Reviewerly/exHarmony huggingface dataset:
Autism Gene Subset Selection from Microarray data – A Wrapper Approach

Autism spectrum disorder is a complex neurodevelopment disorder that affects an individual's social behavior. Microarray analysis is an extensively used technique to detect autism. Microarray data can provide additional insight into the etiology of the disorder. Identifying the specific set of genes associated with autism from complex microarray data poses a significant research challenge due to its high dimensionality. However, Gene subset selection is classified as an np-hard problem that can be handled by the meta-heuristic algorithm. In this paper, a novel meta-heuristic Game Theory Based Whale Optimization Algorithm is proposed. The proposed algorithm uses a two-person zero-sum game theory and convergence parameter to increase convergence rate and avoid local optima. The performance of the proposed algorithm is tested with 23 mathematical benchmark functions and compared with other state-of-the-art algorithms. Further, the proposed algorithm is employed as a wrapper-based gene subset selection model with a support vector machine. Furthermore, the outcomes demonstrate that the gene selection model utilizing a wrapper-based approach is capable of effectively identifying a subset of autism-related genes with desirable accuracy.
Image stitching method based on feature optimization and circular function weighted fusion

Abstract To address the issues of ghosting and chromatic gaps in parallax image stitching, this paper proposes a novel method based on feature optimization and circular function weighted fusion. The method employs the Scale-Invariant Feature Transform (SIFT) algorithm with dimensionality reduction optimization to extract point features, while the Grid-based Motion Statistics (GMS) algorithm is utilized to eliminate mismatched points. To further enhance the number of features, line features are also introduced. A local warping model is employed to guide the deformation of the grid image. Finally, a nonlinear fading-in and fading-out fusion model with circular function weighting is proposed for better image transition. Experimental results demonstrate that the proposed method is effective in dealing with ghosting and chromatic gaps compared to several existing methods.
Cross-modal multi-scale feature fusion-based RGB-T saliency object detection method

Abstract To cope with the challenge of significant target detection in complex scenes, this study proposes an RGB-T significant target detection method called CMFF. The method utilizes the complete potential of RGB and thermal infrared modal images and employs a codec structure and cross-modal multiscale feature fusion techniques. In the coding stage, two VGG16 backbone networks are used for multi-level feature extraction and CBAM attention module feature enhancement, and the enhanced features are fused using a stepwise fusion approach. Meanwhile, the weights of the two modalities are assigned using the L 1 -parametric fusion strategy to enhance the complementarity between them. In the decoding stage, global features are extracted from the high-level fused features by introducing the pyramid pooling module (PPM), and the low-level fused features are fused with multi-scale features in the up-sampling and encoding stages to enrich the global and local information of the feature map. Finally, this study conducted comparison experiments on the publicly available VT5000 dataset, and the method achieved an F-measure value of 0.863 and a mean absolute error (MAE) of 0.062, which significantly improved the overall detection performance relative to the six existing methods.
Drop edges and adapt: A fairness enforcing fine-tuning for graph neural networks

The rise of graph representation learning as the primary solution for many different network science tasks led to a surge of interest in the fairness of this family of methods. Link prediction, in particular, has a substantial social impact. However, link prediction algorithms tend to increase the segregation in social networks by disfavoring the links between individuals in specific demographic groups. This paper proposes a novel way to enforce fairness on graph neural networks with a fine-tuning strategy. We Drop the unfair Edges and, simultaneously, we Adapt the model’s parameters to those modifications, DEA in short. We introduce two covariance-based constraints designed explicitly for the link prediction task. We use these constraints to guide the optimization process responsible for learning the new ‘fair’ adjacency matrix. One novelty of DEA is that we can use a discrete yet learnable adjacency matrix in our fine-tuning. We demonstrate the effectiveness of our approach on five real-world datasets and show that we can improve both the accuracy and the fairness of the link prediction tasks. In addition, we present an in-depth ablation study demonstrating that our training algorithm for the adjacency matrix can be used to improve link prediction performances during training. Finally, we compute the relevance of each component of our framework to show that the combination of both the constraints and the training of the adjacency matrix leads to optimal performances.
Exploration Involving the Community in Upgrading Water Intake in Kampung Bongol, Tamparuli, Sabah, Malaysia

This research focuses on an important project to upgrade the water supply system for a rural area in Kampung Bongol, Tamparuli, Sabah, Malaysia, with exceptional involvement of the communities. This village is located approximately 60 km away from Kota Kinabalu town center, and the journey takes around two hours by car. Despite its remote location and the challenging geographical surroundings of mountains, the village is conveniently situated near an existing catchment area. However, this village's current water distribution setup relies on outdated and inadequate tools, systems, and facilities, leading to water scarcity issues, particularly during drought periods. To address this problem effectively, a sustainable design consisting of a mini dam and a ramp pump was developed and implemented in this study. Combining these two elements ensures the rapid filling of the storage distribution tank and the provision of clean water to the residents. One noteworthy feature of this project is the utilization of a 2-inch Polyvinyl chloride (PVC) ramp pump, significantly reducing operational costs and eliminating theneed for fossil fuels. This design not only proves to be practical and sustainable but also encourages the active participation of the villagers. Despite financial constraints and technical challenges associated with the project's implementation in a remote location, the water upgrading initiative was completed within four months, thanks to the direct involvement of the community. As a result of this endeavour, a fully functional water distribution network using the newly designed system has been installed, ensuring a reliable water supply for the 200 villagers in Kampung Bongol.
Mendeley untuk Semua: Sosialisasi Penggunaan Mendeley untuk Penulisan Akademik dan Non Akademik

This article evaluates the role of the reference management application Mendeley, in facilitating citation and information management in the digital age. Mendeley demystifies this complex citation process, making educating the public about its use essential. As part of this effort, the Faculty of Administrative Sciences at Brawijaya University conducted a Zoom-based training program on August 9, 2021, aimed at enhancing the public's skills in using Mendeley. The program aims to assist the public in utilizing technology to manage and cite information for both academic and non-academic purposes. This article details this program's process, outcomes, and impact and demonstrates how Mendeley can improve public access and capability in information management.
PENGARUH METODE BERNYANYI TERHADAP KEMAMPUAN MEMBACA AWAL PADA ANAK USIA 5-6 TAHUN DI TK YAMASSA SURABAYA

The ability to read is an important foundation in opening up opportunities for a child's future, because it is the first step in mastering knowledge and knowledge. Therefore, teaching reading, especially early reading, can be done at the Kindergarten (TK) level. Observations made at the Yamassa Kindergarten in Surabaya showed that group B children were less interested in learning to read and 6 out of 11 children still could not read. To be able to improve children's early reading skills, educators must pay attention to children's competencies and consider methods that children will like so that learning is effective, such as the singing method. The purpose of this study was to determine whether or not there was an influence of the singing method to improve early reading skills in children aged 5-6 years at Kindergarten Yamassa Surabaya. This study uses a quasi-experimental research type and uses a non-equivalent control group design which uses a pretest and posttest given to the experimental group and the control group. Data collection techniques in this study are observation, interviews, tests, and documentation. This study used the Mann Whitney U Test and the results showed that there was a significant effect of the singing method on early reading skills in children aged 5-6 years at Kindergarten Yamassa Surabaya.
BREAST CANCER CLASSIFICATION ON ENHANCED SEGMENTED MAMMOGRAMS USING OPTIMIZED CONVOLUTIONAL NEURAL NETWORKS

Breast cancer ranks as the second most common malignancy among women and the second-most common reason for cancer deaths worldwide. Digital Mammogram screening can offer low-cost early diagnosis and reduce the breast cancer fatality rate among victims. This research aims to build a model that automatically assists in classifying malignant and benign lesions depicted on digital mammograms without any human interventions. The Mammographic Image Analysis Society (mini-MIAS) image dataset, which contains 322 mammograms, is employed in the present study. This research focuses on the Background Preserved and Feature-Oriented Contrast Improvement (BPFO-CI) method for contrast enhancement that uses the Weighted Cumulative Distribution Function. The Region of Interest (RoI) is then extracted from the improved mammograms using the Thresholding Segmentation method. Then extracted RoIs are used as input for classification using optimal Convolutional Neural Networks (CNN). Data augmentation is applied to the pre-processed dataset. The suggested pre-processed CNN model's performance is compared to various classification algorithms in pertaining to accuracy and confusion matrix. The simulation results confirm the importance and effectiveness of the suggested model in comparison to other well-known conventional approaches. As a result, this classification method is predicted to aid in the diagnosis of breast cancer.
Pemahaman Modul dan Implementasi Transaksi Akuntansi Pada Software PT. Hashmicro Solusi Indonesia

The internship process at PT Hashmicro Solusi Indonesia which is carried out through MBKM activities can create flexible learning in higher education. Internship program activities can also build an innovative learning culture and provide the knowledge and skills needed by students in facing the competitive world of work in accordance with the times, technological advances, and societal dynamics. The internship program activities carried out at PT Hashmicro Solusi Indonesia's Accounting Specialist division are focused on carrying out activities to understand Hashmicro's ERP modules related to accounting and financial reports, handling client issues, conducting meetings with clients related to discussing issues, analyzing the implementation of transactions related to accounting on Hashmicro ERP Software, making work reports in PDF document form on accounting transaction work practices, and presenting work results to mentors on accounting transaction work practices. Internship program activities produce several things such as knowledge, attitudes, general skills, and special skills. Knowledge of software such as functions, usability, and how to operate menus and submenus. Attitudes acquired regarding how to serve and deal with clients properly and correctly. General skills in terms of being able to work with teams, being able to think critically, and solving problems quickly and precisely. As well as special skills obtained such as being able to understand client accounting transactions and provide the desired output from the client from the Hashmicro ERP software.
The Strategies of the High-performing Non-native English Language Readers with Structurally Difficult Texts

This study focused on the activities and strategies that high-performing non-native English language readers applied in coping with structurally difficult texts and how such difficulty triggered them to work beyond their threshold.The study used a single-group design involving fifty-seven high-performing non-native English language readers from a population of third-semester diploma students who were selected to undergo nine levels of reading tests.The participants were required to provide feedback on the activities they had taken to overcome the difficulties.Based on the reflective feedback, fourteen coping activities, with repeated reading being the most popular, were named.The participants' activities were then categorised into five strategies based on Lazarus and Folkman's classification of coping strategies.The self-control strategy was found to be the most widely used.The study also found that different strategies worked well for varying difficulty levels.For this reason, it was suggested that readers should be exposed to various activities and strategies so that they would be able to apply them effectively.This would enable them to move beyond their threshold as they are equipped to cope with the difficulties.
Time Series Prediction For The Housing Construction Market With The Use Of NARNet

The paper discusses the study to develop and tune parameters of a nonlinear autoregressive neural network (NARNet or NARNN) model for predicting the number of housing units. Predictions were made for the housing construction market in Poland, which is a dynamically growing European market. Three stages of the housing construction process have been taken into considerationThe paper discusses the study to develop and tune parameters of a nonlinear autoregressive neural network (NARNet or NARNN) model for predicting the number of housing units. Predictions were made for the housing construction market in Poland, which is a dynamically growing European market. Three stages of the housing construction process have been taken into consideration: permits issued for house construction, houses under construction, and completed new houses. Experimental results have shown that a NARNet model can be a very effective tool in the considered scenario. A network model using the Levenberg-Marquardt backpropagation training function achieved the best model fit, as well as the most accurate one-month predictions.
Cost- And Performance-Based Evaluation Of Cloud-Based Disaster Recovery

Cloud platforms offer not only the capacity to facilitate effective and scalable services for third-party applications and business solutions, but also present an opportunity to implement intricate disaster recovery strategies. For instance, a Chief Technical Officer may opt to maintain operations on private systems in order to effectively manage costs, privacy, and security, while simultaneously leveraging the cloud as an autonomous and immediate disaster recovery support. This can be achieved by building a secondary leg of the IT system that functions as an online cold or hot spare, manages workload peaks, or handles a portion of the workload under normal conditions. To assess the effectiveness and cost-effectiveness of such solutions, appropriate models are essential to examine the trade-offs and explore the parameter space of possible alternatives. This paper presents a modeling approach for the design and evaluation of cloud-based recovery setups.
Phd Clinic and Knowledge Endowment: A Smart, Online Partnership Between Academic Library and Postgraduate Academic Consultants During The Covid 19 Pandemic

Research services have long been referred to as personal research assistance given to library patrons by library staff.The services have been expanding tremendously since the beginning of the 21st century, and abruptly shifting to fully digital, and online activities during the COVID-19 crisis.One of the newly launched, face-to-face consultation services is forced to be operated completely online.The aim of establishing this service to support postgraduate students who have difficulty in understanding or research, or academic writing that does not meet the prescribed language standards.The PhD Clinic begins with two consultants who are faculty members in 2017.Eventually, in 2022, PhD Clinic is proud to announce the commitment of four research consultants who involve under a voluntary service, known as Knowledge Endowment (Waqaf Ilmu).The clinic initially offers in-person advice and academic assistance.However, the crisis prompts the service to go for online consultations which gives rise to customers seeking assistance.Hence, this paper is an attempt to showcase the joint effort of university libraries and faculty members.Due to the COVID 19 epidemic, the endeavor has moved from face-to-face, local gatherings to online, national meetings throughout Malaysia.At present, PhD clinic is seen as an important service to bridge the
Blockchain Technology a Structural Shifts in Banking Sector: Consumer-Oriented Measures

This study reflects on the possibilities of banking institutions using blockchain technology, focusing on empowering the position of consumers and market players in Malaysia the banking sector.The technology advantages, risks and limitations are critically reviewed in order to understand how the technology works, but also how the technology provides the added values for consumer and other stakeholders in banking sector.While still an experimental technology, scholars and bankers argued that using blockchain could enhance innovations in the operational and delivery.Among the challenges of blockchain technology includes disparities and ambiguities in present fintech regulations and other policies warrant the analysis on current blockchain practices in banking delivery, cybersecurity challenges and the potential for fraudulent and criminal activities throughout blockchain.This study employed purely doctrinal research and qualitative analysis.The overall studies involved analysis of blockchain technology in terms of speed, functionality, security and accessibility.This study also explored the approach adopted by UK and US and approach to ensure blockchain technology are consumer -oriented and cyber securities concerns to address blockchain shortcomings and potential challenges to financial consumer welfare and other related policies.The findings revealed that blockchain is a promising financial technology that could eliminate plausible fraud transactions by making the transactions immutable, verifiable and traceable.However, this disruptive innovation shows several disruptions in banking traditional delivery system for example fluctuate costs, ambiguous decentralization structure vulnerable to anti-competitive practices under competition law and create risks to money laundering activities.
Multi-party Cooperative Network Fault Management Mechanism based on Federated Learning

Virtualization, SDN, NFV and other technologies have significantly improved the utilization of network resources, while bringing more dynamic characteristics to the network. In order to solve the problem of low efficiency of fault management in the environment of multiple network providers, a network fault management platform and mechanism based on federated learning is designed. First of all, according to the composition and characteristics of networks in SDN and NFV environments, a network model of multiple network providers is designed. Secondly, in order to improve the service quality of the service function chain, a multi-network provider negotiation fault management platform architecture is proposed. Finally, in order to achieve efficient cooperation among multiple network providers, a multi-party cooperative fault management mechanism based on federated learning is proposed, and the steps for remote nodes to obtain training data are designed in detail. In the performance analysis part, from the implementation feasibility and mechanism performance of the fault management mechanism, it is verified that the fault management platform and mechanism in this paper have good performance and enforceability.
Lagash Archaeological Survey and Recording System (LASRS) - Importing ArcGIS Field Maps Data to QGIS v1

This protocol describes how data collected with ArcGIS Field Maps, using forms generated according to the Rectangular Survey Grid Creation with QGIS and Creating Collection Forms with ArcGIS Field Maps protocols, can be imported into QGIS and combined with tabular data of the collected artifacts (such as counts and weights) for further analyses such as the creation of heat maps.
Pemberdayaan Masyarakat melalui Program Babari sebagai Metode Literasi

Community empowerment activities through the BABARI program as a creative digital literacy method aimed at the community from the elementary, middle, and high school education levels are carried out once a week, and the results of reading and reviewing their writings will be published on the website. The method used is Participatory Rural Appraisal (PRA). This activity aims to help people to love reading and not be left behind by advanced technology. For this reason, BABARI was programmed as a form of digital literacy assistance to the community in Kampung Warung Jogjog. This activity received a positive response from the community because it became a need for young people to add insight into knowledge and skills in the field of reading and writing. In addition, it is hoped that this program will continue to grow and that donors from the Education Office, Library, and Archives Service are needed to add books and technological equipment as additional support for their digital literacy activities.
Pendampingan Belajar Kosakata Bahasa Inggris di Panti Asuhan Pondok Kasih: Pengembangan Media Pembelajaran

This article discusses English vocabulary learning medium for young learners through songs and stories inspired by the implementation of a community service program at the Pondok Kasih Orphanage, Salatiga. It aims to attract interest and motivate children to learn English. Research and Development method is used for the development of 4D (Define, Design, Develop, and Dissemination) by observation and documentation as the data collection techniques. The existing data are analyzed descriptive qualitatively. English Vocabulary media developed in this program is in the form of an audiobook which contains Indonesian children's songs that are translated into English and created in the form of stories. This medium is needed to attract and motivate children in learning English vocabulary in orphanages and young children learning communities.
Pendampingan Belajar Speaking bagi Anak di Oemah Ngaji, desa Argosari, Turi, Sleman, Yogyakarta

Speaking skills, especially English, are important for all people, especially children, as a support for various aspects of future life. In teaching language, one of the factors supporting the success of teaching English to children is the use of media, namely songs. The purpose of this service is to provide speaking learning assistance to children at Oemah Ngaji, Argosari village, Turi, Sleman. This service is carried out by providing learning assistance to grade 3-5 elementary school students who are also Oemah Ngaji students through the media of songs broadcast in video form, followed by learning assistance using worksheets, and evaluation using simple games. The result of this activity is that children enthusiastically participate in mentoring activities and looked very comfortable during material activities or discussions. Enthusiasm in participating in mentoring activities is very important to increase motivation to learn speaking further.