Miracle-dz/UniFAR-FTUs huggingface dataset:
A Qualitative Study on the United States Internet of Energy: A Step Towards Computational Sustainability

The burgeoning growth of Big Data not only matures and improves the data management efficiency and useful information extraction techniques, but also motivates the computational science researchers to come up with a new method or solution that can be repurposed for problems across the domain. Computational Sustainability joins this movement for a transferrable computational technique for sustainable development and a better future. Internet-of-energy (IoE) – leveraging IoT to smart grids associated with advanced analytics – is one of the prominent efforts in this regard. This paper presents a qualitative analysis on the elements of the energy and power management ecosystem in the United States. This qualitative study includes the Grid Overview of the United States; Weather and Climate and its impact on the entire energy generation and consumption dynamics; Peak Load Forecasting and its techniques and burgeoning challenges; Variable Renewable Energy, its reliability challenges and how we can take advantage of this variability; Commodity Prices and its criticality; Energy Disaggregation and its impact on consumption-awareness; and Generation Expansion and Decision Analysis. Besides, IoE integration, associated trade-offs, challenges, research opportunities and transferable computational techniques are addressed in this communication. Furthermore, schematics and quantitative analysis are presented in support of this study.
IoT based System for Smart Agriculture

Agriculture is the most traditional activity over time. Since the beginning of it, agriculture has suffered many changes to improve productivity and quality of crops. Some of the first significant improvements have been remarked when machines and new tools such as irrigation systems, harvest machines, farmland clearing machines were introduced in the primitive agriculture, where these activities were performed mainly by humans and animals. Over time, agriculture has been affected by weather disasters (such as storms or extreme temperatures) and by natural disasters (such as pests and plant diseases). Thus, the next step in the development of the agriculture domain was to propose the Internet of Things (IoT) solutions for monitoring of many parameters for better precision agriculture. Such a system would provide useful information on plant growth, crops' diseases, and soil properties that are a benefit for crops. This paper describes a possible solution for a more reliable IoT-based system using Libelium for Smart Agriculture to monitor the parameters that have a direct impact on crops. Moreover, the monitoring system aims to manage agricultural issues related to irrigations and analyses the effect of the measured parameters on agriculture, helping the farmers to have healthy crops.
An New Algorithm-based Rough Set for Selecting Clustering Attribute in Categorical Data

Several algorithms strategies based on Rough Set Theory (RST) have been used for the selection of attributes and grouping objects that show similar features. On the other hand, most of these clustering techniques cannot deal tackle partitioning. In addition, these processes are computationally complexity and low purity. In this study, the researcher looked at the limitations of the two rough set based techniques used, Information-Theoretic Dependency Roughness (ITDR) and Maximum Indiscernible Attribute (MIA). They also proposed a novel method for selecting clustering attributes, Maximum mean Attribute (MMA). They compared the performance of MMA, ITDR and MIA technique, using UCI and benchmark datasets. Their results validated the performance of the MMA with regards to its purity and computational complexity.
HBLP: A Privacy Protection Framework for TIP Attributes in NTTP-Based LBS Systems

Nowadays, location-based services are being widely popularized due to their massive usage in current and emerging technologies. These services are based on searching out areas of interest which are likely to be accessed by users. Despite helping users worldwide, Location Based Services (LBSs) Systems endanger users’ privacy because a user must provide personal information in order to use the services. Users thus become easy prey for assailants to access their social and personal lives. This problem is a giant issue for contemporary technologies because they are increasingly being used with the passage of time. Many existing solutions have attempted to resolve the challenges, but they face some serious dilemmas regarding the preservation of privacy. In order to address the privacy challenges in LBS systems, in this paper we have introduced a new Hierarchy Based Location Privacy (HBLP) model that protects the user’s privacy, including the user’s query time and identity and location information. The proposed model protects the user’s privacy by using pseudo identity exchange, an aggregation protocol, and the concepts of Forest User (FU), Tree User (TU), and Child Users (CU) with k-anonymity and t-closeness, which is a reasonable combination for privacy provision for a user’s query time, identity, and location. In order to evaluate the privacy protection level, we implemented the HBLP model in a Riverbed (Opnet) simulation and compared the results with existing state-of-the-art privacy-provisioning methods. The results showed that HBLP protected all the privacy attributes when a user interacts with an LBS system.
Laplacian Score and Top Scoring Pair Feature Selection Algorithms

the method by which data from the gene of a human body is converted into a protein, where it can be used to infer how active a gene is called gene expression. Recently, gene expression is applied for disease identification in human body. As the gene data is high dimensional when compared with the number of samples available, gene expression data can be used for the prognosis of diseases like cancer, which is caused when the cells undergo an uncontrollable division and spread into the surrounding tissues. In the case of a prediction system, we need effective datasets to deliver maximum prediction accuracy. In order to transform data from high dimensional data to the most effective data Feature selection is used. Feature selection selects the most appropriate or required features into consideration. This technique has different methods by which it can be executed, they are open-loop method/filter method, close-loop method/wrapper method, embedded method, and the ensemble method. The proposed research work is based on two feature selection algorithms namely, Laplacian score, an open-loop method, and Top scoring pair, a hybrid method.
FPGA Based Real-Time Face Authorization System for Electronic Voting System

in this paper, we have developed a standalone authentication application by using a face recognition technique with NI MyRIO and LabVIEW, along with the integration of the OpenCV library and NI VISION tool kit. The designed system can recognize and allow the authenticated person(s) keeping in view of existing systems limitation in a real-time environment, specifically caused by delay. The proposed solution is designed followed by a performance perspective and comparative analysis through Matlab, Raspberry pie, LabVIEW, and FPGA. The overall system is a combination of hardware and software, employing parallel processing techniques and modules such as NI MyRIO FPGA. The proposed designed system is tested in real-time scenarios and can be tailored to the electronic voting system via NADRA database.
Factors for Continued Interaction between the Kuwaiti Government and Citizens via Social Media Networks: A Proposed Model

With advancements in social networking, there is a need for transparency in the changing nature of the interaction between governments and citizens. Recent studies have discovered that the perception of government transparency greatly influences the interaction between citizens and governments and citizen trust in government via social media networks. Surveys indicate that Kuwaiti citizens lack interaction with their governments on social media networks. This problem is not straightforward to solve; Europe also faces the same issue. Governments can take advantage of social media networking to meet their operational objectives and improve service quality. Therefore, a model is needed that increases the interaction and communication between citizens and their governments. This paper presents a new model for factors influencing the continued interaction between the Kuwaiti government and its citizens via social networks (see Figure 2) based on Delone and McLean information system (IS) success model (2003) combined with the unified theory of acceptance and use of technology and both the trust and loyalty factors.
Melanoma Skin Cancer Detection Based on ABCD Rule

Skin cancer is the most common cancers in the last years, especially in the human body; the Melanoma is the most destructive type of skin lesions. Detect cancer is important at the initial stage, but only an expert dermatologist can detect which one is non-melanoma and melanoma. Computer-aided diagnosis (CADs) application to skin cancer is relatively understudied. The purpose of this paper is the automated detection of Melanoma via digital image processing. In this project, the algorithm consists of automatic ABCD (asymmetry, border irregularity, colour, and dermoscopic structure) rule of dermoscopy lesions images is implemented. Before that, we use hair removal as a pre-processing step which is based on morphological filter and thresholding. Finally, the lesions are classified as either melanoma or benign. The used dataset is containing 200 dermoscopic images, where 120 are benign lesions and 80 malignant melanomas. The proposed method shows an accuracy of 93.2%, 92.59% specificity, and 90.15% sensitivity.
Structuring a Folksonomy in a Community of Questions and Answers

Knowledge communities are composed of individuals who share the same interests and voluntarily work together to expand knowledge and understanding of a domain through learning and sharing. When a user asks a question in a knowledge community, he or she may usually add tags to categorize the question. These tags make it easy for experts to find the question and then answer it. One of the problems with this tagging system is that there is no hierarchy to aid in the search for broader themes. This study proposes a way of hierarchizing the folksonomy of an online knowledge community. In order to verify the applicability of the proposed approach, we used the Stack Exchange’s biology community. Through a crowdsourcing evaluation it was possible to show that the developed approach performed better than other heuristics and it was also possible to identify quality issues on the description of WordNet synsets.
Target Imaging Based on Generative Adversarial Nets in Through-wall Radar Imaging

For multi-input multi-output (MIMO) through-wall radar imaging (TWRI), multipath ghosts, side/grating lobe artifacts and wall penetration effect degrade the imaging quality of the obscured targets inside an enclosed building, there in hindering target detection. In this paper, an approach based on generative adversarial nets (GAN) is proposed to achieve multipath ghosts, side/grating lobe artifacts and wall penetration effect suppression with regard to MIMO TWRI. Specifically, the whole task is divided into two steps (Firstly, multipath ghosts and wall penetration effect are suppressed but the side/grating lobes are preserved. Secondly, side/grating lobes are eliminated.) Then a GAN network is applied to solve those two steps. Extensive electromagnetic simulations and comparisons demonstrate that the proposed approach achieves better suppression of multipath ghosts, side/grating lobe artifacts, wall penetration effect and other significant superiorities, including priori wall information not being required and robustness for different array deployments and building layouts.
A Review on Feature Extraction Techniques for Sentiment Classification

Sentiment analysis is used for social media supervising. It is used to analyze comments, recommendations provided by the reviewers. Depending on this analysis, products will be purchased through either online or offline. Machine learning is an area of artificial intelligence which gives computers the capability to learn without using exceptional computation. Traditional machine learning algorithms used for feature extraction and sentiment classification leads to scalability and computational problems while dealing with a huge amount of data. Deep learning is a field of approach concerned with neural implementations. It is a nonscalable and accurate technique for sentiment classification, automatic feature extraction and dimensionality reduction. Machine learning techniques are easier to implement but give less accuracy compared to deep learning. Deep learning methods are complex to implement from a human point of view, but machines require complex structures to learn large data set easier.
Modeling and simulation of MEMS test structures using finite element method for stress mapping in thin film coatings

Thermal stress, which is due to the mismatch of coefficients of thermal expansion in multilayered structures, is often responsible for failure of thin film devices. MEMS test structures are quick and inexpensive approach for mechanical characterization of thin films during the fabrication process. The research presented here describes a test structure for in situ stress measurements based on rotational beam technique via finite element method analysis. A model was developed to induce tensile and compressive stresses on blanket thin film coatings. Evolution of the tensile/compressive stress induces certain displacement of the thin film as a function of applied temperature. The rotation of the indicator beam due to deflection/displacement of the blanket thin film is further investigated. The optimized layout of the test structure is proposed considering the stress profile and torsion of the indicator beam.
A 39-GHz 64-Element Phased-Array Transceiver With Built-In Phase and Amplitude Calibrations for Large-Array 5G NR in 65-nm CMOS

This article presents the first 39-GHz phased-array transceiver (TRX) chipset for fifth-generation new radio (5G NR). The proposed transceiver chipset consists of 4 sub-array TRX elements with local-oscillator (LO) phase-shifting architecture and built-in calibration on phase and amplitude. The calibration scheme is proposed to alleviate phase and amplitude mismatch between each sub-array TRX element, especially for a large-array transceiver system in the base station (BS). Based on LO phase-shifting architecture, the transceiver has a 0.04-dB maximum gain variation over the 360° full tuning range, allowing constant-gain characteristic during phase calibration. A phase-to-digital converter (PDC) and a high-resolution phase-detection mechanism are proposed for highly accurate phase calibration. The built-in calibration has a measured accuracy of 0.08° rms phase error and 0.01-dB rms amplitude error. Moreover, a pseudo-single-balanced mixer is proposed for LO-feedthrough (LOFT) cancellation and sub-array TRX LO-to-LO isolation. The transceiver is fabricated in standard 65-nm CMOS technology with flip-chip packaging. The 8TX–8RX phased-array transceiver module 1-m OTA measurement supports 5G NR 400-MHz 256-QAM OFDMA modulation with −30.0-dB EVM. The 64-element transceiver has a EIRPMAX of 53 dBm. The four-element chip consumes a power of 1.5 W in the TX mode and 0.5 W in the RX mode.
Industry 4.0 in Production and Household Waste Processing

The article considers the issues of circulating economy in the field of industrial and household waste processing with the aim of producing gas motor fuel for gasgenerating vehicles and transport and technological equipment. The authors suggest the vehicle enabling placing a modernized gas generator with automated feeder for solid fuel. Modern digital technology allows to trackrunning the installation. The automated gasification process runs without intermediate stops of the vehicle, thus reducing weight, dimensions of the gas generator, increasing useful volume and body area, increasing mileage at one gas station, improving technological and environmental characteristics of the engine, and reducing the cost of transportation.
Acoustic Scene Classification Technique for Active Noise Control

This paper works on the idea of integrating the acoustic scene classification technique in active noise control (ANC), whereby the major difficulty lies in the limited dataset available from an ANC system in use. A large-scale acoustic scene dataset recorded by a different acoustic device has to be considered. This leads to the research problem of acoustic scene classification by mismatched devices. Hence, an ensemble of the convolutional neural networks (CNNs) is demonstrated with a novel data augmentation method. Monaural samples are processed by the head-related transfer functions (HRTFs) of 24 azimuths to add in the artificial spatial information. Every two symmetrical azimuths are then paired together to provide a tempo-spectral feature consisting of 4 channels. 12 CNNs are trained with respective features. Finally, different ensemble strategies are carried out and their classification accuracies are compared. When the random forest is used as the meta-learner, the ensemble of CNNs achieves the best classification accuracy of 65.1% over 10 acoustic scenes.
Material Properties Sensitivity Analysis Based on Thermomechanical Analysis on Warpage for Ball Grid Array Semiconductor Packaging

With the advancement of technology, semiconductor devices become more complex to satisfy the need for more features while reducing the size. For a component with varying material parts such as the semiconductor package, it is prone to warpage because of the different material properties present in the materials, mainly the thermal expansion coefficient. Package warpage emerge from varying temperatures from assembly processes which affects the performance of the device. This paper evaluates the product warpage by considering material selection through varying the material properties of different components. A validated model was employed using the material properties and dimensions from a previous study that compared a two-dimensional finite element model results to actual experimental data. The results have shown to be not significantly different from the two-dimensional model and experimental results. The developed model was then used to assess various material properties and identify the factors that could greatly affect the deformation in the semiconductor package.
Dual Band Notch Planar Patch Antenna for UWB Applications

A novel microstrip patch antenna with dual band notch operation and coplanar waveguide feed (CPW) is proposed in this paper. The initial antenna design efficiently covers the ultra-wideband (UWB) range from 3.1 GHz to 10.6 GHz with VSWR<2. Afterward, the antenna patch is embedded with slotted structures to introduce notches. The upper WLAN (5.54 GHz to 5.82 GHz) and WiMAX (3.35 GHz to 3.6 GHz) bands are notched in the UWB antenna by introducing two U-shaped slots in opposite directions in the radiating patch. From the simulation results obtained in HFSS, it is clearly shown that the proposed design exhibits good performance. The antenna is capable of suppressing two interfering bands in its UWB operation, and it has omnidirectional radiation patterns with high gain. The current density plots are also presented to show the effective area of the antenna to achieve band suppression. The compact size of the proposed antenna makes it suitable for multiple wireless miniaturized devices.
Secured Map Building using Elliptic Curve Integrated Encryption Scheme and Kerberos for Cloud-based Robots

Cloud computing has transformed the way of utilizing the computing, storage and network resources as per the user demand. Consequently, the field of robotics performs high complexity tasks that exploit the clouds with the capability to build low-cost light weight and intelligent robots. Recently various researchers have been emerged in the cloud robotics field which are related to offloading computations to the cloud infrastructure, storing and sharing knowledge, coordination and collective learning among robots. However, there are issues related to security and privacy that needs to be addressed while deploying the robotics application in the cloud. Significant research attention is required to build a secure cloud robotic infrastructure. The foremost factor of our research entails the development of standard web services that will allow heterogeneous robots to execute the computationally intense algorithms like map building as a service over the cloud. We have proposed the model that presents the mutual authentication and encryption mechanism for getting access to the hosted robotic services. For mutual authentication, we have used Kerberos module and ECIES (Elliptic Curve Integrated Encryption Scheme) for data encryption. Moreover, we have also performed the cryptanalysis of the proposed protocol by using a Proverif tool. After the cryptanalysis, it is found that our system can also withstand against various type of attacks.
Encoder–Decoder Architecture for Crop-Weed Classification Using Pixel-Wise Labelling

Agricultural growth is an important pathway in development of any country. Its productivity contributes in full filling the basic need of the human society. The productivity is therefore must be smoothen to provide quality and quantity. Reduction in usage of chemicals like pesticides and herbicides to provide quality and quantity. The major factor that affect the quantity is the presences of weed in the crop field. The nutrient present in the soil is therefore observed by both the weed and the crop. Manual removal of weed from crop is tedious, time consuming and costly. Spraying of herbicides over the field affects the quality of the crop. Emergence of technology in the agriculture field paves the path for selective spraying and robot removal of weed, which requires high accuracy classification of crop from weed. Therefore an Encoder-Decoder architecture based on VGG16 architecture is used for the pixel-wise segmentation. The architecture consists of convolutional layers with ReLU, Normalization layer and max-pooling layer.
Modelling and Simulation of Asymmetric Missile Releasing during Flight Case

Air superiority is a crucial thing when it comes to modern defense and conventional war because it enables one side to optimize its military operation and inhibit the enemy’s operation at the same time, whether it’s on the ground or at the sea. This enhance the development of multirole fighters which one of its properties is high maneuverability. On the other hand, this aerodynamically unstable design requires advanced flight control system which makes the development of flight control system a highly important matter. In this paper, the model of a fighter aircraft flight dynamics and the model of disturbance in terms of forces and moments caused by the missile launching are built and simulated on the open-loop system initially. From open-loop simulation, the aircraft becomes unstable after releasing right-wing missile. Furthermore, same processes are operated on two different closed-loop systems to obtain proper controller that can compensate the effect of missile releasing. Both closed-loop system are the aircraft equipped with Stability Augmentation System and aircraft equipped with Stability Augmentation System, Basic Autopilot System, and Flight Path Control System. The results show that these closed-loop systems successfully improve the response of the aircraft after launching a missile since the aircraft flight trajectory is still maintained in desired value.