Reviewerly/exHarmony huggingface dataset:
Reward and Satisfaction: Keeping hospitality employees loyal

Employee loyalty is influenced by job satisfaction which stems from various factors such as reward, work environment, or leadership. Due to the importance of loyal employees in maintaining the competitive advantage of organisation, this study examines how reward in upper-upscale hospitality organisations in Selangor influences job satisfaction and employee loyalty. Structural equation modelling using SmartPLS found significant paths between financial rewards, job satisfaction, and employee loyalty. The same could not be said for non-financial rewards. The findings provide insights into how hoteliers can strategise their financial reward and maximise non-financial reward usage to retain the employees, especially during turbulences. Keywords: Financial reward; non-financial reward; job satisfaction; employee loyalty eISSN: 2398-4287© 2021. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians/Africans/Arabians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning &amp; Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v6i17.281
Proposed Model for Categorizing Consumer Digital Behavior Responses towards Health and Nutrition Information Over a Continuum of Technology

Demonstrate how to effectively segment and target individuals and populations based on their digital behavior response towards digital health and nutrition information across a technology-mediated continuum centered around user experiences.
Multiple PET Reconstruction Assisted Non-local Mean Denoising of PET Images

Non-local mean (NLM) denoising is commonly used for noise suppression in natural as well as medical imaging. Basically, the NLM filter takes advantage of the redundant information present in the image in the form of repeated structures/patterns to identify the underlying signals. In medical imaging (particularly PET and SPECT imaging), different representations of the image data under study (target or original image) could be reconstructed via applying different reconstruction settings. These representative (auxiliary) images bear very similar patterns/structures to the original/target image with different signal-to-noise ratios (SNR) which are ideal for use in the NLM denoising approach. This study proposed the multiple-reconstruction NLM filtering approach (referred to as MR-NLM) for noise reduction in PET imaging, wherein the redundant information present in auxiliary PET images are employed to conduct the NLM denoising process. The MR-NLM method relies on 12 additional PET image reconstructions (apart from the target PET image) using the same iterative algorithm with different iterations and subset numbers. Thereafter, for each target voxel, patches of voxels are extracted at the same location from all auxiliary PET images to be fed into the NLM smoothing process. To evaluate the performance of the MR-NLM algorithm, post-reconstruction denoising approaches including the conventional NLM, bilateral, and Gaussian filters were implemented and compared using 25 <sup>18</sup>F-FDG clinical whole-body (WB) PET/CT studies. The clinical studies demonstrated superior performance of the MR-NLM approach which established a promising compromise between noise suppression and preservation of the underlying signal/structures in PET images leading to higher SNR compared to the conventional NLM approach (34.9&#x00B1;5.7 versus 32.4&#x00B1;5.5). Though MR-NLM exhibited promising performance, this method suffers from long processing time due to the requirement of multiple reconstructions of raw PET data.
Fine Arts as a Means of Developing Creative Activity in Younger Adolescents

The role of fine arts in the process of developing pupils’ creative activity remains insufficiently studied. This is especially true for the specifics of developing a values-based attitude towards any kind of creative activity in the representatives of such a complex age group as younger adolescents. The paper aims to develop and experimentally verify the methodology for developing creative activity in younger adolescents using fine arts in the educational process of secondary schools based on theoretical justification of the problem of the individual’s creative activity. This methodology was aimed at implementing a holistic set of creativity exercises, partial searching and artistic creativity tasks and a variable model for organizing the actions in the form of “the projection of desire”. The control and experimental groups involved the same number of younger adolescents, namely, 143 respondents, whose indicators of development levels of creative activity did not differ significantly. Different methods were used to identify the level of creative activity: questionnaires, interviews, conversations, pedagogical observations, partial searching, multi-level artistic creativity tasks and creativity exercises. The level of creative activity in younger adolescents in the experimental group has increased: a prospective level - by 9.1% (from 7% to 16.1%), a differentiated level - by 5.6% (from 51.7% to 57.3%). A situational level has decreased by 14.7% (from 41.3% to 26.6%). All the obtained data differ from the tabular ones for the selected high level of statistical significance (0.01&lt;p&lt;0.05), which indicates the effectiveness of the developed methodology for developing creative activity in younger adolescents using fine arts in the educational process of secondary schools.
Accurate Matching Algorithm of Small Celestial Body Surface Texture Curve

Irregular curves are often used as navigation landmark in the landing of celestial probes. Accurate matching of curves is an important premise for visual navigation. For the problem that curve descriptor matching algorithm are difficult to accurately match and curvature matching algorithm can only handle a pair of curves, an accurate curve matching algorithm combining curve descriptor and curvature is proposed. The curves are extracted by Edge Drawing algorithm first; then the descriptors for curves and supporting regions are built, and the rough matching is completed based on Nearest Neighbor Distance Ratio. After, the unsigned curvature integral is calculated and the curvature is sampled at equal integration intervals. The accurate curve matching is realized based on Normalized Cross Correlation. The experimental results demonstrate that, the algorithm can achieve more than 84% accurate matching rate under scale, rotation and illumination transformations.
Students’ Attitude on the Benefits of Project-Based Learning in the Multimodal Platform

This study attempts to investigate the benefits of Project-based learning in the multimodal platform. It is exploring students’ perception towards the implementation of Project-based learning (PBL) in the multimodal platform based on students’ experience in working on the project used the multimodal platform in English instructional context. This study adopted a descriptive qualitative method. Data gathered through students’ reflection. The study was conducted in the English Language Education study program, at the University of IAIN Kendari. The participants of this study are fifth-semester students in the academic year of 2019-2020 who taking the Syntax course which the learning process of this course used PBL. Twenty-six students participated in this study by were wrote reflections to obtain information related to their perception toward Project-based learning implementation through the multimodal platform in their classroom. The collected data from students’ reflection then was highlighted, collated, and analyzed through coding and categorization. The results of the research reveal that PBL has advantages to students’ learning process. Besides, this study also shows that students have a positive perception of PBL implementation in the multimodal platform in their English classroom. Thus, this study implies that language teacher and teacher educators could utilize Project-based learning in the multimodal platform as a teaching-learning technic to develop students’ perception as well as students’ performance in the English teaching-learning process.
Research on Technology and Application of Buried Faults Identification in Urban Underground Space

With theurbanization rate's rising and three-dimensional expansion and development of urban, the identification of underground buried faults has become the key factor of earthquake risk in urban underground space and surface area. As a typical method of detecting blind faults in underground space, shallow seismic prospecting technology plays an important role in judging and avoiding potential risks such as underground faults in the process of urban expansion and site selection. In this paper, shallow seismic prospecting technology is adopted, and optimized processing technologies such as parameter test, tomographic correction, pre-stack denoising, fidelity and consistency processing, correction iteration, migration imaging, and time-depth relationship deduction are adopted. Underground faults are identified and interpreted in the studied urban area, and fault risk assessment is carried out based on fault characteristics, scale, distribution and overlying strata, thus providing suggestions for regional pattern and construction of urban planning.
Perbandingan Naïve Bayes dan Support Vector Machine untuk Klasifikasi Ulasan Pelanggan Indihome

IndiHome is an internet service provider from PT. Telekomunikasi Indonesia, Tbk with the widest internet coverage in Indonesia. Customer satisfaction is one of the things that must be considered in a company, including the IndiHome company. IndiHome's customer service satisfaction level can be seen from customer reviews via Twitter social media. This study discusses the classification of IndiHome customer reviews by applying the CRISP-DM research stages and the application of the Naïve Bayes Classifier algorithm and the Linear Support Vector Machine Kernel. Customer review data were obtained from Twitter, totaling 1000 tweets using the Rapid Miner and R library tools. The preprocessing stages applied were cleansing, case folding, tokenizing, word conversion, stopword, and stemming. The results of data visualization are presented in the form of a word cloud which is categorized based on positive and negative opinions of words that often appear. The results showed that the application of the Support Vector Machine Kernel Linear algorithm is better than the Naïve Bayes Classifier algorithm with an accuracy value of 82.11%, 76.44% precision, 88.01% recall, and an AUC value of 0.909.
DOMAIN ENCRYPTION BY SLICE AND SHIFT OPERATION: SAR 256

Symmetric key cryptography is the most commonly used cryptographic primitive in domain encryption that meets the requirement devices which are mostly used today.In the recent past a few such algorithms have been implemented to secure communication channels.In this paper, a domain encryption algorithm SAR 256 (a symmetric key cipher) with probabilistic slice and shift operation has been implemented.Developing a software-based domain encryption algorithm SAR 256 seems to be very beneficial for short length data encryption like node of wireless sensor networks (WSN), SSN, iNode of a file system, password, ATM PIN and other electronic data.The cipher SAR 256 is software-based synchronous ultra-lightweight cryptosystem which mainly design for resource-constrained devices such as Radio Frequency IDentification Devices (RFID) Tags, WSN, iNode, blockchain and other such platform with limited processing capabilities.The cipher SAR 256 inherits the hybrid feature of some well-known cryptographic primitives like Blowfish, Twofish, DES, StegoCrypt3D and IDEA.The cipher SAR 256 uses a variant length keystream ranging from bit long which is known to be safe and can withstand against several cryptographic attacks.It also uses a dynamic , stage slice and shift operation with an average to worst-case probabilistic vulnerability of . with the average plaintext length ⌊.⌋.However, in the best and the worst case, the state update function yields a probability of guess and determines attack as . and . respectively.The cryptosystem SAR 256 is implemented in 'C' with GCC Version 10.0.1 and has been tested under Xilinx ISE 14.2 with 1.8 GHz 8-core processor.
An Optical Model for Show-through Cancellation in Ancient Document Imaging with Dark and Bright Mounts

There is a need to make ancient paper documents from Asian countries such as Japan and China more readable. Many of them have writing or illustrations on both sides for reuse, which can show through the paper. In such documents, it is often difficult to distinguish just the front image because of the ink show-through from the back side. Our aim is to obtain clearer front images by removing the show-through from the back side. Hence, we propose an imaging method that uses dark and bright mounts as well as a prediction step based on a simple optical model. We successfully remove the show-through from the back and extract the images of the front of the document.
An Explainable Evaluation of Unsupervised Transfer Learning for Parallel Sentences Mining

The parallel sentences are known as very important resources for training cross-lingual natural language process applications, such as machine translation (MT) systems. However, these resources are not available for many low-resource language pairs. Existing methods mined parallel sentences using transfer learning. Although several attempts can get a good performance, they are not able to explain why transfer learning can help mining parallel sentences for low-resource language pairs. In this paper, we propose an explainable evaluation to quantity why transfer learning is useful for parallel sentence mining. Besides, we propose a novel unsupervised transfer learning that can maintain the robustness of transfer learning. Experiments show that our proposed method improves the performance of mined parallel sentences compared with previous methods in a standard evaluation set. In particular, we achieve good results at two real-world low-resource language pairs.
From the Greene--Wu Convolution to Gradient Estimation over Riemannian Manifolds

Over a complete Riemannian manifold of finite dimension, Greene and Wu introduced a convolution, known as Greene-Wu (GW) convolution. In this paper, we study properties of the GW convolution and apply it to non-Euclidean machine learning problems. In particular, we derive a new formula for how the curvature of the space would affect the curvature of the function through the GW convolution. Also, following the study of the GW convolution, a new method for gradient estimation over Riemannian manifolds is introduced.
A Review Paper on Image Authentication Scheme

Image authentication is one of the methods which can detect the any tampering data. The original grayscale document image is converted into stego image by adding the alpha channel plane. The stego image is in the Portable Network Graphics (PNG) format. This stego image is transmitted over the network. The authentication process is applied on the stego image on the receiver side. In the authentication process the data extracted from this stego image is compared with the data computed from the binary version of the stego image. If the data is matched the image is considered to be authentic. Else the tampered blocks are marked and the image is self-repaired using inverse secret sharing scheme.but in other methods they cannot resist the self substitution attack, the same-position-substitution attack, or the cut- off attack. Furthermore, those attacks can be completed by the popular image editing software Adobe Photoshop. We proposed security enhanced authentication scheme. Our proposed scheme is capable of repairing the content of the given stego-image if attacked by the methods mentioned above.
Upaya Peningkatan Prestasi Belajar Siswa pada Pembelajaran IPA tentang Benda dan Sifatnya melalui Penerapan Metode Eksperimen di Kelas III Sekolah Dasar

&lt;pre&gt;&lt;em&gt;This research generally aims to improve student learning achievement through the application of the Experimental Method in Mathematics Subjects Materials and Their Properties in class III SD Negeri Gununglarang 02 Academic year 2018/2019. In this study, it was carried out with two learning cycles and two meetings at each the cycle with research stages namely Planning, Implementation, Action and Reflection which focuses on increasing student achievement. The results of the research that has been carried out in the first cycle are as follows from 14 third grade students who experienced learning mastery only 8 students or 57% the remaining 6 students or 42% had not experienced learning mastery while the average grade of the class was 70.3. cycle II with the following results students who experienced mastery learning had reached 12 students or 86% while the remaining 2 students or 14% did not experience mastery learning with an average value of 76.0. It can be concluded from the two cycles that have been implemented that The application of the Experimental Method in learning science material objects and their properties in class III SD Negeri Gununglarang 02 proved effective because it can improve student achievement.&lt;/em&gt;&lt;/pre&gt;
Pandemic COVID-19 Effect on Future Courier Technologies

&#x0D; &#x0D; &#x0D; In Malaysia, when Movement Control Order (MCO) took place, many economic sectors are forced to shut down to control the pandemic. Most of the households are trapped in the house and trying to adapt to many new normal activities such as “work from home”, “virtual learning”, “online shopping” and many more. These new normal activities are increasing the demand of certain economic sectors such as Information and Communication Technology (ICT), digital banking, delivery services, etc.&#x0D; &#x0D; &#x0D;
Sarcasm Detection in Twitter -- Performance Impact when using Data Augmentation: Word Embeddings

Sarcasm is the use of words usually used to either mock or annoy someone, or for humorous purposes. Sarcasm is largely used in social networks and microblogging websites, where people mock or censure in a way that makes it difficult even for humans to tell if what is said is what is meant. Failure to identify sarcastic utterances in Natural Language Processing applications such as sentiment analysis and opinion mining will confuse classification algorithms and generate false results. Several studies on sarcasm detection have utilized different learning algorithms. However, most of these learning models have always focused on the contents of expression only, leaving the contextual information in isolation. As a result, they failed to capture the contextual information in the sarcastic expression. Moreover, some datasets used in several studies have an unbalanced dataset which impacting the model result. In this paper, we propose a contextual model for sarcasm identification in twitter using RoBERTa, and augmenting the dataset by applying Global Vector representation (GloVe) for the construction of word embedding and context learning to generate more data and balancing the dataset. The effectiveness of this technique is tested with various datasets and data augmentation settings. In particular, we achieve performance gain by 3.2% in the iSarcasm dataset when using data augmentation to increase 20% of data labeled as sarcastic, resulting F-score of 40.4% compared to 37.2% without data augmentation.
Explainable Deep Reinforcement Learning Using Introspection in a Non-episodic Task

Explainable reinforcement learning allows artificial agents to explain their behavior in a human-like manner aiming at non-expert end-users. An efficient alternative of creating explanations is to use an introspection-based method that transforms Q-values into probabilities of success used as the base to explain the agent's decision-making process. This approach has been effectively used in episodic and discrete scenarios, however, to compute the probability of success in non-episodic and more complex environments has not been addressed yet. In this work, we adapt the introspection method to be used in a non-episodic task and try it in a continuous Atari game scenario solved with the Rainbow algorithm. Our initial results show that the probability of success can be computed directly from the Q-values for all possible actions.
Norms and Policies for Agents on the Web

The study of policies and norms has a long tradition in the Semantic Web & Linked Data and Multi-Agent Systems communities, with applications ranging from business processes, and legal reasoning, to information systems governance. Although representing and reasoning about norms is crucial in ensuring that autonomous agents act in a manner to satisfy stakeholder requirements, normative concepts have yet to be considered as first-class abstractions in Web-based software systems. The chapter motivates the practical need to apply research on policies and norms to autonomous agents on the Web, and highlights research challenges and opportunities at the intersection of the Semantic Web & Linked Data, Multi-Agent Systems, and Web Architecture & Web of Things communities.
A new semi-supervised inductive transfer learning framework: Co-Transfer

In many practical data mining scenarios, such as network intrusion detection, Twitter spam detection, and computer-aided diagnosis, a source domain that is different from but related to a target domain is very common. In addition, a large amount of unlabeled data is available in both source and target domains, but labeling each of them is difficult, expensive, time-consuming, and sometime unnecessary. Therefore, it is very important and worthwhile to fully explore the labeled and unlabeled data in source and target domains to settle the task in target domain. In this paper, a new semi-supervised inductive transfer learning framework, named Co-Transfer is proposed. Co-Transfer first generates three TrAdaBoost classifiers for transfer learning from the source domain to the target domain, and meanwhile another three TrAdaBoost classifiers are generated for transfer learning from the target domain to the source domain, using bootstraped samples from the original labeled data. In each round of co-transfer, each group of TrAdaBoost classifiers are refined using the carefully labeled data. Finally, the group of TrAdaBoost classifiers learned to transfer from the source domain to the target domain produce the final hypothesis. Experiments results illustrate Co-Transfer can effectively exploit and reuse the labeled and unlabeled data in source and target domains.
LibGuides: CRJU U361 - Criminal Justice Research Methods: Internet Searching

This LibGuide is a work in progress designed to assist upper level Criminal Justice students with research