Reviewerly/exHarmony huggingface dataset:
AsaPy: A Python Library for Aerospace Simulation Analysis

AsaPy is a custom-made Python library designed to simplify and optimize the analysis of simulation data. It offers a range of features, including the design of experiment methods, statistical analysis techniques, machine learning algorithms, and data visualization tools. AsaPy's flexibility and customizability make it a viable solution for engineers and researchers who need to quickly gain insights into constructive simulations. AsaPy is built on top of popular scientific computing libraries, ensuring high performance and scalability. In this work, we provide an overview of the key features and capabilities of AsaPy, followed by an exposition of its architecture and demonstrations of its effectiveness through some use cases applied in military operational simulations. We also evaluate how other simulation tools deal with data science, highlighting AsaPy's strengths and advantages. Finally, we discuss potential use cases and applications of AsaPy and outline future directions for the development and improvement of the library.
Intelligent medical IoT health monitoring system based on VR and wearable devices

Abstract In order to improve the shortcomings of the traditional monitoring equipment that is difficult to measure the daily physical parameters of the elderly and improve the accuracy of parameter measurement, this article designs wearable devices through the Internet of Things technology and virtual reality technology. With this device, four daily physical parameters of the elderly, such as exercise heart rate, blood pressure, plantar health, and sleep function, are measured. The feasibility of the measurement method and equipment is verified by experiments. The experimental results showed that the accuracy of the measurement method based on the reflective photoplethysmography signal was high, with the mean and difference values of the subjects’ heart rate basically lying around 0 BPM and in good agreement between the estimated heart rate and the reference value. In the blood pressure measurements, the correlation coefficient between the <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:msub> <m:mrow> <m:mi>P</m:mi> </m:mrow> <m:mrow> <m:mo>r</m:mo> <m:mo>s</m:mo> </m:mrow> </m:msub> </m:math> {P}_{rs} estimate and the reference value was 0.81. The estimation accuracy of the device used in the article was high, with the highest correlation coefficient of 0.96 ± 0.02 for subjects’ heart rate at rest, and its estimation error rate was 0.02 ± 0.01. The <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:msub> <m:mrow> <m:mi>P</m:mi> </m:mrow> <m:mrow> <m:mi mathvariant="italic">n</m:mi> <m:mi>t</m:mi> <m:mi>h</m:mi> </m:mrow> </m:msub> </m:math> {P}_{{n}th} value for subject B8 exceeded the threshold of 0.5 before subject B21, and subject B8 had more severe symptoms, which was consistent with the actual situation. The wearable device was able to identify the subject’s eye features and provide appropriate videos to help subjects with poor sleep quality to fall asleep. The article provides a method and device that facilitates healthcare professionals to make real-time enquiries and receive user health advice.
Certified Robustness via Dynamic Margin Maximization and Improved Lipschitz Regularization

To improve the robustness of deep classifiers against adversarial perturbations, many approaches have been proposed, such as designing new architectures with better robustness properties (e.g., Lipschitz-capped networks), or modifying the training process itself (e.g., min-max optimization, constrained learning, or regularization). These approaches, however, might not be effective at increasing the margin in the input (feature) space. As a result, there has been an increasing interest in developing training procedures that can directly manipulate the decision boundary in the input space. In this paper, we build upon recent developments in this category by developing a robust training algorithm whose objective is to increase the margin in the output (logit) space while regularizing the Lipschitz constant of the model along vulnerable directions. We show that these two objectives can directly promote larger margins in the input space. To this end, we develop a scalable method for calculating guaranteed differentiable upper bounds on the Lipschitz constant of neural networks accurately and efficiently. The relative accuracy of the bounds prevents excessive regularization and allows for more direct manipulation of the decision boundary. Furthermore, our Lipschitz bounding algorithm exploits the monotonicity and Lipschitz continuity of the activation layers, and the resulting bounds can be used to design new layers with controllable bounds on their Lipschitz constant. Experiments on the MNIST, CIFAR-10, and Tiny-ImageNet data sets verify that our proposed algorithm obtains competitively improved results compared to the state-of-the-art.
Exploration of fibro-glandular region and breast density classification of digitised mammograms using least square support vector machine

Breast tissue density is one of the significant risk-marker for identification of breast cancer in early stage. In the proposed work, fibro-glandular region is explored and classification of breast density as dense and non-dense is performed. Image pre-processing is performed to improve the image quality followed by segmentation of breast region to obtain region of interest (RoI). For the obtained RoI, pseudo colouring is performed to improve image acuity accompanied by R-image extraction and post-processing to obtain fibro-glandular breast tissues. Area, histogram, fractal, grey-level co-occurrence matrix and grey-level run length matrix features are derived from both fibro-glandular and RoI regions and ratiometric value of features are computed. Further, mutual-information-based feature ranking algorithm is applied on the derived ratiometric values and the significant features are identified. These significant features when fed to least square-support vector machine produced average classification accuracy (%) of 86.1 ± 6.03 for mini-MIAS and 82.3 ± 4.78 for CBIS-DDSM database.
Research - based technology education – the EDURES partnership experience

The paper presents the state regarding practices in teaching at partner universities of the EDURES project and current approaches supporting research-based education. It also presents the needs of various groups of stakeholders formulated on the basis of survey results. Moreover, the paper proposes new methodologies which are useful if research results are implemented in teaching programmes at technical universities (research-based lectures, e-learning, web portal methodologies, wizards, etc). It is finally stated that research-results implementation into teaching processes is possible by the use of various tools and approaches proposed by the EDURES partnership and has a potential to be appreciated by students, academics and external stakeholders.
Use of augmented reality for small parts assisted assembly

The use of augmented reality (AR) for assisted assembly of small parts remains a current and dynamically developing issue in industry and the technology sector. Because AR technologies and related applications are constantly advancing, it is necessary to pay sufficient attention to this topic. This article provides a simplified view of software application design and architecture. We are focusing on a 45mm x 45mm x 42mm component consisting of 42 parts. In the article, we gradually show how to properly create an application with a main, and working environment in which we can create animations from a database of 3D models. In the end, we performed a product assembly test with and without the use of the AR application, for one piece and a production batch. In conclusion, we can state that the applications have an impact on the training of the workforce and the ways through which new workers are prepared to work in an industrial environment. Improved education and training programs based on AR can help integrate new workers into production processes faster and more efficiently. Considering these perspectives and the dynamics of the AR technologies development, our article raises awareness of current trends and innovations in this field, as it can have a significant impact on the efficiency and competitiveness of various industries.
AdaDpFed: A Differentially Private Federated Learning Algorithm With Adaptive Noise on Non-IID Data

The popularity of emerging consumer electronics, such as Mobile phones, PADs, and various smart home appliances, brings unprecedented convenience to people. Currently, how to obtain targeted information without revealing personal privacy is the new challenge with the application of precision recommendation and other Artificial Intelligence technologies in consumer electronics. Several solutions, based on Federated Learning transfer and update models by intermediate parameters instead of individual data, can perform some recommending tasks with weak security. However, they still face various attacks like model inversion attacks and membership inference attacks, especially in the complex non-independent and identically distribution (non-IID) environment. In this paper, we propose AdaDpFed, an adaptive federated differential private protocol in the non-IID setting. AdaDpFed can adaptively adjust the perturbation parameters, aggregated clients and sampling size according to the changing distribution of the individual data. The convergence proof shows that AdaDpFed has O(1T) convergence rate. Comparative experiments demonstrate that the performance of AdaDpFed outperforms other state-of-the-art protocols in both accuracy and global privacy budget.
ANP Model as a Support to Decide the Project Management Style

Agile methods in project management are trending as a reaction to the growing and changing business environment. Choosing a proper approach to managing a project is difficult. Many criteria need to be considered. Nowadays, two main approaches to project management are used -agile and waterfall. In the waterfall approach, project phases are in sequence. It means that the customer gets results at the end of the project. Agile methods are based on iterations and increments. The customer is involved in the whole project and provides feedback on the incremental product. This paper discusses creating an Analytic Network Process (ANP) model, which can provide multi-criteria decision-making. In this paper, clusters and relations are established. Clusters composition comes out on the international project management standard PRINCE2® Agile, Agile Manifesto and SCRUM Guide. Clusters include project management triangle (or hexagon), team characteristics (location, working style and communication) and team members’ characteristics (T–Shape, Pi–Shape, I–Shape, X–Shape).
Distributed collaborative complete coverage path planning based on hybrid strategy

Collaborative coverage path planning (CCPP) refers to obtaining the shortest paths passing over all places except obstacles in a certain area or space. A multi-unmanned aerial vehicle (UAV) collaborative CCPP algorithm is proposed for the urban rescue search or military search in outdoor environment. Due to flexible control of small UAVs, it can be considered that all UAVs fly at the same altitude, that is, they perform search tasks on a two-dimensional plane. Based on the agents' motion characteristics and environmental information, a mathematical model of CCPP problem is established. The minimum time for UAVs to complete the CCPP is the objective function, and complete coverage constraint, no-fly constraint, collision avoidance constraint, and communication constraint are considered. Four motion strategies and two communication strategies are designed. Then a distributed CCPP algorithm is designed based on hybrid strategies. Simulation results compared with pattern- based genetic algorithm (PBGA) and random search method show that the proposed method has stronger real-time performance and better scalability and can complete the complete CCPP task more efficiently and stably.
Data Reconstruction via Consensus Graph Learning for Effective Anomaly Detection in Industrial IoT

In the industrial Internet of Things (IIoT), anomaly detection is fundamental to ensuring system safety and product quality, among other things. However, the massive amount of unlabeled, high-dimensional data generated in the IIoT challenges some existing anomaly detection methods as follows: 1) the distance concentration caused by the high dimensionality of the data can cause a decrease in the detection accuracy of some of the methods; and 2) some of the methods fail to explore the intrinsic relationships between the data, resulting in less effective detection of anomalies in the data. To handle the above challenges, a framework named data reconstruction via consensus graph learning (DRCG) and two anomaly score functions are proposed. Specifically, DRCG overcomes the distance concentration problem and explores the intrinsic relationships of the data by integrating projection learning, low-dimensional embedding, and consensus graph learning into a unified objective function. Then, an iterative algorithm is designed to solve the DRCG model. By doing so, DRCG not only drives the reconstruction error of abnormal samples higher than that of normal samples, but also obtains the projection that can effectively extract the intrinsic relationship between the data. Furthermore, to identify anomalies in the data, two anomaly score functions based on the reconstruction error and projection are designed, respectively. To achieve online anomaly detection for streaming data, DRCG with the projection-based anomaly score function is extended into an online version. The effectiveness and superiority of the proposed methods have been demonstrated on four real-world datasets.
Factors Influencing Students’ Intention to Use Mobile Learning in Tanzanian Higher Education: A Case of National Institute of Transport

Assessing users’ attitudes towards mobile learning and understanding mediation effects of effort expectancy has posed challenges for researchers. This study aims to fill theoretical and empirical gaps by using mediation analysis to assess factors influencing students’ behavioural intention to use mobile learning in Tanzanian higher learning institutions. Guided by the Unified Theory of Acceptance and Use of Technology, the study focuses on the indirect influences of Performance Expectancy, Social Influence, and Facilitating Conditions on students’ behavioural intention, mediated by Effort Expectancy. A sample size of 181 respondents was obtained through simple random sampling. Data were collected using questionnaires. Findings reveal positive indirect effects of Performance Expectancy, Social Influence, and Facilitating Conditions on students’ behavioural intention. The study concludes that these factors, along with Effort Expectancy, influence students’ intention to use mobile learning. The study recommends that the policymakers and stakeholders should consider these indirect effects when implementing mobile learning, ensuring it supports academic goals and provides sufficient infrastructure and support for effective usage.
SOCIAL MEDIA USE, CHANGES IN READING /WRITING CULTURE, AND ACADEMIC PERFORMANCE OF UNDERGRADUATES IN THE NIGER DELTA UNIVERSITY, BAYELSA STATE

This study examined the impact of social media usage on the reading and writing culture of undergraduate students of Niger Delta University vis-a-vis the academic performance of the students. No doubt, the use of social media has brought significant changes to all aspects of human lives within the past decade. Despite this, there is a dearth of empirical studies analysing the change it has brought to reading and writing culture of undergraduate students in Nigeria. Uses and gratification theory was adopted as framework for the study, while a survey research design was employed. The Niger Delta University was purposively selected as the research locale based on its lack of empirical analysis on the subject matter. The university was clustered into faculties and a sample of 392 respondents was drawn using Yamane (1967) formula. Simple random sampling was used to proportionately administer a structured questionnaire on students. Descriptive statistics and Multiple Linear Regression at P&lt;0.05 were applied to analyse Data. The average age of the respondents was 21.29±2.49 years. The majority used WhatsApp (100.0%) and Facebook (30.9%) to study or write as undergraduate students. They also used these platforms to read journal articles (47.2%), academic books (85.7%) and watch movies (99.0%) among others. These activities, however, jointly predicted academic performance at (R=0.895, R2=0.801, Adj. R2=0.797, F(8, 383)=192.854). Using the social media to read Journal articles (β=0.112), academic books (β=-0.308), magazines (β=-0.123), newspapers (β=-0.265), watch movies (β=0.107), pictures (β=-0.496) and advertisement (β=-0.974) correlated student academic performance. The study recommended that, students academic reading materials be distributed on social media platforms following the students’ level of trend and addiction to social media.
The accuracy of forecasting results of the Box-Jenkins method for time series analysis on the number of pneumonia patients

This study was quantitative with a non-reactive or unobstructed approach using Time Series analysis with the ARIMA Box-Jenkins method on secondary data. From secondary data, 1, 3, and 6 monthly data simulations were carried out. Each simulation data was divided into two groups: the first group of initialization data for 2016 – 2019 and the second group of actual data for 2020. The aim of this study was to determine the accuracy of forecasting results using the Box- Jenkins method on the number of pneumonia sufferers at Kamonji Public Health Center, Palu. The 1-monthly data simulation amounted to 48-time series as initialization data, obtained the appropriate forecasting model, namely ARIMA (1,1,1), then forecasting was done, and the results obtained were 289,166 patients with pneumonia. There are 16-time series simulations for 3-monthly data as initialization data and 6-monthly simulations totaling two-time series for initialization data, not finding a suitable model for forecasting. In conclusion, no data simulation gets the right result on the number of pneumonia sufferers because it gets more forecasting results than the actual data. Suggestions that can be given in this research are to use data that is more than five years old.
MULTICULTURAL SCHOOLS AND THE INCLUSIVE DIMENSION OF WELL-IMPLEMENTED COMMUNITY PRACTICES ON BOOSTING THE SCHOOL ETHOS

Multicultural schools are not merely schools where students of different ethnic groups or nationalities learn together, but primarily they are schools with an ethos. The facets and characteristics of such schools impact the learning trajectories of students and inclusive practices are the norm there. The effective implementation of such practices, the strategies for keeping the learning environment inclusive, alongside preserving identities and fostering developmental pathways are core values in multicultural schools. The article aims at highlighting the importance of salient features of such schools and the implementation of case studies provides models and frameworks to be observed. Furthermore, a skill set that promotes critical thinking and problem-solving in the context of blended learning, alongside the face-to-face one, are core competences in a multicultural learning environment. The identity construction of each ethnic group needs to blend into an educational environment that fosters unswerving cross-cultural teaching. Inclusion and integration are part of a student-centered approach to teaching, in schools where means and methods are tailored to address the specific needs of the student, turning a dependable child into an autonomous adult, regardless of the diverse socio-economic or cultural backgrounds students come from.
Wavelet Scattering Transform for Improving Generalization in Low-Resourced Spoken Language Identification

Commonly used features in spoken language identification (LID), such as mel-spectrogram or MFCC, lose high-frequency information due to windowing. The loss further increases for longer temporal contexts. To improve generalization of the low-resourced LID systems, we investigate an alternate feature representation, wavelet scattering transform (WST), that compensates for the shortcomings. To our knowledge, WST is not explored earlier in LID tasks. We first optimize WST features for multiple South Asian LID corpora. We show that LID requires low octave resolution and frequency-scattering is not useful. Further, cross-corpora evaluations show that the optimal WST hyper-parameters depend on both train and test corpora. Hence, we develop fused ECAPA-TDNN based LID systems with different sets of WST hyper-parameters to improve generalization for unknown data. Compared to MFCC, EER is reduced upto 14.05% and 6.40% for same-corpora and blind VoxLingua107 evaluations, respectively.
TRAM: Benchmarking Temporal Reasoning for Large Language Models

Reasoning about time is essential for understanding the nuances of events described in natural language. Previous research on this topic has been limited in scope, characterized by a lack of standardized benchmarks that would allow for consistent evaluations across different studies. In this paper, we introduce TRAM, a temporal reasoning benchmark composed of ten datasets, encompassing various temporal aspects of events such as order, arithmetic, frequency, and duration, designed to facilitate a comprehensive evaluation of the temporal reasoning capabilities of large language models (LLMs). We conduct an extensive evaluation using popular LLMs, such as GPT-4 and Llama2, in both zero-shot and few-shot learning scenarios. Additionally, we employ BERT-based models to establish the baseline evaluations. Our findings indicate that these models still trail human performance in temporal reasoning tasks. It is our aspiration that TRAM will spur further progress in enhancing the temporal reasoning abilities of LLMs.
Faster and Accurate Neural Networks with Semantic Inference

Deep neural networks (DNN) usually come with a significant computational burden. While approaches such as structured pruning and mobile-specific DNNs have been proposed, they incur drastic accuracy loss. In this paper we leverage the intrinsic redundancy in latent representations to reduce the computational load with limited loss in performance. We show that semantically similar inputs share many filters, especially in the earlier layers. Thus, semantically similar classes can be clustered to create cluster-specific subgraphs. To this end, we propose a new framework called Semantic Inference (SINF). In short, SINF (i) identifies the semantic cluster the object belongs to using a small additional classifier and (ii) executes the subgraph extracted from the base DNN related to that semantic cluster for inference. To extract each cluster-specific subgraph, we propose a new approach named Discriminative Capability Score (DCS) that finds the subgraph with the capability to discriminate among the members of a specific semantic cluster. DCS is independent from SINF and can be applied to any DNN. We benchmark the performance of DCS on the VGG16, VGG19, and ResNet50 DNNs trained on the CIFAR100 dataset against 6 state-of-the-art pruning approaches. Our results show that (i) SINF reduces the inference time of VGG19, VGG16, and ResNet50 respectively by up to 35%, 29% and 15% with only 0.17%, 3.75%, and 6.75% accuracy loss (ii) DCS achieves respectively up to 3.65%, 4.25%, and 2.36% better accuracy with VGG16, VGG19, and ResNet50 with respect to existing discriminative scores (iii) when used as a pruning criterion, DCS achieves up to 8.13% accuracy gain with 5.82% less parameters than the existing state of the art work published at ICLR 2023 (iv) when considering per-cluster accuracy, SINF performs on average 5.73%, 8.38% and 6.36% better than the base VGG16, VGG19, and ResNet50.