Reviewerly/exHarmony huggingface dataset:
Optimizing Communication Topology for Collaborative Learning Across Datacenters

Federated learning (FL) is emerging as an increasingly important and popular paradigm for collaboratively training high-quality machine learning (ML) models over massive amounts of data stored by geo-distributed datacenters. However, the communication efficiency of gradient aggregation during the training process comes as a primary bottleneck that impedes the adoption of FL, especially in cross-silo settings, as the available bandwidth of inter-datacenter links connecting data silos is often very limited. To improve the training efficiency of cross-silo FL between datacenters, we propose TopoAdopt, an efficient communication topology design for gradient aggregation to overcome the communication bottleneck of cross-silo model training. TopoAdopt uses multiple aggregators to share aggregation load and tree-based hierarchical aggregation to reduce bandwidth consumption from clients to aggregators. For better performance, it jointly optimizes the parameter assignment among aggregators and the construction of aggregation trees. We formulate this optimization problem as a mixed-integer nonlinear programming model and develop efficient algorithms to find satisfactory communication topologies in reasonable computational time. The experimental results show that TopoAdopt achieves significant speedup, up to 5.2 $$\times $$ , in gradient aggregation completion time compared to existing solutions.
Post quantum blockchain architecture for internet of things over NTRU lattice

The Internet of Things (IoT) and blockchain, the hottest frontier technologies in recent years, are expected to lead the next technological revolution. Blockchain promises to solve the current challenges encountered by the IoT. However, most of the proposed blockchain-based IoT architectures, which are based on discrete logarithm or large integer factorization problems, are susceptible to quantum attacks. Several quantum-resistant blockchain schemes have recently been proposed. However, the efficiency of their construction or the equipment required is not satisfactory. In this paper, to construct a more efficient postquantum blockchain infrastructure, we propose blockchain architecture for the IoT over the NTRU lattice and provide a cryptographic security proof of the scheme. Attributed to the more efficient underlying lattice structure, our scheme has excellent performance when compared to the existing quantum-resistant blockchain scheme, and we reduce the transaction size from hundreds of megabytes to several kilobytes. To further improve the blockchain’s performance, we present the general framework of segregated witnesses and aggregate signatures over the NTRU lattice. Our scheme promises a blockchain solution for resource-constrained environments.
Kesiapan perpustakaan SMA Muhammdiyah 2 Bandar Lampung dalam pengembangan website E-Perpustakaan

Information technology in libraries is applied to meet the needs of users in obtaining various information quickly, precisely, and accurately. A website-based library is one form of information technology support to optimize the use of library information and materials, as well as optimize the substantive work of librarianship, starting from the procurement, processing, circulation of library materials, retrieval and information dissemination. So that in its implementation, a readiness from various aspects is needed so that the system can run optimally in meeting user needs. The objectives to be achieved in this research are to find out the readiness of the Muhammadiyah 2 Senior High School library Bandar Lampung in developing the E-Library website, as well as to find out what inhibiting factors are in developing the E-Library website. The writing method uses qualitative-descriptive data and data collection techniques are carried out through observation, interviews, documentation, and literature review which are then processed by data analysis techniques. The results of this paper indicate that the readiness of the Muhammadiyah 2 Senior High School library Bandar Lampung in developing the E-Library website has not been fully maximized, to be able to develop a library automation system there are still obstacles such as the librarians who are there not yet fully able to develop and operate the website E-Libraries, there is still an inadequate internet network access, and the menu features on the E-Library website are still incomplete. So there is still a need for improvement in preparing the automation system in the library of Muhammadiyah 2 Senior High School Bandar Lampung, such as improving the menu feature system, internet network, and human resources so that the development process is more optimal.
A GIS-based interactive map enabling data-driven decision-making in Nigeria's food supply chain

The accessibility of open-source data on fresh food supply chains provides key stakeholders from the public and private sectors with insights for better decision-making to drive food loss reduction. Nigeria has a fair amount of open-source agricultural and climate-related data. However, most of these datasets are not readily accessible. This paper presents a detailed method used to develop an interactive web Geographic Information System (GIS) tool that collates and visualizes available open-source datasets on Nigeria's Agricultural Sector with particular focus on the fresh produce supply chains. The following steps were used to create such an interactive map.•Open-source data were acquired in various forms, including tabular, vector, and rasters, processed and uploaded as layers on the interactive web map.•Most of the data needed some processing on open-source geographic information system applications and web-based computing platforms to transform them into sources of actionable insights•These final processed layers were then uploaded to a consolidated interactive web map built on the Google Earth Engine platform.The gathered open-source data includes crop production data, market prices, weather, road network, market locations, mobile coverage, water access, water scarcity, and food insecurity. The method described here also allows the reproduction of such maps for other countries.
Marketing of electronic resources by universities and colleges in Tanzania

Purpose This study aims to evaluate Web-based tools that are potentially used by universities and college libraries to market e-resources in Tanzania. Design/methodology/approach This study evaluated Web-based tools that are potentially used to market e-resources in libraries in Tanzania. A sample of 52 universities and colleges was approved by Tanzania Commission for Universities and was purposively selected for this study. The evaluation was conducted with an established checklist to evaluate the websites with a view to identifying Web-based tools, which are potentially useful in marketing e-resources. Findings The finding of this study indicates that most libraries have websites linked to their institutional websites. The finding has revealed that social media are mostly used by public universities to market e-resources. However, only one university is using WhatsApp to communicate with patrons. The findings show that the majority of public university libraries display a list of e-resources on their websites. Moreover, the displayed e-resources of most websites are not up to date, and some of them are irrelevant. Also, most public universities provide guidelines on how to use the subscribed e-resources as opposed to their counterpart. Originality/value The study on the evaluation of Web-based e-resources marketing tools is new in Tanzania. The finding of this study will trigger efforts to adjust the way libraries market their e-resources online.
Optimization of the path planning for the robot depending on the hybridization of artificial potential field and the firefly algorithm

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Noor Alhuda F. Abbas; Optimization of the path planning for the robot depending on the hybridization of artificial potential field and the firefly algorithm. AIP Conference Proceedings 2 February 2023; 2457 (1): 040004. https://doi.org/10.1063/5.0118693 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Agile Requirements Engineering Practices: A Survey in Brazilian Software Development Companies

Requirements Engineering (RE) is one of the prime areas in software development. Since agile software development englobes several emerging techniques and advocates for continuous improvement, it urges the question of which agile RE practices are currently most used, their characteristics, and the challenges in their employment. The aim of this work is to investigate and categorize the collection and specification of agile requirements practices based on how professionals perceive their importance for a software project that applies agile methodologies. Thus, a survey was carried out with forty-six (46) Brazilian software development professionals, inquiring which methods are used for the collection and specification of agile requirements, as well as the features, benefits, and difficulties when employing the methods. The responses allowed us to perform data analysis and identify the relationships between the respondents’ experience and the viewpoints on the collection methods and the agile requirements specification. In addition, it was noted that the adoption of these methods is still very recent. They have mainly been used for less than five years. Moreover, it was noted that, for most respondents, there are yet significant challenges and advances to be made for better efficiency in applying the informed methods.
A Comparative Study of CatBoost and Double Random Forest for Multi-class Classification

Multi-class classification has its challenge compared to binary classification. The challenges mainly caused by the interactions between explanatory and responses variable are increasingly complex. Ensemble-based methods such as boosting and random forest (RF) have been proven to handle classification problems. We conducted this research to study multi-class classification using CatBoost, a method developed with gradient boosting and double random forest (DRF), RF’s development that is good to be used when the resulting RF model is underfitting. Analysis was carried out using simulation and empirical data. In the simulation study, we generate data based on the distance between classes: high, medium, and low. The empirical data used is the industrial classification code, namely KBLI. CatBoost and DRF can rightly solve the multi-class classification problem at a high distance, measured by a 100% balanced accuracy score. At a medium distance, CatBoost and DRF produce balanced accuracy scores of 99.25% and 97.54%, respectively, whereas 32.37% and 23.97% at the low distance. In empirical studies, CatBoost’s performance outperforms DRF by 4.27%. All the differences are statistically significant based on the t-test result. We also use LIME to explain individual predictions of CatBoost and learn words that contribute the most to an example class’s prediction.&#x0D;
Object Tracking with Multiple Dynamic Templates Updating

Most existing Siamese visual trackers see the object tracking task as similarity learning between a search image and a single template image. Utilizing only one template leads to the negligence of the rich semantic information in other frames. Meanwhile, those Siamese trackers with temporal context exploitation either incorporate specially designed non-generic modules or include online-learning parts which compromise real-time performance. In this paper, we propose a novel model architecture incorporating multiple dynamic templates in a Siamese visual tracker to maximize temporal information utilization. To attain a favorable appearance representation from these templates, we propose an online dynamic template pool updater that leverages the frames with dissimilar appearances. Furthermore, we design a new hard positive sampling strategy to train the tracker with dissimilar templates. With the proposed methods, a Siamese tracker can be straightforwardly transformed and trained to benefit from the temporal correlations among frames. Comprehensive experiments on various tracking datasets show positive results and prove the effectiveness of the proposed methods.
Nonlinear autoregressive neural network with exogenous input for an energy efficient non-cooperative target tracking in wireless sensor network

The prediction algorithms have been studied as a part of target tracking applications for many years. The prediction algorithm helps to select appropriate nodes to achieve precise target locations while tracking. The only group of sensor nodes nearer the predicted location is activated to save network energy. The inaccurate prediction algorithm may hamper energy consumption by activating inappropriate nodes resulting in a target loss. We propose a nonlinear autoregressive neural network with exogenous input (NARX)-based target-tracking algorithm that improves tracking accuracy and energy efficiency. The proposed algorithm uses vehicle location time series and exogenous vehicle velocity time series as inputs and exerts accurate prediction location for given non-cooperative manoeuvring targets. The proposed algorithm is evaluated in terms of average prediction error, total network energy used, and the count of a target loss with state of art. The experiment outcome proves that the proposed novel NARX-based tracking algorithm outperforms and saves up to 26% of network energy with up to 83% reduction in tracking error compared to existing target tracking algorithms.
Self-Supervised Texture Image Anomaly Detection by Fusing Normalizing Flow and Dictionary Learning

A common study area in anomaly identification is industrial images anomaly detection based on texture background. The interference of texture images and the minuteness of texture anomalies are the main reasons why many existing models fail to detect anomalies. We propose a strategy for anomaly detection that combines dictionary learning and normalizing flow based on the aforementioned questions. The two-stage anomaly detection approach that is already in use is enhanced by our method. In order to improve baseline method, this research adds normalizing flow in representation learning and combines deep learning and dictionary learning. Improved algorithms have exceeded 95[Formula: see text] detection accuracy on all MVTec AD texture type data after experimental validation. It shows strong robustness. The baseline method’s detection accuracy for the Carpet data was 67.9[Formula: see text]. The paper was upgraded, raising the detection accuracy to 99.7[Formula: see text].
Optimization Silhouette Coefficient on Genetic Algorithm for Clustering in Auto Correction German Learning

Teaching, especially language, is very difficult; for example, speaking needs more effort. Teaching the German language need more method and tools for the student. Some tools had used in teaching language for correction, but it has a limit for the database in vocabulary. This research made a tool for helping teachers in the German language that has correct. The tools can improve the ability of teachers and students through assessment correction. The part of the application evaluates students and teachers who can speak German. This research used a genetic algorithm to classify the correction of the German language assessment. This algorithm was used to classify the correction of assessment tasks in the German language. The genetic algorithm plays a role in optimizing the initial centre of the cluster in the K-Means algorithm. The data used in this study is data from lecturers of the German language study program at Universitas Negeri Malang in 2020. The data was obtained from SIPEJAR online learning at the German Program, Universitas Negeri Malang. The results of the clustering test of students and lecture in the German language based on the correction of the assessment using the Silhouette Coefficient-GA method has a higher cluster quality of 3.24% compared to the K-Means algorithm without the genetic algorithm.
Qluster: An easy-to-implement generic workflow for robust clustering of health data

The exploration of heath data by clustering algorithms allows to better describe the populations of interest by seeking the sub-profiles that compose it. This therefore reinforces medical knowledge, whether it is about a disease or a targeted population in real life. Nevertheless, contrary to the so-called conventional biostatistical methods where numerous guidelines exist, the standardization of data science approaches in clinical research remains a little discussed subject. This results in a significant variability in the execution of data science projects, whether in terms of algorithms used, reliability and credibility of the designed approach. Taking the path of parsimonious and judicious choice of both algorithms and implementations at each stage, this article proposes Qluster, a practical workflow for performing clustering tasks. Indeed, this workflow makes a compromise between (1) genericity of applications (e.g. usable on small or big data, on continuous, categorical or mixed variables, on database of high-dimensionality or not), (2) ease of implementation (need for few packages, few algorithms, few parameters, ...), and (3) robustness (e.g. use of proven algorithms and robust packages, evaluation of the stability of clusters, management of noise and multicollinearity). This workflow can be easily automated and/or routinely applied on a wide range of clustering projects. It can be useful both for data scientists with little experience in the field to make data clustering easier and more robust, and for more experienced data scientists who are looking for a straightforward and reliable solution to routinely perform preliminary data mining. A synthesis of the literature on data clustering as well as the scientific rationale supporting the proposed workflow is also provided. Finally, a detailed application of the workflow on a concrete use case is provided, along with a practical discussion for data scientists. An implementation on the Dataiku platform is available upon request to the authors.
Reading bots: The implication of deep learning on guided reading

This study introduces the application of deep-learning technologies in automatically generating guidance for independent reading. The study explores and demonstrates how to incorporate the latest advances in deep-learning-based natural language processing technologies in the three reading stages, namely, the pre-reading stage, the while-reading stage, and the post-reading stage. As a result, the novel design and implementation of a prototype system based on deep learning technologies are presented. This system includes connections to prior knowledge with knowledge graphs and summary-based question generation, the breakdown of complex sentences with text simplification, and the auto-grading of readers' writing regarding their comprehension of the reading materials. Experiments on word sense disambiguation, named entity recognition and question generation with real-world materials in the prototype system show that the selected deep learning models on these tasks obtain favorable results, but there are still errors to be overcome before their direct usage in real-world applications. Based on the experiment results and the reported performance of the deep learning models on reading-related tasks, the study reveals the challenges and limitations of deep learning technologies, such as inadequate performance, domain transfer issues, and low explain ability, for future improvement.
Predicting type 2 diabetes prevalence for people with severe mental illness in a multi-ethnic East London population

Prevalence of type two diabetes mellitus (T2DM) in people with severe mental illness (SMI) is 2–3 times higher than in general population. Predictive modelling has advanced greatly in the past decade, and it is important to apply cutting-edge methods to vulnerable groups. However, few T2DM prediction models account for the presence of mental illness, and none seemed to have been developed specifically for people with SMI. Therefore, we aimed to develop and internally validate a T2DM prevalence model for people with SMI. We utilised a large cross-sectional sample representative of a multi-ethnic population from London (674,000 adults); 10,159 people with SMI formed our analytical sample (1,513 T2DM cases). We fitted a linear logistic regression and XGBoost as stand-alone models and as a stacked ensemble. Age, sex, body mass index, ethnicity, area-based deprivation, past hypertension, cardiovascular diseases, prescribed antipsychotics, and SMI illness were the predictors. Logistic regression performed well while detecting T2DM presence for people with SMI: area under the receiver operator curve (ROC-AUC) was 0.83 (95 % CI 0.79–0.87). XGBoost and LR-XGBoost ensemble performed equally well, ROC-AUC 0.83 (95 % CI 0.79–0.87), indicating a negligible contribution of non-linear terms to predictive power. Ethnicity was the most important predictor after age. We demonstrated how the derived models can be utilised and estimated a 2.14 % (95 %CI 2.03 %-2.24 %) increase in T2DM prevalence in East London SMI population in 20 years’ time, driven by the projected demographic changes. Primary care data, the setting where prediction models could be most fruitfully used, provide enough information for well-performing T2DM prevalence models for people with SMI. We demonstrated how thorough internal cross-validation of an ensemble of a linear and machine-learning model can quantify the predictive value of non-linearity in the data.
Quantifying semantic relatedness across base verbs and derivatives: English out-prefixation

Most authors on derivational semantics agree that the meaning of complex words builds on components contributed by the base, components contributed by the word formation process, as well as contextual knowledge components.There is far less agreement on how these components interact or on their relative contributions.In this paper, we approach the question of relative contributions by looking at relatedness between bases, between derivatives, as well as between bases and derivatives.We use the English scalar-comparative verb prefix out-as a test case.We show that classes of bases serve as predictors for the resolution of systematically underspecified parts of the semantics of outderivatives.By means of distributional similarity measures, we also show that the word formation process's derivatives exhibit a high degree of uniformity relative to the similarities between other components of the process.Finally, distributional measures for four further prefixes suggest that this rich contribution of the affix is peculiar to scalar-comparative out-rather than a characteristic of prefixation in general.