per-packet granularity (such as RPS) and per-flowlet granularity (such as LetFlow) are proposed. These two different granularities have their features, but there is no existing load-balancing scheme considering both their advantages. Moreover, these schemes ignore the traffic characteristics in DCNs. In this paper, we mix per-flowlet and per-packet granularity in scheduling decisions and consider traffic characteristics. Based on this idea, we propose a flow-aware and mixed granularity method for load-balancing, referred to as FAMG. The key idea of FAMG is to distinguish elephant and mouse flows and schedule them with different granularities. We evaluate FAMG in simulations with NS-3. Experimental results show that FAMG slightly sacrifices elephant flows FCT but it is worth it since our results demonstrate that FAMG is a good trade-off that outperforms RPS, LetFlow, and TLB in terms of mouse flows FCT in both symmetric and asymmetric network topology.
Superresolution Image Reconstruction: Selective milestones and open problems

In multidimensional signal processing, such as image and video processing, superresolution (SR) imaging is a classical problem. Over the past 25 years, academia and industry have been interested in reconstructing high-resolution (HR) images from their low-resolution (LR) counterparts. We review the development of SR technology in this tutorial article based on the evolution of key insights associated with the prior knowledge or regularization method from analytical representations to data-driven deep models. The coevolution of SR with other technical fields, such as autoregressive modeling, sparse coding, and deep learning, will be highlighted in both model-based and learning-based approaches. Model-based SR includes geometry-driven, sparsity-based, and gradient-profile priors; learning-based SR covers three types of neural network (NN) architectures, namely residual networks (ResNet), generative adversarial networks (GANs), and pretrained models (PTMs). Both model-based and learning-based SR are united by highlighting their limitations from the perspective of model-data mismatch. Our new perspective allows us to maintain a healthy skepticism about current practice and advocate for a hybrid approach that combines the strengths of model-based and learning-based SR. We will also discuss several open challenges, including arbitrary-ratio, reference-based, and domain-specific SR.
The Importance of Higher Education on Economic Growth

"It is very important for countries to stimulate their economic prosperity and reduce poverty. While it is commonly accepted that education of all forms enhances economic performance of a country, yet the question of which level of education, secondary or tertiary, is more important to fuel the economy is not conclusively answered. The finding in Avraham Cohen’s PhD thesis is that education, as measured by published research, is strongly associated with Artificial Intelligence index of countries. Also, he found that AI is strongly associated with GDP. His findings encourage the idea that higher education, which is needed for Artificial Intelligence (AI), will be a dominant factor for economic growth in general; hereby, policy makers should invest in higher education to ensure economic affluence. Enrollment rates for higher education in Sub-Saharan Africa are by far the lowest in the world and the academic research output in the region is among the world’s lowest. Because of a belief that primary and secondary schooling are more important than tertiary education for poverty reduction, the international development community has encouraged African governments’ relative neglect of higher education. Using methodology of fuzzy logic-based soft regression, this study challenges these beliefs and demonstrates that economic growth is almost exclusively determined by higher education. Also, the importance of higher education relative to secondary education is more so in recent years relative to a decade or two ago. Motivated by this finding, I propose to find the right branch of higher education for every country based on its economic competitive advantage that will impact its economic growth best. Keywords: Education, Economic Growth, Artificial Intelligence, fuzzy logic, soft regression"
VeGAn-Tool: A fuzzy-logic approach for value-based goal model analysis

Goal-oriented analysis tools are used to assess goal models and assist analysts in decision-making. We introduce the VeGAn-Tool, which prioritizes goals according to their qualitative importance for the stakeholders and propagates this information in the goal model according to the different types of relationships. The FTOPSIS technique is used to calculate the value of each intentional element by employing the fuzzified importance (importance level fuzzified and refined by a confidence level) and the impact among the related intentional elements. The result is a prioritized goal model according to the value of each intentional element from the stakeholders' point of view.
Understanding the Dynamics of Economic Development in Developing Countries

In order for emerging countries to advance and prosper, their economies must develop in a complex and varied manner. The goal of this research study is to give readers a thorough grasp of the forces that drive economic growth in emerging countries. The introduction of the chapter provides an overview of the main ideas and theories surrounding economic development, covering elements like human capital, technical innovation, institutional quality, and globalisation. After that, it analyses the unique problems that developing nations face, including poverty, income disparity, and restricted access to resources and infrastructure. Additionally, this research study explores how social, market, and governmental elements interact to promote economic development. It examines how social capital, inclusive institutions, and good governance all contribute to the promotion of long-term economic success. Along with the potential advantages and difficulties of inclusion in the global economy, the influence of globalisation and trade on developing economies is also addressed.
A Study on the Acceptability of Homomorphic Causative/Passive Verbs in Korean

The purpose of this paper is to show the relations of acceptability changes between the lexical causative and lexical passive constructions. In order to do this, using the constructions with homomorphic causative/passive verbs, the acceptability between the lexical causative and lexical passive constructions is investigated through questionnaire survey and the analysis of the result is executed by SPSS 27.0 program. The paper is structured as follows. First, the acceptability between the lexical causative and lexical passive constructions is presented using descriptive statistics. In order to examine the changes in the acceptability between the lexical causative and lexical passive constructions, the 'Welch test' is conducted in this analysis. I focus on comparing the acceptability between the lexical causative and lexical passive constructions according to the age groups, respectively. Afterwards, it is demonstrated through Pearson correlation analysis and Two Way Anova that there are some relationships between the changes in the acceptability between the lexical causative and lexical passive constructions. Also, the tendencies of the acceptability changes is claimed to be predicted in the lexical causative and the lexical passive constructions. Finally, the thesis concludes by presenting a hypothesis of the interactions on the acceptability between lexical causative and lexical passive constructions.
Image Error Detection: A Systematic Literature Review

The advancement of technology, as well as the creation of new techniques and methodologies for image analysis, is rapid. However, image detection may face some errors. Image error detection will be discussed in this comprehensive literature review. Throughout the papers, this work attempts to learn about the types of images used, algorithms that are frequently used, techniques that are frequently used, and metrics used to test the correctness of the suggested approach. The most commonly used image type is medical images such as Magnetic Resonance Imaging, the algorithm that is widely used is a Convolutional Neural Networks based algorithm. The method that is widely used is a machine learning-based method, and the measurement that is widely used is a Peak Signal Noise Ratio measurement method to measure the accuracy of the algorithm.
VLSI Implementation of Neural Systems

A unique strategy for optimum multi-objective optimization for VLSI implementation of artificial neural network (ANN) is proposed. This strategy is efficient in terms of area, power, and speed, and it has a good degree of accuracy and dynamic range. The goal of this research is to find the sweet spot where area, speed, and power may all be optimised in a very large-scale integration (VLSI) implementation of a neural network (NN). The design should also allow for the dynamic reconfiguration of weight, and it should be very precise. The authors also use a 65-nm CMOS fabrication method to produce the circuits, and these results show that the suggested integral stochastic design may reduce energy consumption by up to 21% compared to the binary radix implementation, without sacrificing accuracy.
Energy and Performance Analysis of Robotic Applications Using Artificial Neural Network

Many robotic applications require autonomous decision making. The obstacles may be uncertain in nature. Because of the mobility, most robots might be battery operated. This chapter briefs the energy and performance analysis of robotic applications using artificial neural networks. This chapter is designed to understand the operation of robots from understanding sensor data (training), processing (testing) the data in an efficient manner, and respond (prediction) to the dynamic situation using self-learning and adaptability.
Attacks by Hardware Trojans on Neural Networks

The security aspects of neural networks (NN) have become a crucial and appropriate theme for basic research as a result of recent developments in neural networks and their use in deep learning techniques. In this research, the authors examine the security issues and potential solutions in computing hardware for deep neural networks (DNN). The latest hardware-based attacks against DNN are then described, with an emphasis on fault injection (FI), hardware Trojan (HT) insertion, and side-channel analysis (SCA). This chapter presents the various security issues in hardware-based attacks and security concerns in the hardware trojan (HT) and side-channel analysis are focused. Moreover, discussed the countermeasure for the hardware trojan and side channel attacks (SCA) is in neural networks.
Students Code-switching and Code-mixing in Spoken Communication Activities at English Language Education Department of UIN Mataram

The research aims to know the types of code mixing, the type of code switching and the factors of code-switching and code-mixing in the process of learning English in 6th semester students of the English Language Education department of UIN Mataram. This research was used descriptive qualitative approach. The subject of this study was one of the classes in the British American Australian course. The research applies observation, interviews and documents in collecting data. The study reveals that (1) there are 2 types of code mixing namely Insertion and alternation;(2) there are 2 types of code switching namely Intra-Sentential and Intra-Sentential; (3) there are 4 factors that cause students did code switching and code mixing in class namely situation, talking about a particular topic, difficulties to finding equivalent words in language in used, and intention of clarifying the speech content for interlocutor.
Rethinking University Pedagogy

As a result of the outbreak of COVID-19, and the subsequent move to online teaching and learning for contact institutions of higher learning, pedagogies that were on the periphery have now taken a central position. Such a transition has brought a paradigm shift on how universities will function in a VUCA world. There is a need to rethink how the curriculum prepares graduates to be relevant for the 21st century workplace. In this study, the author sets out to explore the challenges and opportunities of rethinking university pedagogy for curriculum transformation post COVID-19. The findings of this study confirm that the transition to online, despite the difficulties of implementation, awakened in students some of the 21st century skills related to living in the world, tools for working and ways of thinking. Noteworthy is that students value their learnings from both the physical and virtual social learning spaces. It is, therefore, necessary to rethink curriculum design and pedagogical practices to ensure that a binary between online and traditional contact classes does not occur.
Budaya Seiri, Seiton, Seiso, Seiketsu, dan Shitsuke di Industri Kecil Menengah Kerupuk Azizah

This community service activity was carried out by designing a cabinet for finished products and handing over the cabinet to a small and medium industry producing various types of crackers, namely IKM Kerupuk Azizah. IKM Kerupuk Azizah used open shelves to store packaged finished products. This practice was unhygienic and risky. Animals like rats and insects ate and damaged the materials and products. In addition, the warehouse needed to look more tidy. Moreover, from an aesthetic point of view, it was unattractive. A storage cabinet was designed to solve this problem. In designing the cabinet, available space, the number of piles of finished products, and the dimensions of the packaged finished products were considered. The results showed that, with a storage cabinet for finished products, IKM Kerupuk Azizah improved products’ quality and hygiene so that consumers would have more confidence in purchasing the products. In addition, IKM Kerupuk Azizah also applied the Seiri, Seiton, Seiso, Seiketsu, and Shitsuke (5S) principles in their work environment to create a clean, healthy, and comfortable workplace.
ZyPy: Intercepting NumPy operations for acceleration on FPGAs

Python's most popular numerical library, NumPy, is accelerated using GPUs, multi core CPUs, and clusters, yet a general approach on how to achieve this goal using Field Programmable Gate Arrays (FPGAs) is lacking. A methodology is presented to accelerate NumPy array operators and functions using FPGAs. A custom Python module ZyPy is developed which routes function calls transparently to their hardware-accelerated counterpart on the FPGA, provided that a compatible and profitable accelerator exists. To support this system, various NumPy routines are transformed into custom hardware accelerators by means of high level synthesis and integrated into Python using Xilinx's PYNQ platform A speedup by an order of magnitude is obtained for common array operations. The same approach allows to accelerate user defined Python functions.
Intelligent Lightning Hazard Warning System for a Wind Farm

Wind energy is one of the most important forms of renewable energy, and with the progress in this field, as the production capacity of wind turbines has increased, their height has also increased significantly. The height of wind turbines, number of them in a wind farm, and their specific location have increased the probability of lightning strikes and made them one of the most important hazards for wind turbines. Given the importance of maintenance and inspection for wind farms, creating a system for detecting safe time for these operations with low lightning probability is very useful. In this regard, by analyzing local meteorological data (pressure, temperature, wind speed, wind direction and humidity) and the lightning location system data an intelligent warning system for lightning hazard in a wind farm is developed based on machine learning methods. It is applied and tested on a case study of a wind farm in Croatia. The results show the success of this lightning hazard warning system in predicting the safe times with low lightning probability for the wind farm.
cuSCNN : an Efficient CUDA Implementation of Sparse CNNs

Deep Neural Network models are becoming much larger which greatly increases their computation and memory requirements. Sparsity offers great opportunities to reduce unnecessary data transfers and computations. However, exploiting sparsity in CNN inference presents challenges such as irregularities in memory access patterns. To overcome this challenge, we propose cuSCNN, an efficient sparse CNN inference engine that leverages the sparsity of both models and activations using optimized sparse-sparse matrix convolution kernels with compressed operands. cuSCNN is motivated by the concepts introduced by the SCNN hardware accelerator[21] but modified appropriately to achieve an efficient software implementation for GPUs. We develop GPU optimizations that boost execution performance and reduce the required memory size and bandwidth. cuSCNN achieves a speedup of up to 171 × compared to an efficient CPU implementation and 30 × speedup compared to a multi-threaded CPU implementation without batching, enabling the use of inexpensive low-end memory-constrained GPUs to implement large networks with near real-time latency. Although GPU throughput can benefit from larger batch sizes, batch size 1 achieves the lowest latency and hence we focus on it.
A Performance Evaluation of Two Blockchain Platforms for a Use Case

This paper describes the results of benchmarking two blockchain platforms’ performance. The authors conducted a performance assessment of various actions including adding, editing, deleting, and selecting the data from a blockchain. The blockchain platforms were run locally, in Ganache for Ethereum and IBM Blockchain Platform for Hyperledger Fabric. Additionally, code optimizations at the smart-contract level resulted in higher test speeds for both cases. The experimental case consisted in storing students’ diploma identification numbers, with the practical aim of preventing document forgeries.