Miracle-dz/UniFAR-FTUs huggingface dataset:
In Search of Distance Functions That Improve Autoencoder Performance for Intrusion Detection

Expectations of detection systems have risen with the increase in cyber-attacks. In order to detect the latest and future attacks, systems capable of detecting unknown attacks are needed. Among the various approaches offered by machine learning models, anomaly detection methods can address this need. It is possible to use the autoencoder to detect anomalies and therefore attacks. An autoencoder trained on data from normal use is able to detect attacks, unknown to the model. The attack detection is possible by observing the reconstruction error, which is the distance between the input and the reconstructed input resulting from the model. We considered different distance functions to improve the separation between attacks and normal events, and thus, to improve the performance of the autoencoder. We propose to use the cosine function of the angle formed between the actual input vector and the reconstructed input vector, as a distance function to address the problem of overlapping between normal events and attacks. In addition, we used Tree-structured Parzen Estimator algorithm for the optimization of the hyperparameters of the model. We ran our method on the NSL-KDD dataset and compared the obtained results to those of other methods that exist in the literature.
Analysis of the Surface Layer and Feed Force after Milling Polymer Composites with Coated and Uncoated Tools

Polymer composites are widely used in various fields and industries. This study investigated milling of four dif - ferent glass and carbon fiber reinforced plastics. First, feed force values were determined in the milling process conducted using tools with polycrystalline diamond inserts, with titanium nitride-coated cemented carbide inserts and with uncoated ground cemented carbide inserts. Machined surfaces were then examined for roughness. Using scanning microscopy (SEM), differences in the surface layer were also determined. Results showed that the lowest force values were obtained in milling of glass fiber reinforced plastics using tools with polycrystalline diamond inserts. The best machining results in terms of roughness were obtained after milling glass fiber reinforced plastics.
Diversity and Genetic Association Analysis of Dry Chillies (Capsicum annuum L.) of Northern India for Yield and Biochemical Traits

A study was conducted at the Experimental Farm of Department of Vegetable Science, Dr Yashwant Singh Parmar University of Horticulture and Forestry, Nauni, Solan (HP), India during the year 2018 (May–October) to screen twenty-one (21) genotypes of chilli for thirteen characters including yield and total capsaicin content. The evaluation was done in a randomized block design with three replications. Sufficient genetic variability was found for all characters. Correlation analysis revealed a positive significant correlation of fresh ripe yield plant-1 with the number of red fruits plant-1 (0.842*), days to fresh ripe maturity (0.394*), dry yield as percentage of fresh ripe (0.386*), days to 50 % flowering (0.384*), plant height (0.374*) and days to green fruit maturity (0.352*). The number of red fruits plant-1 (0.440*) had a positive significant correlation with total capsaicin content. Path analysis revealed a positive direct effect of number of red fruits plant-1 (0.698) on fresh ripe yield followed by days to green fruit maturity (0.372), number of seeds fruit-1 (0.218), average red fruit weight (0.168), dry yield as percentage of fresh ripe (0.111), plant height (0.111) and red fruit length (0.007) whereas total capsaicin content in dry chilli had the positive direct correlation with days to 50% flowering, days to fresh ripe maturity, number of fruits plant-1 and 1000 seed weight. Finally, it was revealed that the selection of genotypes bearing many small-thick fruits would be the most suitable criterion for selecting a population having high capsaicin content and fruit yield.
Networks, strata and ageing: towards a compositional demography of vulnerability

In a recent commentary, Kreager (2011) has argued for the “strategic value of studying population composition” in order better to identify the units of population relevant for understanding vulnerability. Conventional approaches, relying on census and survey sources which model populations as homogenous, bounded and composed of discrete households, have failed to accomplish this. Drawing on ethnographic and demographic fieldwork in rural Indonesia, this paper attempts a contribution to ‘compositional demography’ by illustrating a methodology for identifying the subpopulations within a community which are relevant for understanding vulnerability in later life. Key differentials at the local level include the availability of children and socio-economic strata; combining these two dimensions begins to point to significant vulnerable subgroups but falls short of explaining outcomes in later life, because demographic and socio-economic characteristics are mediated by membership of wider networks. The paper therefore characterises a number of indicative and contrasting kinship networks in the study community in terms of their demographic success, marital alliances, land ownership and occupational identities, and examines how these networks have differentially aligned themselves with broader religious, economic and social shifts. Some networks are better able to exploit the opportunities that education or migration entail, others founder on fragmentation of assets, fecklessness and disharmony, and the loss of reputation that entails. It becomes possible to distinguish ascending and descending, locally-bound and translocalizing, and ‘traditional’ and ‘modernising’ networks. Once older people are located within these different kinds of networks, their treatment and differential vulnerability need no longer be modelled purely on their individual characteristics or immediate household or family contexts, but can be understood as the outcome of negotiations, manoeuvrings and sometimes failures of the networks to which they belong. The needs and priorities of elders themselves are rarely of central concern to key agents within the networks, a fact which underlines the limitations of treating ‘the older population’ in isolation. In short, the paper makes an argument for treating networks as the population components of relevance for understanding socio-demographic processes, including population ageing. Identifying and modelling networks raises important conceptual and methodological challenges, but in virtue of being multi-generational, reproductive identities which encompass diversity and cut across strata and space, networks are socially meaningful and empirically grounded population units within which to understand human behaviour
Water Quality Status of Way Batanghari River, Metro City, Lampung Province Based on Water Fit for Consumption Parameters

Introduction: The need of water fit for consumption will continue to be sustainable along with the increasing human population, so we need a source of clean water that suitable for consumption. Therefore, this study aims to determine the water quality status of the Way Batanghari river based on an analysis of the feasibility of potable water. Methods: This research used survey method and water sampling in the Way Batanghari tributary, Metro City, Lampung using the consecutive sampling method. Results and Discussion: The physical parameters which include water color exceeds the 50 TCU and indicates the odor of ammonia. The chemical parameters which include COD were 63.5 mg/L (4th class) upstream, 59.3 mg/L (4th class) downstream. DO values upstream were 4.36 mg/L (1st class), 2.83 mg/L (3rd class) downstream. The phosporus value shows a number of 0.54 mg/L (2nd class). pH parameter which shows a value of 7.1 on the upstream, 6.6 on the downstream. Found species from class of Bacillariophyceae, Cyanophyceae as parameters of pollution. Conclusion: Based on Regulation of the Minister of Health, Republic of Indonesia Number 492 of 2010, this river has color and odor of water shows that indicates not meet the requirements for consumption.
Automatic Tealeaf Disease Detection Using Machine and Deep Learning Method

Agriculture is a main source of livelihood. In everyday life, the tea plant is the most cultivatable plant in the highlands. Most farmers in India have turned to manual labor due to a lack of experience and technological understanding. When plants are infected with a variety of diseases, it has an impact on losses. In tea farming, the most profitable part of the plant is on the top. This leaf is quickly infected by illnesses. Farmers face a difficult task in identifying illnesses in tea plant leaves. Finding diseases in tea plant leaves is a challenge for the farmers. The purpose of this research is to build a CNN model using large datasets. To detect tea plant diseases from images, we developed CNN architecture, including Inception-V3, Googlenet, and Resnet-110. We also developed a machine learning model, the SVM classifier, to forecast diseases, prescribe pesticides, and compare machine and deep learning models.
A LIFE-CYCLE INTERGENERATIONAL MODEL CONSIDERING SOCIAL SECURITY

The central objective of this paper is to deliver an intergenerational Life Cycle model considering the retirement process based on the pay-as-you-go system (PAYG). Our model is inspired by Baranzinis’ approach, where the optimal consumption analysis is made in a two-class type and restricted by the capital variation of each one. Our main results observe the equilibrium solution of the consumption and capital stock to both classes and conclude that the PAYG system does interfere with the results, as well as the time preference to leave or not inheritance. The methodology approached here is the Pontryagins’ Maximum Principle. We also applied a numerical simulation to ensure the robustness of our approach.
Screening for Thyroid Disorder in First Trimester of Pregnancy: A Cross-Sectional Study

Background: Alteration in thyroid function is very common among females, in particular during pregnancy. The majority of them are clinically veiled, and with considerable differences in outcome of mother and fetus, even more serious maternal and fetal outcome if present during the first trimester. So, we conducted this study to explore the magnitude of thyroid problems during first trimester of pregnancy at a tertiary health care facility in Nepal and highlight the benefits of its early diagnosis. Methods: This was a cross-sectional study conducted over a period of 1 year among 160 pregnant females in their first trimester, attending to the outpatient department of a tertiary care center. Blood samples were collected from all the patients for thyroid function test which included measurement of serum levels of thyroid-stimulating hormone (TSH), free thyroxine (fT4), and free tri-iodothyronine (fT3). Pregnant females with previously diagnosed thyroid disorders or currently under medications were excluded. Results: Among the 160 patients included in the study, 25.83 years was the mean age. The mean body mass index (BMI) was 24.62 kg/m 2 and most of the patients were overweight (40.625%). Of the patients 53.12% were primigravida and 46.87% were multigravida. Based on the thyroid function tests, 91.25% were found to be euthyroid, 3.125% were hyperthyroid and 5.625% were hypothyroid. The prevalence of subclinical hyperthyroidism and overt hyperthyroidism was found to be 60% and 40% respectively and that of subclinical hypothyroidism and overt hypothyroidism was 55.6% and 44.4% respectively. None of the patients had symptoms of thyroid disorder (neither hyperthy-roidism nor hypothyroidism). The correlation between BMI and TSH among the pregnant females included in the study was statistically significant when tested by using Karl Pearson correlation coefficient (r = 0.164, P = 0.038). Conclusions: Based on this study, around one in every 12 pregnant women had thyroid disorder. Early identification of thyroid disorders and prompt initiation of treatment is very essential for the health of mother and fetus due to the significant maternal or fetal morbidity and mortality associated with thyroid disorders. Thus, routine screening for thyroid disorder should be considered during pregnancy.
The Role of Artificial Intelligence in Gastric Cancer: Surgical and Therapeutic Perspectives: A Comprehensive Review

Stomach cancer has a high annual mortality rate worldwide necessitating early detection and accurate treatment. Even experienced specialists can make erroneous judgments based on several factors. Artificial intelligence (AI) technologies are being developed rapidly to assist in this field. Here, we aimed to determine how AI technology is used in gastric cancer diagnosis and analyze how it helps patients and surgeons. Early detection and correct treatment of early gastric cancer (EGC) can greatly increase survival rates. To determine this, it is important to accurately determine the diagnosis and depth of the lesion and the presence or absence of metastasis to the lymph nodes, and suggest an appropriate treatment method. The deep learning algorithm, which has learned gastric lesion endoscopyimages, morphological characteristics, and patient clinical information, detects gastric lesions with high accuracy, sensitivity, and specificity, and predicts morphological characteristics. Through this, AI assists the judgment of specialists to help select the correct treatment method among endoscopic procedures and radical resections and helps to predict the resection margins of lesions. Additionally, AI technology has increased the diagnostic rate of both relatively inexperienced and skilled endoscopic diagnosticians. However, there were limitations in the data used for learning, such as the amount of quantitatively insufficient data, retrospective study design, single-center design, and cases of non-various lesions. Nevertheless, this assisted endoscopic diagnosis technology that incorporates deep learning technology is sufficiently practical and future-oriented and can play an important role in suggesting accurate treatment plans to surgeons for resection of lesions in the treatment of EGC.
Screening for Batrachochytrium dendrobatidis in New Zealand native frogs: 20 years on

: A chytrid fungus ( Batrachochytrium dendrobatidis ; Bd) has been a cause of amphibian declines worldwide. Batrachochytrium dendrobatidis was first detected in New Zealand on an introduced frog species in 1999 and two years later was associated with morbidity in Leiopelma archeyi , one of the three native New Zealand frog species . In this study, we aimed to document the prevalence of Bd in native frog species in New Zealand from 2014–2021. Skin swabs were collected from six sites in New Zealand: Maud Island/Te Pākeka, Zealandia Ecosanctuary, Auckland Zoo, and University of Otago between 2019–2021, and Whareorino Forest and Pukeokahu between 2014–2020. Swabs were analysed using qPCR to detect Bd from skin swabs. A total of 324 frogs from the six sampling sites were screened for Bd presence. Four percent of the L. hamiltoni and eight percent of the L. archeyi were Bd-positive, and all L. hochstetteri were Bd-negative. The detection of Bd in Leiopelma species confirmed that Bd persists in New Zealand endemic frogs at a low prevalence and intensity.
NGUB: Novel Greedy Algorithms for User and Beam Selection in mmWave Networks

We study the problem of maximization of the sum of weighted rates by determining user equipment (UE) and a beam for each access point (AP) for concurrent transmissions, in millimeter wave (mmWave) networks. The inherent directional nature of communication in mmWave networks, along with the susceptibility of connections to blockage makes the problem challenging. In prior works, several attempts have been made to design algorithms for UE and beam selection in different mmWave network scenarios. However, only a few works consider beam and UE selection in mmWave networks having multiple APs and UEs. In this paper, we design two novel greedy algorithms, NGUBI and NGUB2, for UE and beam selection in mmWave networks containing multiple APs and UEs. Through extensive simulations, we show that our proposed algorithms outperform the most relevant algorithms proposed in prior work.
Robotics Framework for Object Tracking using FPGA with Novel Automatic Image Labelling

Autonomous robots require the ability to perceive their environment. This must be done in a power-efficient manner to allow them to operate for an extended duration of time. Convolutional neural networks (CNN) are typically used to process image data but they require large amounts of processing power to deploy. CNNs can be efficiently implemented on an FPGA achieving low power consumption. In this work, we present a framework for implementing CNNs on an MPSoC that can be used in robotics applications. A method for automatic image labelling is used to create a dataset for training the neural network. The model is trained using TensorFlow and the weights are automatically exported and programmed onto the FPGA. An example application is developed to showcase the proposed framework. The application achieves a 428% increase in performance and a 432% increase in power efficiency when using hardware acceleration compared to running the application on a CPU.
Perceived Health Risks among Teachers Working Remotely in the Era of Covid-19 Pandemic

The COVID-19 pandemic gained prominence and posed a threat to the traditional education system when quarantine was implemented. The teachers' workload increased as work from home was mandated. As a result of working remotely, the teachers experience challenges in their physical, mental, and socio-emotional health. This study evaluated the health risks as perceived by teachers in a tertiary educational institution working remotely during the era of the COVID-19 pandemic in terms of physical, mental, and socio-emotional health. It also aimed to describe the socio-demographic profile of the respondents and determined their current health problems. A cross-sectional descriptive study design was utilized. The respondents were selected using a non-probability convenience sampling technique and data were collected using a researcher-made Likert-type questionnaire adapted from the Occupational Safety and Health Standards by DOLE (2022). Content and construct validity were accomplished through expert reviews and reliability testing was established through a Cronbach alpha coefficient value of 0.950. Data were analyzed using descriptive statistics as well as Z-test and ANOVA for the test of difference. The demographic profile of the respondents indicated a majority belonging to the College of Arts and Sciences, mostly of married civil status, and predominantly female with ages ranging from 41 to 50. The dominant ranks showed Instructors I, II, and III while time engagement in educational technology ranged from 3 hours and above but not greater than 6 hours. The respondents' perceived physical, mental, and socio-emotional health risks revealed a moderate risk as supported by an overall grand mean of 2.629. The physical risk was primarily associated with musculoskeletal problems and had the highest grand mean of 3.038, signifying a moderate level of risk. On the other hand, mental and socio-emotional dimensions were both perceived as low risks, with corresponding grand means of 2.525 and 2.323 respectively. Further results highlighted that level of health risks has a significant difference when grouped according to age (p-value of 0.0012) and academic rank (p-value of 0.0019), thus rejecting the null hypothesis. Sex, marital status, college affiliation, and time engagement in education technology demonstrated no significant difference at all. Health Optimization Practices for Educators (HOPE) was crafted to address health risks by promoting health and wellness through knowledge enhancement. Future studies akin to teachers’ health risks while working remotely need to be explored in the areas of ergonomics and occupational hazards.
Innovation in Early Reading Instruction: The Development of e-Learning Materials in Mother Tongue

Mother Tongue-Based-Multilingual Education has been shown to improve the academic performance of early-grade learners. However, challenges such as inaccessibility and lack of contextualized materials in the mother tongue (MT) have hindered students' full engagement in learning. To address this, a quasi-experimental study was conducted with struggling first-grade learners (n=60) to investigate the effectiveness of researcher-developed e-learning materials for early reading in MT. Instruments used in this study included a researcher-created storyboard for video creation, with its dialogue and storyline; the test questionnaires, adapted from the MT-based Curriculum Guide of the Department of Education in the Philippines, for measuring participants’ reading performance in phonic and word recognition. The pre-test and post-test data were analyzed using mean, standard deviation, and a t-test at a 5% significance level. Qualitative data extracted through interviews were analyzed thematically, while a four-point Likert scale was used to interpret the overall rating of the e-learning materials. The content and ICT experts measured the e-learning materials’ validity and effectiveness. The results illustrated e-learning materials like video stories and PowerPoint presentations, developed using the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) model, improved learners' letter-sound and word-blending abilities. Experts rated the materials excellent for technical standards, content quality, and effectiveness. The e-learning materials as a pedagogical intervention in early reading are innovative, interactive, and engaging and can potentially enhance early graders' reading performance. Future research can explore design or layout modification of the materials creatively to enhance learner engagement in early-grade MT instruction.
Examining the association between urban green space and viral transmission of COVID-19 during the early outbreak

Even though exposure to urban green spaces (UGS) has physical and mental health benefits during COVID-19, whether visiting UGS will exacerbate viral transmission and what types of counties would be more impacted remain to be answered. In this research, we adopted mobile phone data to measure the county-level UGS visi- tation across the United States. We developed a Bayesian model to estimate the effective production number of the pandemic. To consider the spatial dependency, we applied the geographically weighted panel regression to estimate the association between UGS visitation and viral transmission. We found that visitations to UGS may be positively correlated with the viral spread in Florida, Idaho, New Mexico, Texas, New York, Ohio, and Penn- sylvania. Especially noteworthy is that the spread of COVID-19 in the majority of counties is not associated with green space visitation. Further, we found that when people visit UGS, there may be a positive association be- tween median age and viral transmission in New Mexico, Colorado, and Missouri; a positive association between concentration of blacks and viral transmission in North Dakota, Minnesota, Wisconsin, Michigan, and Florida; and a positive association between poverty rate and viral transmission in Iowa, Missouri, Colorado, New Mexico, and the Northeast United States.