Miracle-dz/UniFAR-FTUs huggingface dataset:
Authorization Secured Dynamic Privileged Escalation

To approve of action first identity of the requestor needs to be established which is authentication. A lot of research work done to secure the identity of IoT device focusing either on bootstrapping credentials, securing it to ensure no tamper can happen in the first place, and if forbids it happens ever, detect the compromised device and block it from further grants of access. Focus on authentication further goes deeper in the advancement of research at the protocol level to optimize the number of bytes under transfer as well as processing time needed and latency. Authorization is indeed mentioned but grossly left out as custom needs and implementation specific to the organization. In this paper, we propose solution to address the concern where a non-statically preconfigured IoT device needs to connect and ask permission/authorization to gain access or perform a certain operation. We also attempt to provide a solution to provide escalated permission to perform an action in a controlled order. Here the solution focuses on allowing a low privileged device is eventually authorized to perform operation which under normal circumstances is not allowed to, however allowed by due to association of device having privilege. Implementation done on Raspberry Pi to simulate IoT client and server written in Java as IAM server to demonstrate the feasibility of solution.
Analysis of the stability of Discrete-time Markov Jump Linear Singular Systems

This paper investigates the stability for Markov jump linear singular system. It yields that mode to mode causality and regularity are sufficient conditions. Mean square stability is analyzed using the spectral radius of a certain matrix. In addition, the equivalence between the different definitions of stability is presented. Finally, a simple example is set to check the results.
A Self-Organizing Map Approach for Time-of-Use Feature Based Wind Resource Clustering

The variable nature of wind and solar photovoltaic renewable energy sources, especially under high grid penetration levels, impacts adversely on the electrical grid operations. These impacts can include increased use of costly peaking generation as well as increased ramping of conventional generation. The operational challenges posed by variable renewable energy can be mitigated by optimal geographic siting of the renewable energy plants. Such siting optimization studies typically use large, high-resolution temporospatial datasets that are difficult to interpret and present great computational burdens. This paper investigates a data-reduction methodology using a self-organizing map approach for the clustering of temporospatial wind speed profiles with the view to reduce the dimensionality of the dataset. The clustering algorithm is implemented using the statistical means and standard deviations of the associated wind speed profiles for pre-defined time-of-use periods. The results obtained with the self-organizing map approach are compared with results obtained using the k-means and k-medoids algorithms. Overall, the results for the three clustering methodologies show good agreement. The clustered resource maps obtained with time-of-use statistical features are of value for siting and interpreting wind generation capacity such that the aggregated power generation profile is optimized in terms of time-of-use grid support.
High-Precision Self-Calibrating Current Transformer With Stray Capacitances Control

This article shows the design of a simple construction two-stage current transformer with ratios from 5 A:5 A to 2000 A:5 A. It can be self-calibrated. Primary windings are separated into sectors to allow series–parallel configurations. Beginning with 5 A:5 A-ratio self-calibration, the higher ranges relate their errors to this first one, considering parasitic effects. Stray capacitances are considered as the major source of error when changing the configuration of the primary winding. New methods to calculate and compensate them are proposed, and electronic devices are used to reduce ratio error and phase displacement to few <inline-formula> <tex-math notation="LaTeX">$\mu \text{A}$ </tex-math></inline-formula>/A and <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>rad. The participation of this transformer as the UTE standard in an international comparison confirmed the calculated performance.
Deep Learning Identification of Asthma Inhaler Techniques in Clinical Notes

There are significant variabilities in clinicians’ guideline-concordant documentation in asthma care. However, assessing clinicians’ documentation is not feasible using only structured data but requires labor intensive chart review of electronic health records. Although the national asthma guidelines are available it is still challenging to use them as a real-time tool for providing feedback on adhering documentation guidelines for asthma care improvement. A certain guideline element, such as teaching or reviewing inhaler techniques, is difficult to capture by handcrafted rules since it requires contextual understanding of clinical narratives. This study examined a deep learning based natural language model, Bidirectional Encoder Representations from Transformers (BERT) coupled with distant supervision to identify inhaler techniques from clinical narratives. The BERT model with distant supervision outperformed the rule-based approach and achieved performance gain compared with the BERT without distant supervision.
Prioritizing the Issues extracted for Getting Right People on Right Project in Software Project Management From Vendors’ Perspective

Software project management inspires and urges the spirit of software developing team members which continues until project completion. Obviously, success of every project based on right selection of team members that ensures to meet the desired requirements of any software developing project. The fundamental aim of current study is to extract and prioritize issues faced by vendors of global software development (GSD) organizations during the selection of right team with having aim to complete the project successfully. As a methodology, a systematic literature review (SLR) used for data extraction and categorization, a questionnaire survey adopted for data validation, and a hierarchical analytical process (AHP) used for prioritizing extracted findings. A total of 12 issues are extracted and grouped into 3 categories (association, teamwork, and fascination). The overall result showed that “association” is the most important category as compare to other categories. Similarly, communication and coordination issues, team’s consistency and stability issues, and lack of expertise issues, etc are highlighted as the most critical issues during selection of right people for the right project from vendors’ perspective.
Extraction and Classification of TCM Medical Records Based on BERT and Bi-LSTM With Attention Mechanism

Traditional Chinese Medicine (TCM) medical records contain huge amounts of valuable medical information. However, in terms of text mining and utilization of TCM medical records, it is always difficult to extract and classify this information effectively. It is critical to identify a method of extracting and classifying the text from TCM medical records automatically. The method used in this paper attempts to apply a short medical record classification model based on BERT and Bi-LSTM with Attention mechanism. BERT prepossessing was used to obtain the short text vector as the input of the model. Result shows that the BERT-Bi-LSTM-Attention model achieves a highest average F1 value of 89.52% in the extraction and classification of TCM medical records, and therefore represents a significant improvement in modeling.
Microwave and Millimeter Wave Power Beaming

Power beaming is the efficient point-to-point transfer of electrical energy across free space by a directive electromagnetic beam. This paper clarifies the basic principles of power beaming in simple terms, and proposes a benchmarking methodology for improving the comparative assessment of power beaming systems and technology. An in-depth historical overview tracing the worldwide progress in microwave and millimeter wave (mmWave) experimental demonstrations over the past 60 years shows clear evidence of a significant increase in activity during the last 5 years. In addition, a review of progress in scalable rectenna arrays for the reception of microwave power beaming shows sufficient maturity for new research to initiate on the ruggedization, productization, and system integration aspects of the technology. A review of regulatory issues including spectrum management and safety indicates the need for additional technical solutions and international coordination. Breaking results reported in this paper include 1) data from the first in-orbit flight test of a solar-to-RF “sandwich module”, 2) the construction of multiple US in-orbit demonstrations, planned for 2023 launch, that will demonstrate key technologies for space-based solar power, and 3) a 100-kW mmWave power beaming transmitter demonstrating inherent human life safety.
Thermal Power Plant Detection in Remote Sensing Images With Saliency Enhanced Feature Representation

The detection of Thermal Power Plants (TPPs) is a meaningful task for remote sensing image interpretation. It is challenging due to the variations in appearance and complex structures. In this article, we propose a novel end-to-end detection framework for TPPs based on deep convolutional neural networks. Specifically, a large-scale TPPs Dataset for Detection (AIR-TPPDD) in remote sensing images is presented. AIR-TPPDD is collected from the Google Earth worldwide, and provides detailed annotations including names and locations. To the best of our knowledge, this is the first publicly available dataset for TPP detection. Then, based on Faster R-CNN, a saliency enhanced module is proposed to strengthen the ability in representing complex structure, as well as alleviate distractions in the background. In addition, we design a multi-scale feature module to adapt to the large size range of TPPs. Experiments show that the proposed method outperforms the state-of-the-art methods and achieves 76.7% mAP on the challenging AIR-TPPDD.
Joint Compressed Sensing and Spread Spectrum Through-the-Wall Radar Imaging

High-resolution radar imaging has to be accomplished within the constraints of aperture size, frequency, and acquisition time. To achieve effective and reliable imaging, we introduce a compressed sensing (CS) technology to spread spectrum through-the-wall radar imaging. Using the sparseness of the spread spectrum through-the-wall radar echo in the correlation domain and the sparsity of the target information in the imaging region, the feasibility of CS technology for reducing the number of observation points and the number of samples per observation point is verified. Moreover, considering the high sensitivity of CS technology to clutter, receiver cancellation is adopted to remove the strong clutter information present in echo signals. The results of numerical simulations show that CS technology can provide very efficient sampling, thereby significantly reducing the amount of data required. Compared to back-projection imaging based on traditional sampling technology, the proposed CS technique can obtain almost the same satisfactory resolution and quality imaging results using only 0.72% of the total data. This will reduce the hardware costs and significantly shorten the data-acquisition and data-transfer times.
An Approach to Minimum Attention Control by Sparse Derivative

Minimum attention control proposed by Brockett is an important formulation for resource-aware control, while his problem formulation and the underlying optimization problem that he proposed is in general very hard. In this paper, we propose a computationally tractable design method of minimum attention control based on promoting sparsity of the derivative of control. The optimal control problem is formulated as L0 norm minimization of the time derivative of control under the constraint that the derivative is bounded by a fixed value. This is a non-convex problem, and we propose L1 relaxation for linear systems to obtain optimal control by efficient numerical computation. We then show equivalence theorems between the L0 and L1 optimal controls. Also, we present an example of feedback control for the first-order integrator, that illustrates the proposed methodology.
Driving Behavior Classification Based on Oversampled Signals of Smartphone Embedded Sensors Using an Optimized Stacked-LSTM Neural Networks

Driving behavior classification is an essential real-world requirement in different contexts. In traffic safety, avoiding traffic accidents by taking corrective actions against aggressive behaviors is necessary to protect drivers. Similarly, in the automotive insurance industry, distinguishing between driving behaviors is essential to adopt usage-based insurance (UBI) policies. Also, in the ridesharing industry, monitoring and evaluating driving behaviors is critical for risk assessment and service improvement. This research presents a deep learning-based solution for driving behavior classification using an optimized Stacked-LSTM model based on the signals of smartphone embedded sensors generating two different classification models: three-class and binary. Three-class classification distinguishes between normal, drowsy, and aggressive behaviors to support advanced driver-assistance systems (ADAS). Binary classification differentiates between aggressive and non-aggressive behaviors to support commercial applications, such as ridesharing services and automotive insurance services based on UBI. Our time-series classification models have been evaluated on the public UAH-DriveSet dataset. Using the proper number and type of features, the optimum factor of upsampling for the raw signals, and the optimum time-series window size, our proposed Stacked-LSTM model made a breakthrough in the F1-score when applied to the aforementioned dataset. The achieved scores are 99.49% and 99.34% for the Three-class and binary classification models, respectively. Comparisons with state-of-the-art models, our three-class classification model surpassed the highest published F1-score of 91% by 8.49% when applied to the aforementioned dataset.
Automated Tension Belt Status Monitoring

Tension belts are used in transport vehicles to secure the load. To remain overall safety, some regulations need to be applied using the tension belts. Some of those are summarized in DIN EN 12195–2. One regulation is, that the damage of used tension belts needs to be in certain ranges. Especially, small cuts on the edges of the tension belts unavoidably occurring in real working environments may be tolerated to a certain degree. Furthermore, even if the belt is in good health condition, using it in the field in an inappropriate way also endangers the safe transport of the good. E.g., if the belt is getting loose during the ride the good is no longer safely stored. How will the driver know? Applying automatic condition control of the tension belt and all related parameters (e.g., tension force, health status of the tension belt) would help to more safely transport goods on vehicles. In this paper we propose some technical solutions towards a smart tension belt allowing safer transport of goods on the road.
Extended E-N-DIST Algorithm for Alias Detection

Nowadays personal names are not the only way to refer to celebrities and experts from different fields, instead, they can be referred to by their aliases on the web. Associated aliases have remarkable importance in retrieving information about the personal name from the websites. Therefore, disclosing aliases can have an important role in overcoming many real-world challenges. In this research, the aim is to explore and propose a reliable algorithm that can detect aliases that occurred due to transliteration of Arabic names into English. An extension to the Enhanced N-gram distance algorithm (E-N-DIST) which was previously published is introduced in this paper. The proposed algorithm is called the Extended Enhanced N-gram distance algorithm (E-E-N-DIST). The differences between E-N-DIST and E-E-N-DIST are two main changes in calculating the cost of substitution and transposition. First, E-E-N-DIST is computed based on 2n+1 – 1 states. The second is the use of an edit operation called the ’Exchange of Vowels’ to count the common spelling errors that happen due to the transliteration from one language to another. The idea of exchange of vowels is to search for vowels (viz. = a‘, = e‘, = i’, = o‘, and = u‘) and the non-vowel character = y‘ that has a vowel sound or a part of it in other languages to estimate the operations cost of insertion and deletion. The proposed algorithm tested using a dataset for the literature; the results obtained are compared with other algorithms from the state of the art. The proposed algorithm outperforms other algorithms; it achieved a better average percentage of similarity than all other compared algorithms.
Consistent Pricing or Inconsistent Pricing: A Tool to Deal with Dominant E-tailers

Manufacturer, as a dominated player, often sells products via competing dominant platforms (e.g., Amazon.com, Tmall.com, et al.). The investigation reveals that prices of same products on different platforms are sometimes the same and sometimes different. This phenomenon yields a novel research problem: Why these pricing strategies (consistent pricing and inconsistent pricing) exist? How should the weak manufacturer choose an appropriate one and protect his profits? We focus on it and model the problems by considering two important factors, i.e., competition between e-tailers and spillover effect from online channel to offline channel. By employing Stackelberg approach to our analysis where dominant e-tailers play as the leader and the manufacturer plays as the follower, we have two new findings: For the e-tailer, he always prefers inconsistent pricing regardless of spillover effect and platform competition. However, manufacturer prefers this strategy only when the spillover is negative. In case that the spillover is positive, manufacturer can use consistent pricing as a tool to mitigate competition between e-tailers, which improves not only online sales but also offline sales, and in turn increases manufacturer’s profits.
MADM-A New Multicomponent Aerosol Dynamics Model

A Multicomponent Aerosol Dynamics Model (MADM) capable of solving the condensation/evaporation equation of atmospheric aerosols is presented. Condensable species may be organic and/or inorganic. For the inorganic constituents the equilibrium model ISORROPIA is used to predict the physical state of the particle, i.e., whether the aerosol is liquid or solid. The mass transfer equations for the fluxes for solid atmospheric particles are developed. MADM is able to simulate aerosol deliquescence, crystallization, solid to solid phase transitions, and acidity transitions. Aerosols of different sizes can be in different physical states (solid, liquid, or partially solid and partially liquid). Novel constraints on the electroneutrality of the species flux between the gas and aerosol phases are presented for both liquid and solid aerosols. These constraints aid in the stability of the algorithm, yet still allow changes in aerosol acidity. As an example, MADM is used to predict the dynamic response of marine aerosol entering an urban area.
Indigenous Big Data Implications in New Zealand

Our world is dynamic and digital. It is irrefutable; big data are reshaping commerce, healthcare and governmental decisions. Individuals forgo awareness or even desire to uncover or appreciate how big data are affecting our society. This raises distinct data issues of huge effect, especially for indigenous communities. The novel proposition of this research, is that by considering advances in big data optimisation and issues from an indigenous perspective, a broad appreciation of decision-making significances will result. The microcosm in Aotearoa, New Zealand is relatively simple. It permits both a clear and leading view, especially of complex indigenous effects, due to its foundations in Māoridom. As big data advancements impact the quality of life for us all, there is an enormous responsibility, opportunity and incentive to focus research and awareness into nascent data design fields. Through an indigenous lens, unique perspectives and insights are shaped and presented in this paper, implicating feature selection in high-dimensional data.
Using a Novel Unbiased Dataset and Deep Learning Architectures to Predict Protein-Protein Interactions

Proteins are indispensable to the living organisms and are the backbone of almost all cellular processes. However, these macromolecules rarely act alone, forming the protein-protein interactions. Given their biological significance it should come as no surprise that their deregulation is one of the main causes to several disease states.The sudden surge of interest in this field of study motivated the development of innovative in silico methods. Despite the obvious advances in recent years, the effectiveness of these computational methods remains questionable. There is still not enough evidence to support the exclusive use of in silico techniques to predict protein-protein interactions not yet experimentally determined. It is proved that one of the primary reasons leading to this situation is the non-existence of a ”gold-standard” negative interactions dataset. Contrary to the high abundance of publicly available positive interactions, the negative examples are often artificially generated, culminating in biased samples.In this paper a new unbiased dataset that does not overly constraint the negative interactions distribution is presented. Beyond the novel dataset, also distinct deep learning models are proposed as a tool to predict whether two individual proteins are capable of interacting with each other, using exclusively the complete raw amino acid sequences. The obtained results firmly indicate that the proposed models are actually a valuable tool to predict protein-protein interactions, mainly when compared with the existing approaches, while also highlighting that there is still some room for improvement when implemented in unbiased datasets.
Thermal and Dielectric Properties of Thermally Aged Polypropylene/Calcium Carbonate Nanocomposites

This paper reports on the effects of aging on thermal, chemical, and dielectric properties of unfilled polypropylene and polypropylene nanocomposites containing 5 wt% of surface-modified calcium carbonate nanofiller. The evaluated samples were prepared using a melt blending technique. Then, a hydraulic laboratory press was used to obtain a thin film sample. Three different aging conditions were considered, namely, unaged (as reference), and aged at 110 °C and 140 °C. The evaluated samples were then characterized using differential scanning calorimetry and Fourier transform infrared spectroscopy for thermal and chemical analyses, respectively. Meanwhile, the dielectric strength was determined based on DC electrical breakdown tests. The experimental results show that aging the samples at 140 °C changed the thermal behavior of all evaluated samples. Specifically, the melting temperature increased when the sample was aged at 140 °C. Of note, the DC breakdown strength of unfilled polypropylene reduced significantly after aging at 140 °C when compared to polypropylene nanocomposites.