Reviewerly/exHarmony huggingface dataset:
Chaotic Whale Optimization Algorithm in Hyperparameter Selection in Convolutional Neural Network Algorithm

In several previous studies, metaheuristic methods were used to search for CNN hyperparameters. However, this research only focuses on searching for CNN hyperparameters in the type of network architecture, network structure, and initializing network weights. Therefore, in this article, we only focus on searching for CNN hyperparameters with network architecture type, and network structure with additional regularization. In this article, the CNN hyperparameter search with regularization uses CWOA on the MNIST and FashionMNIST datasets. Each dataset consists of 60,000 training data and 10,000 testing data. Then during the research, the training data was only taken 50% of the total data, then the data was divided again by 10% for data validation and the rest for training data. The results of the research on the MNIST CWOA dataset have an error value of 0.023 and an accuracy of 99.63. Then the FashionMNIST CWOA dataset has an error value of 0.23 and an accuracy of 91.36.
Image Synthesis using Ensemble of Loss Functions: A Comparative Analysis using GAN Architecture

In this paper authors tries to show success of using ensemble of loss function on GAN network model. The model was able to generate quality synthetic data on MNIST handwritten dataset. The performance of the simple vanilla GAN model is evaluated for binary entropy loss and least square loss combined and compared for both of these loss functions separately. Instead of using only one single activation function we have used multiple activation functions such as Sigmoid., Tanh and ReLU in the last layer of the discriminator. Minimum loss value is counted among the pairs of transformed output vectors that are changed using different activation functions. In this case study we have used MNIST handwritten dataset. At last observations are made that training a model using ensemble of L2 loss and binary cross entropy loss functions produced significantly better results compared to using only L2 and only binary cross entropy loss function.
Factors Affecting Employee Performance: A Mediation Analysis

The research reported in this article was on “Factors affecting employee performance: A mediation analysis”. The study aims to examine the mediating role of job crafting in the Public universities. This research sheds light on the significance of job crafting with reference to negative emotions and diverse workforce and psychological aspects in regard to employee performance. The study aims to explore the determinants of employee performance of the workers in Public universities. Secondary data was collected with the help of prior research and literature from internet and libraries. This primary data was accumulated by distributing questionnaires and to analyzed data SPSS was used. This data was accumulated from 200 faculty members working in public universities of Lahore. In This current research it uses an empirical approach to test the hypotheses model and this research is quantitative and descriptive in nature. Data was collected thorough cross sectional design and questionnaire on variables. The SPSS 21 and Process Hayes is used for examine the data.&#x0D;
Research on Library Intelligent Service Based on Blockchain Technology

Computer technologies such as big data, cloud computing, and artificial intelligence are changing the present and future of the library. More and more technologies are applied to the library field, making the library more modern, intelligent and intellectual. Blockchain technology is one of the database technologies that has emerged in recent years, which consists of a series of cryptographically generated and timestamped data blocks (namely blocks composition). It uses a peer-to-peer distributed database system construction method to establish an open account book.. Applying this technology to the library will bring about a series of changes in the functions and services of the library. This article attempts to start from the blockchain technology, connect the blockchain with the library development technology, and explore the application of blockchain technology in the library's intelligent service.
Comparative Study of Loss Functions for Imbalanced Dataset of Online Reviews

Loss functions play a critical role in evaluating the performance of a model trained on specific parameters of the dataset. In simpler terms, loss functions serve as a penalty for a bad prediction to improve the prediction with its testing values. However, in the case of a prediction on an imbalanced dataset, the loss function must also be modified so that the weights of the loss that occurred due to misclassification do not change. In contrast, the weights in case of loss due to correct classification were reduced. Different developers have brought up a variety of loss functions to curb this problem that occurred due to an imbalanced dataset, one of them being the focal loss function. This focal loss function will be compared with the normal cross-entropy loss function, widely used in the evaluation. The final comparison between the two-loss functions will help determine whether the change of loss function can create how much difference in the model performance.
VALIDITY OF GUIDED INQUIRY-BASED LEARNING INSTRUMENT WITH GEOGEBRA APPLETS FOR TEACHING CIRCLES

The guided inquiry learning model is a student activity that emphasizes maximally training critical thinking skills. The learning instruments developed must be adapted to the syntax/stages of the guided inquiry learning model. Several learning instruments have been developed to assist teachers in making teaching materials. However, many teachers have not mastered technology, especially learning media using applications. Therefore, teachers need learning applications in schools, such as the GeoGebra applet. This study aims to develop valid guided inquiry-based learning instruments using GeoGebra applets for circles. This research is development research employing Plomp’s model and involving preliminary research and prototyping stages. The data was collected by compiling the comments and input from the validator through validation sheets related to learning instruments (lesson plans, worksheets, and GeoGebra applets). The results of the data analysis showed that the learning instruments were declared valid with an average lesson plan score of 4.75, an average student worksheet validation score of 4.60, and an average value of the total validation of GeoGebra applets of 4.94. Further research should assess the practicality and effectiveness of the instruments.
N°362 – Contribution of spectral analysis in epileptic encephalopathy with spike-and wave activation in sleep

It is a challenging task to detect small salient objects by fusing color information and spatial information from RGB-D images. Most RGB-D salient object detection (SOD) methods use U-Net or its variants because they tend to extract high-level features that can quickly determine the location of salient objects. However, it is also indispensable to effectively learn low-level features to detect fine boundaries and small structures. To this end, we propose a novel two-branch SOD network (COSU), which includes channel-overcomplete (CO) and spatial-undercomplete (SU) branches. First of all, the CO branch pays attention to low-level features by transform the spatial distribution into the channel connection and restricting the receptive field of filters. Secondly, SU branch uses increased receptive field to capture the high-level features. Finally, a multi-scale modal correction strategy is designed to fully fuse the multi-level features from the two branches and mitigate the modal divergence from RGB-D features. Experimental results on seven widely used datasets show that the proposed COSU outperforms ten state-of-the-art RGB-D SOD methods.
Correlation Filter Based on Saliency Detection and Channel Selection for Visual Object Tracking

To improve the utilization of convolution features by correlation filter trackers and reduce the influence of interference feature channels on algorithm performance, a correlation filter visual object tracking algorithm based on saliency detection and channel selection is proposed in this paper. Firstly, the HOG features and double-layer convolution features are used to represent the target, and the target salient region mask is obtained by the saliency detection method. Secondly, a channel selection mechanism is designed by using the salient region feature energy and the search region feature energy to remove redundant feature channels containing a large number of background information. Extensive evaluation results obtained on the OTB2015 benchmark demonstrate the effectiveness of the proposed method. The success rate and precision of the proposed algorithm are 67.5% and 91.3%, which are 5.4% and 9.1% higher than the benchmark algorithm BACF, respectively. In addition, according to the experimental results, it can be seen that the proposed tracker has a significant improvement in tracking performance compared with competitors in tracking scenarios with challenges such as deformation, background clutter, and rotation.
Narrative inquiry of Distance Learning in Practicum of Secondary pre-service mathematics teachers

Objectives This study explores te experience of distance learning in practicum of seoncdary pre-service mathmatics teachers. The future of distance learning and the system for the effective practicum within and without the school field were discussed.&#x0D; Methods Three of fourth-grade students of the mathematics education department attending a local university were selected as research participants, and data collection and interviews were conducted. Data were collected for two months from October 2021, and interviews were conducted either directly with the research participants or using Zoom.&#x0D; Results The distance learning experienced by the secondary pre-mathematics teacher was insignificant, and the secondary pre-service mathematics teachers experienced difficulties due to the lack of interactive communication with students in distance learning at school, unskilled time arrangement and document implementation, and the gap of class participation among students. In addition, it was found that the practicum serves as an opportunity for secondary school pre-service mathematics teachers to improve the difficulties and problems they experienced.&#x0D; Conclusions Based on these findings, it was discussed that the advantages of distance learning should be maximized in the school field and the up-coming system of practicum, the gap of the quality of experience in practicum caused by differences between practical schools should be minimized, and the ability of pre-service mathematics teachers using technology in class should be improved.
Food Recommendation System using One-Stage Algorithm

Abstract: There are a number of reasons why it's helpful when people suggest their favourite foods. There has been a rise in the use of automated systems that provide recommendations for various forms of food, including recipes, restaurants, and grocery stores. Scientists have been looking at these sorts of systems for a long time, and their findings show they may help individuals not just identify food they would want to eat, but also fuel themselves more properly. This article summarises the current status of so-called food recommender systems, focusing on both foundational and cutting-edge approaches to the issue, as well as essential specialisations such food recommendation systems for groups of users or systems that encourage healthy eating. Assisting consumers in finding their ideal meal is a crucial function of food recommender systems. Choosing what to eat is a complicated and multi-faceted process that is impacted by many things, including the ingredients, the look of the dish, the user's own preference on food, and many situations, such as what had been eaten at the previous meal. This paper formalises the meal suggestion issue as forecasting user preference on recipes using three essential factors: the user's (and other users') history, the recipe's components, and the recipe's descriptive picture. In order to solve this difficult issue, we design a special neural network. The recommender creates a model of eating habits based on responses to previous surveys. The model is then used to prompt respondents, based on the meals they have previously picked, to submit any linked items they may have forgotten to mention. Model-generated cues were evaluated against those that were manually programmed by dietitians.
Parsing through paradigms: uncertainty and decision-making in human information behavior

Purpose This study demonstrates how individual paradigms implicate the questions asked, methods used and results drawn in association with a common object of study in human information behavior (HIB) research – the relationship between uncertainty and decision-making. Design/methodology/approach The author uses textual case studies to examine uncertainty and decision-making through the framework of four paradigms used in HIB research: positivism, cognitivism, collectivism and constructionism and suggests deconstructionism as a paradigm which raises new questions around this topic. Findings Positivistic approaches to uncertainty are often systems oriented; cognitive approaches are often user-oriented; collectivist approaches are intersubjective; and constructionist approaches blend a subjective and intersubjective research orientation. Deconstructionism raises new questions around ethics and responsibility in relation to decision-making, and the author therefore situates it as a new paradigmatic approach for this topic in HIB research. Originality/value Despite the presence of research aimed at recognizing and defining paradigms in HIB research, a comparative micro-examination of how individual paradigms implicate a specific research topic has yet to be conducted. Each paradigm uniquely shapes the ways in which uncertainty and decision-making are characterized, but the four central ones examined here have thus far left out questions of ethics and responsibility as being core elements of decision-making as tied to uncertainty. Therefore, this paper introduces deconstructionism as a paradigm new to HIB uncertainty research, arguing that it provides an important and novel complication of existent research questions and approaches.
Seesaw

Abstract “Seesaw” studies the up and down motions of fingers and keys. To strike a note at the piano is to cause certain forces to go down in space—the finger, the hand, the arm, the piano key. Downward forces are easier to enact and control if an upward counterpart is already built into the downward gesture. The pianistic action may be likened to skipping rope or bouncing on a trampoline—or, indeed, to the up-and-down movements of a seesaw. This chapter covers the use of driving rhythms to facilitate the integration of up-and-down motion, the importance of overall coordination and mindset in mastering the bounce of the up-and-down motion, and the difference between responding to a physical command and to an interpretive, creative command. The entire chapter is based on a single archetypal rhythmic formula, spun into compositions of varied styles.
The Influence of Shared Leadership towards Organizational Commitment in a Public Organization of United Arab Emirates

The existence of many leadership styles implies a lack of consensus on the best way to guide organizations in their pursuit for progress.Equally, the interplay between leadership style and outcomes such as organizational commitment remains unclear.This study investigated the influence of shared leadership on organizational commitment within the public sector in UAE context.The research aims to explicate the influence of shared leadership on organizational commitment in one of the public organizations in UAE.The study employed a quantitative research approach -a survey method using questionnaires to collect data.Data was analyzed using both descriptive and inferential statistical methods.The descriptive approach relied on the mean, standard deviation while inferential method was deployed using regression analyses.The study found that shared leadership is practiced in the public sector entity in UAE, given the average mean values of 4.1 and standard deviation of 0.92.The R value between shared leadership and organizational commitment is 0.48 thus indicating a moderate relationship between shared leadership and organizational commitment in public sector entity in UAE.The study has both practical and theoretical implications.Given the possibility of the influence of shared leadership being indirect, the study implication is that future researcher should investigate the presence and role of intervening variables.Practically, leaders within the UAE context would benefit from employing shared leadership as best practice more extensively to improve organizational commitment.
Unconventional Data and the Precedent of Newspaper Clippings Services: Innovative Services at Dr. B R Ambedkar Central Library, JNU, New Delhi

Newspapers are essential sources of information as they contain current information in the form of news.In the current digital era, online newspapers and online services based on newspapers are becoming more and more wellliked and valuable.The information published in the newspaper covers every aspect of Socioeconomic technology, International relation, Education, Politics, Elections, research related, etc., around the world.These services helped libraries reach out to users wherever they were and saved users time.
Utilizing Blockchain Technology to Enhance Smart Home Security and Privacy

In recent years, the Internet of Things (IoT) has emerged rapidly in the field of smart home automation. However, the short battery life, low processing power, and limited memory of IoT technology result in limited fault lines. According to the Open Web Application Security Project (OWASP) report, approximately 70% of IoT devices are vulnerable. However, it can also be easily hacked and exploited. By utilizing blockchain technology, the fragile structure of IoT can be made more robust and reliable by tackling its vulnerable nature. The study aims to assess the security of blockchain technology by launching a predetermined attack on the MAN-IN-THE-MIDDLE (MITM) in a smart home environment. This testing attack is intended to prevent various threats and make the system more reliable and secure. Several tests have been conducted to determine whether the indigenous IoT protocol (MQTT) is more secure than blockchain technology. As a result, the MQTT protocol was replaced with the blockchain protocol in this study. This study examined Hyperledger, which is associated with the Chaincode, among the various blockchain platforms. Three IoT systems have been assessed for security by simulating Man in the Middle attacks and examining their security features. Our results show that the blockchain is more secure for IoT systems compared with MQTT protocols.
The Ability of 1st Class Students of SMAN 11 Enrekang to Arrange Verbal and Nominal Sentences

This research aims to reveal the ability of 1st graders of SMAN 11 Enrekang in compiling verbal and nominal sentences using the simple present tense and the difficulties that occur when compiling verbal and nominal sentences using the simple present tense. The researcher used quantitative method by applying the Surface Strategy Taxonomy in analyzing the data. The results of this research indicating that there are four types of errors. The frequency and percentage based on the surface strategy taxonomy are 63.59% misformation, then 16.18% addition, 13.88% omission, and 6.35% misordering. Furthermore, there are three difficulties experienced by students in compiling verbal and nominal sentences using the simple present tense, 1) Students do not understand the grammatical system; 2) students just write down what comes to mind without following the grammar; and 3) students do not understand vocabulary. The teachers recommend practicing grammar rules, in the simple present tense for mistakes. In addition, other researchers can provide techniques to improve students' mastery of the simple present tense
Vocabulary Strategies for English Learners

Vocabulary is an essential part of a language. In Indonesia, especially in Ternate, many students struggle to enrich their vocabulary on mastering English. Students find difficulties in learning English and strategies in learning vocabulary. Therefore, this study aims to investigates vocabulary strategies for English learners. The research purposes were: 1) to determine the students' strategies in learning vocabulary and 2) to determine the dominant strategies used by university students. In this study, the researcher used a descriptive qualitative design, and the subjects of the study were students of Khairun University, consisting of 59 students. The data of the study were collected through a questionnaire. The result found that students used all types of vocabulary learning strategies. However, they used the memory and cognitive learning strategy more often than other strategies. Additionally, using a dictionary, repeatedly spelling the word in mind, and consciously contacting the target language through various media were the dominant strategies used by the students.
FAST Headings in MODS: Michigan State University Libraries Digital Repository Case Study

The Michigan State University Libraries (MSUL) digital repository contains numerous collections of openly available material. Since 2016, the digital repository has been using Faceted Application of Subject Terminology (FAST) subject headings as its primary subject vocabulary in order to streamline faceting, display, and search. The MSUL FAST use case presents some challenges that are not addressed by existing MARC-focused FAST tools. This paper will outline the MSUL digital repository team’s justification for including FAST headings in the digital repository as well as workflows for adding FAST headings to Metadata Object Description Schema (MODS) metadata, their maintenance, and utilization for discovery.
A Quantum States Preparation Method Based on Difference-Driven Reinforcement Learning

Due to the large state space of the two-qubit system, and the adoption of ladder reward function in the existing quantum state preparation methods, the convergence speed is slow and it is difficult to prepare the desired target quantum state with high fidelity under limited conditions. To solve the above problems, a difference-driven reinforcement learning (RL) algorithm for quantum state preparation of two-qubit system is proposed by improving the reward function and action selection strategy. Firstly, a model is constructed for the problem of preparing quantum states of a two-qubit system, with restrictions on the type of quantum gates and the time for quantum state evolution. In the preparation process, a weighted differential dynamic reward function is designed to assist the algorithm quickly obtain the maximum expected cumulative reward. Then, an adaptive [Formula: see text]-greedy action selection strategy is adopted to achieve a balance between exploration and utilization to a certain extent, thereby improving the fidelity of the final quantum state. The simulation results show that the proposed algorithm can prepare quantum state with high fidelity under limited conditions. Compared with other algorithms, it has different degrees of improvement in convergence speed and fidelity of the final quantum state.