Miracle-dz/UniFAR-FTUs huggingface dataset:
Hyper Automation and Augmented Intelligence

The state-of-the-art technologies, hyperautomation and augmented intelligence, have the potential to completely transform the way in which organizations operate and humans interact with technology. Previously, hyper automation used advanced technologies like AI and robotic process automation to automate manually performed processes and tasks. Simultaneously, Augmented Intelligence utilizes AI to improve human intelligence and decision-making. This article investigates the potential benefits and drawbacks of these technologies, as well as the challenges and opportunities. It also discusses about the role of government and industry in promoting a responsible hyper automation and augmented intelligence development and deployment.
Benchmarking Container Technologies For IoT Environments

Containers have become the basis for many Cloud-based information technology systems in recent years. At the same time, the Internet of Things (IoT) technology is gradually being widely adopted and used. Increasing amounts of data are being produced on such devices with the expectation that they will provide edge compute resources. The question is which container technologies offer the most performant system suitable to IoT devices. In this work, we benchmark a range of container technologies on IoT devices and evaluate their performance. The overall conclusion is that the container management technology K3s and container solution Containerd are the most suitable platforms for IoT devices.
Mobile-Kube: Mobility-aware and Energy-efficient Service Orchestration on Kubernetes Edge Servers

In recent years Kubernetes has become the de facto standard in the realm of service orchestration. Despite its great benefits, there are still numerous challenges to make it compatible with decentralised cloud computing platforms. One of the challenges of mobile edge computing is that the location of the users is changing over time. This mobility will constantly alter the proximity of the users to their connected services. One solution to this problem is to regularly move services to computing nodes near the users. However, distributing the services in edge nodes only subject to user movements will result in the fragmentation of active nodes. This leads to having active nodes that do not use their full capacity. We have proposed a method called MobileKube to reduce the latency of Kubernetes applications on mobile edge computing devices while maintaining energy consumption at a reasonable level. An experimental framework is designed on top of real-world Kubernetes clusters and real-world traces of mobile users’ movements have been used to simulate the users’ mobility. Experimental results show that Mobile-Kube can achieve similar energy consumption performance to a heuristic approach that focuses on reducing energy consumption only while reducing the latency of services by 43%.
Scenario-Driven Device-to-Device Access Control in Smart Home IoT

The Internet of Things (IoT) has been widely integrated in people's everyday lives. As an infrastructure of connected heterogeneous devices, IoT has not yet achieved the seamless integration of device-to-device collaboration which is necessary for real-life home automation. Smart home IoT devices expect to exchange their collected data or status in certain circumstances, in spite of their heterogeneity, viz. working with different communication protocols, IoT platforms, middleware, data and semantics. Deploying appropriate access control models and mechanisms is of utmost importance as any unauthorized access to data could have a cascading violation of privacy, safety and security of users. In this work, we propose a novel device-to-device access control paradigm in the smart home IoT. Our approach relies on message passing as the paradigm for device-to-device interactions. We further introduce actions and scenarios reflecting the chain of events in the smart home context, which facilitates scenario-driven attribute-based access control. Each scenario is triggered by triggering events, based on previously set administrative definitions. We define totally ordered sets of triggering events using priorities to enable conflict resolution for devices which may run into conflicting commands delivered though messages in different ongoing scenarios. The viability of the proposed approach is substantiated via a formal model and an enforcement architecture, backed up by a proof-of-concept implementation which affirms a trade-off between required authorization and efficacy. Potential future challenges are explored in the context of smart home IoT platforms.
Lightweight Design for 2 m SiC Arch Mirror

For a primary mirror with the diameter 2 060 mm in large aperture based-ground telescope, parameters of new arch light-weighted structure are detailedly designed with the material SiC. Meanwhile, radius of supporting rings is also optimized. According to 18-point and 27-point Whiffle tree support scheme, mirror deformation for two models is analyzed and contrasted individually in statics (gravity effect) and thermodynamics. Considering the processing technic of SiC mirror,the 18-point supporting light-weighted structure is judged to be the preferred scheme at last. However, thermal deformation for SiC mirror is sensitive to the steady temperature difference. Referring to this problem, a measure that the supporting-structure design should be suited to the mirror thermal deformation is provided to achieve the design requirements.
A Multi-Objective Approach Based on Differential Evolution and Deep Learning Algorithms for VANETs

Intelligent transportation systems (ITS) are becoming more prominent in our society (for example, in smart cities), although a number of challenges remain to be (fully) addressed (e.g., high vehicle mobility). In this paper, we propose a scheme that combines both a cluster algorithm and a Multi-Objective Task Distribution algorithm based on Differential Evolution (MOTD-DE), designed to ensure stability and reliability in vehicular ad-hoc network (VANET) deployments. Specifically, we use Kubernetes container-base as the cluster algorithm to select various vehicles that fulfill the algorithm's conditions. Hence, this allows us to perform complex tasks on behalf of data owner vehicles. In our approach, the vehicles' information will be available on the master vehicle (data owner vehicle) when the vehicle joins the cluster, and a deep learning model is used to define the fit complexity of sub-tasks. The proposed MOTD-DE distributes sub-tasks between vehicle clusters to reduce the execution time and the resources (vehicles) used to perform a task. We also assume the sub-tasks to be independent. To evaluate our work, we propose scenarios with varying number of tasks, vehicles, CPU and memories values, and distances between cluster vehicles and data owner vehicle. A comparative summary of the evaluation findings between MOTD-DE and four other widely used approaches (i.e., Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant-Colony algorithm (ACO), and Artificial-Bee-Colony (ABC) algorithm) shows that MOTD-DE outperforms these competing approaches.
IoT Based Automated Hydroponic System Using Light Intensity for Lettuce Growth

Hydroponic is a farming that utilizes nutrient solution to grow plants instead of soil. In this type of cultivation, plants are grown using a water based nutrient solution. It helps in increasing the plant growth with efficient use of resources. There has been a lot of research and various attempts to integrate IoT into agricultural field to advance and make progress in agricultural field. This method is advantageous in a number of ways over soil farming. In this work, we use Deep Water culture (DWC) which is a type hydroponics farming. Throughout the growing process, it is necessary to control several factors such as air circulation, light intensity, water quality and so on. The parameter values of temperature, humidity, atmospheric pressure, electrical conductivity and light intensity is read using sensors which are integrated into the system. For oxygenation of the nutrient solution, an air-stone is added and this is connected to an air-pump. Sunlight is essential for plant growth but here it is supplemented with artificial lighting. The lighting is altered and monitored to identify better results.
Intensity modulation and chirp of 1.55-/spl mu/m multiple-quantum-well laser diodes: modeling and experimental verification

A laser model based on rate equations for the simulation of high-speed optical access networks using directly modulated lasers is presented. The laser model is implemented into a commercial microwave simulator (Microwave Design System (MDS), Hewlett Packard). The extraction of DC- and small-signal RF parameters of the laser is described. The laser model is verified by the very good agreement of simulated and measured large-signal intensity and chirp waveforms. Model validity is further confirmed by comparing measured and simulated 10-Gbit/s eye diagrams after up to 100 km of standard fiber.
Convolution neural network with multiple pooling strategies for speech emotion recognition

Speech emotion recognition (ASC) is an important part of speech recognition. The convolutional neural network (CNN) has shown excellent performance in ASC. However, the Traditional CNN models usually use the last fully connected layer as the output of the model. Lack of time- frequency related information research results in an inadequate depth of emotional information mining. Moreover, CNN only focuses on the convolution region in each convolution and cannot focus on the global features. To better explore the recognition effect of speech features in CNN, a CNN model with multiple pooling strategies is proposed. Mel-spectrum is the input to the model. CNN learns emotional details. In addition, three global average pooling layers (GAP) connect different pooling layers to reduce the dimensions of different tensors. Then, an Average Pyramid Pooling (APP) layer is used to fuse all features. Finally, a SoftMax classifier is used to classify different emotions. Experiments prove that the improved CNN model has a better recognition effect than the traditional model.
A Comprehensive Survey on Sales Forecasting Models Using Machine Learning Algorithms

Sales Forecasting is a most commonly used in marketing. Nowadays a large number of companies are using this technique to manufacture their product. In this we are going to study about the usage of different Machine Learning Models and Techniques used for sales prediction. The overall study of models and techniques is to increase the efficiency of future sales prediction. Nowadays for any product there are lakhs of reviews were generated by the users on different products in the market. Which confuses customers to make decision whether to buy product or not. And for a specific company to study overall reviews is hard to make product manufacture. This study mainly deals with arranging the opinions of different customers and different kinds of techniques used in sales forecasting. The present work uses mainly four machine learning algorithms namely Support Vector Machine (SVM), Decision Tree (DT), Linear Regression, Random Forest, and K-Nearest Neighbors, K-means Clustering, Logistic Regression for classifying reviews. The forecasting accuracy of each algorithm is evaluated with the Root Mean Square Error (RMSE). The study found that Random Forest is the best model because it had lowest Root Mean Square Error (RMSE) compared to other model and for classifying reviews the Logistic Regression is giving accurate result.
A Deep Convolutional Neural Network Model for Nonlinear Drift Fault Diagnosis in Sensors using Transfer Learning and Multiresolution Analysis

Reduction in manufacturing cost due to the implementation of advanced technology resulted in the large-scale deployment of wireless sensors. As the sensors are subject to harsh operating conditions they develop faults. The faulty sensor readings and the faulty sensors need to be isolated in real-time and remedial measures to be taken for maintaining system stability. Drift faults are slow-changing constant faults that get induced into the sensor measurements and are difficult to isolate. Drift faults can be linear or may be nonlinear. Though studies have been conducted for linear drift fault isolation, studies related to nonlinear drifts are limited. Studies have achieved a very high level of accuracy while deploying Deep Convolutional Neural Networks (DCNNs). Continuous Wavelet Transform (CWT) has also shown promising results as a tool for noise reduction and feature extraction. Keeping these things in view, in the present study the ability of pre-trained DCNN through transfer learning is exploited, and scalograms as the feature extractor from the sensor signals. Three nonlinear drift fault modes are simulated to test the sensitivity of the DCNN models under different fault scenarios. The performance of the proposed model for the classification of nonlinear drift faults is compared with other models in terms of accuracy and loss. The proposed Inception V3-based DCNN model achieved the highest accuracy of 99.05% in classifying nonlinear drift faults. An algorithm for simulating nonlinear drift faults is also proposed.
SVM Multi-Task Learning and Non convex Sparsity Measure

Recently, there has been a lot of interest around multi-task learning (MTL) problem with the constraints that tasks should share common features. Such a problem can be addressed through a regularization framework where the regularizer induces a joint-sparsity pattern between task decision functions. We follow this principled framework but instead we focus on ‘p i‘2 (with p • 1) mixed-norms as sparsity-inducing penalties. After having shown that the ‘1 i ‘2 MTL problem is a general case of Multiple Kernel Learning (MKL), we adapted the available ecient tools of solving MKL to the sparse MTL problem. Then, for the more general case when p < 1, the use of a DC program provides an iterative scheme solving at each iteration a weighted ‘1 i ‘2 sparse MTL problem.
Continuous-wave THz imaging

The first THz imaging spectrometer based on continuous-wave THz radiation is presented. The new setup is less expensive than conventional time-domain imaging systems that comprise femtosecond lasers. The system uses a two-colour external-cavity laser diode. Hence it is much more compact as compared to systems based on optically pumped solid-state lasers. As an example we investigate a human liver with metastasis.
Fusion of Multimodal Textual and Visual Descriptors for Analyzing Disaster Response

People are using social media (SM) sites like Twitter, Facebook and Instagram more frequently to report senous catastrophes or disaster situations The scale of the event, the number of victims, and the extent of the infrastructure damage are all frequently revealed in mullimodal data published on these platforms The information can give local govemment off$\iota$cials and aid otganizations a comprehensive ovenfiew of the situation. It can also be used to eff$\iota$ciently and quickly plan relief efforts The goal of the suggested effort is to solve the diff$\iota$cully of locating pertinent infomwtion among the numerous published SM posts In particular, embeddings that encapsulate the relatedness of multimodal SM post in the context of disaster occurrences are created using pretrained deep learning models One dataset that offers annotations in addition to textual and visual data is the Multi-modal Datasets from Natural Disasters named as CnsisMMD captured the posts from social media, which can be used by researchers to create cnsis response systems This study has examined CnsisMM$D'$s multi-modal data on seven significant natural disasters, including earthquakes, floods, humcanes, and f$\iota$res, and developed an eff$\iota$cient model for categonzing social media data into useful and non-useful categones Proposed model makes use of transfer learning approach for extraction imwge features using DenseNet and transformer-based BERT model for extraction of textual features. This multimodal fusion approach has achieved the accuracy of 85.33 %,which isproved to be better than state of art techniques
A STUDY OF SEMIREGULAR GRAPHS

2002 2 ACKNOWLEDGMENTS I would like to thank all my mathematics professors here at Stetson: for his patient support while advising me with my schedule and with my senior research. I also want to thank Mrs. Bast and Ron Louchart for encouraging me to enjoy mathematics throughout middle and high school. And, lastly, I would like to thank my parents for their constant support and encouragement throughout my life.
Vertical-cavity surface-emitting lasers with low-ripple optical pumping windows

A general technique for numerically optimizing the optical admittances in GaAs QW vertical-cavity laser structures is used to suppress the interference ripple in the typical reflectance/transmittance spectra. This technique is applicable to any vertical-cavity device whose photonic properties at various wavelengths requires modification for specific applications. In this paper, we report the use of this optimization method to enhance the coupling of pump light into 850-nm vertical-cavity surface-emitting lasers (VCSELs). We have designed and fabricated novel lasers which contain a wideband window of low reflectance amidst the typical interference fringe spectrum. The 750-800-nm region for the low-ripple design has an average reflectance of 5%; the peak-to-peak amplitude of the ripple is 0.25%. The sensitivity of these devices to temperature variations and layer-thickness manufacturing variations is also studied. The low-ripple pump window shifts at a rate of 0.036 nm//spl deg/C, the peak-to-peak ripple of the reflectance varies less than 2%, and the pump bandwidth remains constant, over temperatures ranging from 0/spl deg/C to 100/spl deg/C. The low-ripple structure substantially reduces the temperature and wavelength variation of the pump-field overlap by creating a window of nearly constant reflectance.
A study of the structure of mid‐latitude depressions in a numerical model using trajectory techniques. I: Development of ideal baroclinic waves in dry and moist atmospheres

Trajectory-tracing techniques are used to investigate the structure and air flow of developing baroclinic waves simulated by a numerical atmospheric forecast model. Three stages are described. The structure of the linear stage is explained with reference to the normal mode theory of baroclinic instability. In the mature stage, flow separation zones form which are identified as warm and cold fronts. At the occluded stage, circulation of cold air round the centre of the depression leads to changes in the structure of the cold front. When the initial state is nearly saturated, the overall development is speeded up, and at the mature stage the rate of deepening of the depression is doubled at the surface. In the occluded stage formation of a warm air conveyor belt is observed. Comparisons are made with previously published integrations by specially constructed models in order to establish a basis for comparison of forecasts from real data which will appear in a future paper.
Self-sensing properties of carbon-polymer composite (CPC) actuators

CPC (carbon-polymer composite) is a type of low voltage electromechanically active material, which is often built using two layers of electrodes containing nanoporous carbon separated by a thin ion-permeable polymer film; ionic liquid is used as electrolyte. In cantilever configuration, while low voltage (3 V) is applied to these electrodes, the CPC sheet undergoes bending. To date, virtually no research into sensing properties of these materials has been conducted. In order to determine the tip displacement (curvature) of the CPC actuator, change of surface resistance in the process of bending is measured. Within the scope of this paper, it is also to investigate whether the acquired signals are feasible for use as a feedback to the actuator's driving mechanism and thus creating a self-sensing CPC device. Experimental data is presented to report that both resistive and capacitive effects are present on surface electrodes and alter during the actuator's work-cycle.
An Evolutionary Approach to the Identification of Linkage Curves

Identification of an appropriate linkage curve is an important step in the application of linkage mechanisms. This paper applies a new identification method, evolutionary identification, to search tbr the curve based on the analogy of species evolution. In this method, features of general linkage curves are defined and represented as evolutionary factors, or “genes”. Then the evolutionary factors are operated through evolutionary computation. This method can automatically identity the curve with the maximum similarity and thus obtain the structural parameters of the curve from the atlas base of linkage curves. General principles are also given for the evolutionary identification approach as a new identification method. The proposed method has been coded and applied in real linkage design problems. Further research topics are also discussed.Copyright © 2002 by ASME
American Spinal Injury Association (ASIA) Impairment Scale (AIS) Conversion Underestimates Neurological Recovery Following Traumatic Spinal Cord Injury

total motor score were signi ﬁ cant predictors of motor score recovery. However, no variables were prognostic when utilizing AIS conversion as the endpoint. CONCLUSIONS: AIS conversion underestimates true degree of functional improvement following SCI. Changes in full motor score is a more robust outcome metric in the evaluation of factors prognostic of neurological recovery following injury.
Hemangioblastoma of the medulla oblongata that caused isolated fourth ventricle after stereotactic radiosurgery: A case report

Isolated fourth ventricle is a rare complication following shunt insertion of the lateral ventricles for hydrocephalus. The present report describes a rare case of a hemangioblastoma of the medulla oblongata that caused isolated fourth ventricle due to intraventricular deposition of fibrin. A 34-year-old man presented with headache a month before admission. Magnetic resonance imaging indicated multiple tumors in the medulla oblongata and the bilateral cerebellar hemisphere with surrounding edema, and the patient was diagnosed with hemangioblastoma. The patient began to develop progressive headache and nausea after stereotactic radiosurgery, and computed tomography showed obstructive hydrocephalus. Endoscopic third ventriculostomy was performed, and the intraoperative view of this showed that the walls of the lateral and third ventricles were covered with a white membrane-like substance. Endoscopic third ventriculostomy and then ventriculoperitoneal shunt did not improve the hydrocephalus. The patient's consciousness deteriorated due to isolated fourth ventricle and upward herniation. The patient underwent posterior fossa craniotomy and the tumor in the medulla oblongata was removed via a telovelar approach. Intraoperatively, the fourth ventricle was filled with a white membrane-like substance, which was surgically removed and pathologically diagnosed as fibrin. The patient's consciousness and obstructive hydrocephalus improved after surgery. The present case suggests that isolated fourth ventricle may occur after VP shunt placement for the hydrocephalus with hyperproteinorachia.