Miracle-dz/UniFAR-FTUs huggingface dataset:
An in-depth exam of IoT, IoT Core Components, IoT Layers, and Attack Types

The Internet of Things (IoT) is a global network of devices that can communicate with each other through different communication protocols and have the ability to detect and process data. Since the Internet of Things connects electronic devices to the Internet for efficient examination and execution of daily activities, it has a seriously positive effect on human life. These devices use some communication protocols to communicate with each other. The selection of these communication protocols is of great importance. The data obtained from these smart devices are stored with the help of cloud technology. These stored data reveal the concept of big data. The processing and analysis of these data are of great importance. Machine learning methods are of great importance in processing this large amount of data. In this study, IoT, the basic components of the IoT, layer structure, security attacks are examined.
Relevance Search via Bipolar Label Diffusion on Bipartite Graphs

- The task of relevance search is to find relevant items to some given queries, which can be viewed either as an information retrieval problem or as a semi-supervised learning problem. In order to combine both of their advantages, we develop a new relevance search method using label diffusion on bipartite graphs. And we propose a heat diffusion-based algorithm, namely bipartite label diffusion (BLD). Our method yields encouraging experimental results on a number of relevance search problems
Predicting the Toxicity of Chemicals and Drugs using Machine Learning Models

: Toxicity is the way to find out if the drug / medicine is harmful to human body. Currently, the toxicity of the medicine is calculated using in-vivo method, where the medicine is tested on the animals and their results are generated. However, this method of toxicity testing for all existing compounds biologically may not be viable financially and logistically. We try to solve this problem by using machine learning and deep learning techniques. We have used the ensemble learning algorithm voting based classifier [logistic regression, decision tree, support vector machines] to predict the toxicity of theTox21 dataset. Where we get the AUC (Area under Curve) of NR-AR-LBD: 0.87 SR-mmp: 0.84 NR-Ahr: 0.81 on these assays.
Open Source Communities in Technical Writing: Local Exigence, Global Extensibility

By offering open-source software (OSS)-based networks as an affordable technology alternative, we partnered with a nonprofit community organization. In this article, we narrate the client-based experiences of this partnership, highlighting the ways in which OSS and open-source culture (OSC) transformed our students' and our own expectations of traditional hierarchies in technical writing classes and work. The integration of OSS into technical communication classes shifted our work toward distributed symbolic-analytic issues and practices. Specifically, our engagements with OSS/OSC increased student awareness of the political and cultural significance of OSS and proprietary technology systems, and flattened traditional educational and client-student hierarchies. In this way, OSS/OSC offers ways to attune local pedagogy and practice to global developments in technical writing, and provide today's technical communication students with the experiences needed to succeed in the workplace of tomorrow.
How Does Zestimate Affect Housing Market Outcomes Across Socio-economic Segments?

We study the impact of Zillow’s Zestimate on housing market outcomes and how the impact differs across socio-economic segments. Zestimate is produced by a machine learning algorithm using large amounts of data and aims to predict a home’s market value at any time. Zestimate can potentially help market participants in the housing market as identifying the value of a home is a non-trivial task. However, inaccurate Zestimate could also lead to incorrect belief about property values and hinder the selling process. Meanwhile, Zestimate tends to be systematically more accurate for rich neighborhoods than poor neighborhoods, raising concerns that the benefits of Zestimate accrue largely to the rich, widening socio-economic inequality. Using data on Zestimate and housing sales in the United States, we show that Zestimate benefits the housing market, as on average it increases both buyer welfare and seller welfare. Moreover, Zestimate actually reduces socio-economic inequality, as our results reveal that both rich and poor neighborhoods benefit from Zestimate but the poor neighborhoods benefit more. We build a structural model of a housing market where sellers and buyers face uncertainty about property values. Zestimate provides a signal of the property value. Our model captures two potential effects of Zestimate. First, it reduces uncertainty in beliefs about property values. Second, it shifts the mean belief about property values towards the Zestimate. Since Zestimate predicts the property value with some error, the reported Zestimate could under or over estimate the property value. Hence, the mean belief about the property value may be shifted away from the true property value.
Earth Imagery Segmentation on Terrain Surface with Limited Training Labels: A Semi-supervised Approach based on Physics-Guided Graph Co-Training

Given earth imagery with spectral features on a terrain surface, this paper studies surface segmentation based on both explanatory features and surface topology. The problem is important in many spatial and spatiotemporal applications such as flood extent mapping in hydrology. The problem is uniquely challenging for several reasons: first, the size of earth imagery on a terrain surface is often much larger than the input of popular deep convolutional neural networks; second, there exists topological structure dependency between pixel classes on the surface, and such dependency can follow an unknown and non-linear distribution; third, there are often limited training labels. Existing methods for earth imagery segmentation often divide the imagery into patches and consider the elevation as an additional feature channel. These methods do not fully incorporate the spatial topological structural constraint within and across surface patches and thus often show poor results, especially when training labels are limited. Existing methods on semi-supervised and unsupervised learning for earth imagery often focus on learning representation without explicitly incorporating surface topology. In contrast, we propose a novel framework that explicitly models the topological skeleton of a terrain surface with a contour tree from computational topology, which is guided by the physical constraint (e.g., water flow direction on terrains). Our framework consists of two neural networks: a convolutional neural network (CNN) to learn spatial contextual features on a 2D image grid, and a graph neural network (GNN) to learn the statistical distribution of physics-guided spatial topological dependency on the contour tree. The two models are co-trained via variational EM. Evaluations on the real-world flood mapping datasets show that the proposed models outperform baseline methods in classification accuracy, especially when training labels are limited.
Preliminary Investigation into the Ability of Tropical Rhizobia to Degrade Petroleum Products as Environmental Pollutants and Chemical Bases for Some Syhnthetic Pesticides

This study involves the investigation of the ability of some Tropical Cowpea Rhizobia to degrade crude oil in oil spilled soil as well as crude oil products which includes diesel, kerosene and petrol. This was carried out by introducing the rhizobial strains Rhizobium species CWP A17, R. species CWP G34A, R. species CWP G34B, R. species C, Bradyrhizobium species R. 3IB, Br. species FA3, Br. species T5BL 442 , Br. speciesUSAD 4675 (400) and Br. species 1495 MAR into various fuel media. The population and absorbance of the various bacteria were taken in each case at 24 hours interval for 10 days. Each of the various Rhizobia and Bradyrhizobia degraded hydrocarbon. The physicochemical parameters of the oil polluted soil were determined alongside with those of the control. The particle size analysis of the polluted soil sample indicated that on the average the soil is made up of 10.69% sand, 84.86% silt and 4.5% clay. This corresponds to silt loam soil. Other parameters indicated the utilization of the various hydrocarbon contents by the plant. The effects of oil polluted soil on nodulating and nitrogen fixing abilities of the tropical rhzobia were examined by inoculating each of the bacterial strain on the cowpea (Vignaunguiculata) grown on the oil polluted soil in a green house. These were observed at two weeks interval for 12 weeks. The various plant indices (i.e. height, number of leaves, and leaf area) showed retarded germination of the plant of oil polluted soil when compared to the control. However, there was slight difference in the case of stem girth. The stem girth of the plants grown on oil populated soil is wider. The biodegradation experiment showed that for the various rhizobia grown in crude oil, diesel, petrol and kerosene media, the degradation of the hydrocarbon occurred, in which the organisms were able to utilize the fuels as carbon source.
Inhibition of three toxigenic fungal strains and their toxins production using selenium nanoparticles.

Spoilage and poisoning of foods by microfungi are a major problem, especially in developing countries. While selenium nanoparticles (Se-NPs) have been used for a wide range of applications including antibacterial, antioxidant and anticancer applications, the effects of Se-NPs on fungal strains remain for the most part unknown to date. Our research is a pioneering attempt to evaluate the antifungal activity and antimycotoxin properties of Se-NPs (32 nm). Se-NPs at different concentrations were evaluated against the growth and mycotoxins production of three toxigenic fungal strains. The growth of Aspergillus parasiticus, A. ochraceus and A. nidulans was completely inhibited using 7000, 9000 and 3000 μg/ml of Se-NPs, respectively, while the complete inhibition in aflatoxins, ochratoxin A and sterigmatocystin production was reported by addition of 2000, 2000, and 800 μg/ml of Se-NPs, respectively. Results of this study show that Se-NPs were effective against the fungal strains tested and their toxin production. These results suggest that Se-NPs could be used as an effective microfungicide in agricultural and food safety applications against toxigenic microfungi.
SHADOW IT AND BUSINESS-MANAGED IT: PRACTITIONER PERCEPTIONS AND THEIR COMPARISON TO LITERATURE

Two concepts describe the autonomous deployment of IT by business entities: Shadow IT and Business-managed IT. Shadow IT is deployed covertly, that is, software, hardware, or IT services created/procured or managed by business entities without alignment with the IT organization. In contrast, Business-managed IT describes the overt deployment of IT, that is, in alignment with the IT organization or in a split responsibility model. The purpose of this paper is to extend the conceptual understanding of Shadow IT and Business-managed IT, comparing the perceptions of 29 CIOs and senior IT managers with the results of a systematic literature review. By doing so, this paper presents a structured and comprehensive view of causing factors, outcomes, and governance of Shadow IT and Business-managed IT in practice. A comparison of academic literature and practitioner perceptions reveals the limitations and gaps of the current research and highlights avenues for future research. The authors find three category-spanning themes occurring as causing factors, outcomes, and—as part of governance measures—factors to improve the IT organization: (1) (Poor) business-IT alignment (2) (lack of) agility, and (3) (lack of) policies. This study is innovative with its comprehensive qualitative interview data that the authors compare to the existing literature. Therefore, the paper brings together theoretical and practical insights into Shadow IT and Business-managed IT, which should aid practitioners and scholars in decision making and future research.
Rhinosinusite Acute

Upper airway infections (IVAS) are the most common infectious diseases during childhood, being characterized by being located in the upper respiratory tract. Acute rhinosinusitis is a type of URTI that causes symptomatic inflammation of the nasal cavity and paranasal sinuses lasting less than 4 weeks. The general objective is to analyze the profile of patients, aged 0 to 60 years, diagnosed with rhinosinusitis acute, attended at the private Hospital, located in the municipality of Aracaju / SE, during the years 2014 to 2018. This article was built on from a course conclusion work that was based on a research which was a descriptive, cross-sectional, observational and retrospective study of public archives at the Hospital da particular de Sergipe, where secondary data collection was made, from information from medical records. In the analyzed data, patients were mandatorily aged between 0 and 60 years old, as well as were seen during the years 2014 to 2018. The period in which there was a higher incidence of cases of URTI was between March and June, characterizing a greater incidence during the autumn period. The results showed an annual average of 8587 patients with URTI, with 2018 having the highest incidence. The main age group affected by URTI was between 0 and 9 years old, representing 25,790 (60.1%) patients, and adults were responsible for 9421 (21.9%). The results showed a greater involvement in the female gender and in the group from 0 to 19 years old. Due to the high incidence of this pathology, the greatest complaint in adult women among the population studied is evident and the importance of primary care to it in view of the large amount of expenses with the diagnosis and treatment of symptoms of acute rhinosinusitis.
Microsystem packaging: lessons from conventional low cost IC packaging

This paper describes some of the issues associated with the packaging of microsystems and how packaging can influence their reliability and performance. The importance of early consideration in the design cycle of packaging, and the environment in which the microsystem will be placed, is stressed. It illustrates how the IC packaging industry has striven to overcome the problems of residual stress, stress minimization, hermeticity, and improved thermal performance in their packages and how this can be applied to microsystem packaging. Packaging problems specific to microsystems are identified.
An Agile Internet of Things (IoT) based Software Defined Network (SDN) Architecture

Industry, Business, Society, governments are excited for the endless applications that can be done by Internet of Things (IoT) technology. Managing and controlling high dynamic IoT network overlaid huge number of ad hoc things/devices is a tough task. The traditional networks architecture cannot afford this task efficiently. Software Defined Networking (SDN) provides the agile dynamic solution that can cope with the special requirements of the diversity of innovative IoT applications. The Agile network architecture is built on having more softwarization of devices functions to offer more agility than what was provided traditionally by the network architectures cross layer concept. Network functions virtualization (NFV) and Deep Packet Inspection (DPI) could efficiently complement the SDN functions. The paper provides a vision on expanding the need for convergence of SDN and NFV into a swift, Self-organization, self-healing IoT network in heterogeneous environments. This paper presents an agile SDN based IoT network architecture; SDNoT. SDNoT newly considers an efficient merge of SDN, NFV and DPI for worldwide implementation of IoT.
PTR: A Benchmark for Part-based Conceptual, Relational, and Physical Reasoning

A critical aspect of human visual perception is the ability to parse visual scenes into individual objects and further into object parts, forming part-whole hierarchies. Such composite structures could induce a rich set of semantic concepts and relations, thus playing an important role in the interpretation and organization of visual signals as well as for the generalization of visual perception and reasoning. However, existing visual reasoning benchmarks mostly focus on objects rather than parts. Visual reasoning based on the full part-whole hierarchy is much more challenging than object-centric reasoning due to finer-grained concepts, richer geometry relations, and more complex physics. Therefore, to better serve for part-based conceptual, relational and physical reasoning, we introduce a new large-scale diagnostic visual reasoning dataset named PTR. PTR contains around 70k RGBD synthetic images with ground truth object and part level annotations regarding semantic instance segmentation, color attributes, spatial and geometric relationships, and certain physical properties such as stability. These images are paired with 700k machine-generated questions covering various types of reasoning types, making them a good testbed for visual reasoning models. We examine several state-of-the-art visual reasoning models on this dataset and observe that they still make many surprising mistakes in situations where humans can easily infer the correct answer. We believe this dataset will open up new opportunities for part-based reasoning.
Spectral-Cluster Framework For Credit-Card Fraud Detection Using A Genetic Algorithm Trained Modular Deep Learning Neural Network

Adversaries achieved such intrusion via carefully crafted attacks of large magnitude that seek to wreak havoc on network infrastructures with a focus on personal gains and rewards. Study proposes a spectral-clustering hybrid of genetic algorithm trained modular neural network to detect fraud in credit card transactions. The hybrid ensemble seeks to equip credit-card users with a system and algorithm whose knowledge will altruistically detect fraud on credit cards. Results show that the hybrid model effectively differentiates between benign and genuine credit card transactions with a model accuracy of 74%. © 2021
Self-Supervised Cryo-Electron Tomography Volumetric Image Restoration from Single Noisy Volume with Sparsity Constraint

Cryo-Electron Tomography (cryo-ET) is a powerful tool for 3D cellular visualization. Due to instrumental limitations, cryo-ET images and their volumetric reconstruction suffer from extremely low signal-to-noise ratio. In this paper, we propose a novel end-to-end self-supervised learning model, the Sparsity Constrained Network (SC-Net), to restore volumetric image from single noisy data in cryo-ET. The proposed method only requires a single noisy data as training input and no ground-truth is needed in the whole training procedure. A new target function is proposed to preserve both local smoothness and detailed structure. Additionally, a novel procedure for the simulation of electron tomographic photographing is designed to help the evaluation of methods. Experiments are done on three simulated data and four real-world data. The results show that our method could produce a strong enhancement for a single very noisy cryo-ET volumetric data, which is much better than the state-of-the-art Noise2Void, and with a competitive performance comparing with Noise2Noise. Code is available at https://github.com/icthrm/SC-Net.
Data Cluster Algorithms in Product Purchase SaleAnalysis

_____ Abstract — Big data has brought dramatic change in Online marketing. To analyze online big data there are various tools and methods. Clustering algorithm helps to partition online data and calculate various parameters. Main four algorithms plays an important role for data partition. In our paper we have reviewed four algorithm in second section. In third section we have discussed the methodology used in performance analysis of four clustering algorithm. In fourth section result and analysis of output. And in fifth section conclusion of the work we have done.
Thermally Induced Ventilation of Livestock Transporters

Abstract Livestock require ventilation during transport to ensure acceptable air temperatures, humidities and contaminant levels. Provision of ventilation in pig, sheep and cattle vehicles is usually through apertures positioned along the sides. Air exchange through these apertures occurs by forced ventilation, from vehicle motion or wind, or by thermally induced ventilation, generated by heat from the animals causing internal to external air temperature differences. Thermally induced ventilation is always present, but forced ventilation may be intermittent; particularly for a stationary vehicle on an enclosed roll-on roll-off ferry deck which prompted this study. Thus, to ensure adequate ventilation at all times, design calculations should be based on thermally induced ventilation. A model is presented which takes into account thermally induced ventilation through slot openings, heat transfer through the structure of the vehicle and heat, moisture and carbon dioxide output of the animals. Predictions of vehicle internal air temperature show this to be very insensitive to vehicle characteristics, species, heat output and stocking density. This arises because the ventilation is caused by the animal heat increasing the vehicle internal air temperature and consequently increasing the internal to external air temperature difference. For a given vent area, reducing stocking density, and thus animal heat, reduces the ventilation rate resulting in an internal air temperature which is largely self compensating. Comparison of predicted results with measurements made on a ferry voyage show agreement of vehicle internal air temperature to better than ±1°C and relative humidity to +6%.
Advancing the Evidence Base of Future Warming Impacts on Human Mobility in African Drylands

A better understanding of climate change impacts and resulting human responses (climate‐related human mobility) have been identified as a research priority by the climate science community. Here, we provide the basis for future research efforts by identifying knowledge gaps and consolidating published evidence in the IPCC 1.5 Special Report (SR15) with recent evidence from climate science and the literature on human mobility in African drylands, a region where migrants are particularly vulnerable to climate change. We first synthesize climatic changes and their projected impacts across the region to then contextualize the projected impacts with current knowledge regarding the effect of anthropogenic climate change on human mobility. We discuss these often indirect impact channels and argue that a systems approach is needed to address the interconnectedness of climate impacts and the cascading risks of adverse consequences for human mobility.