Miracle-dz/UniFAR-FTUs huggingface dataset:
Demand-Side Regulation Provision From Industrial Loads Integrated With Solar PV Panels and Energy Storage System for Ancillary Services

Nowadays, enabled by current smart grid technology, electricity consumers can play an active role in providing ancillary service (AS) as a type of demand response. Participating AS can assist stabilizing the power grid by following the frequency regulation signal, or dynamic regulation signal (RegD) in this study while receiving economic benefits. Industrial loads are an indispensable component as a demand-side regulating resource of ancillary service due to their intensive electricity consumption. In this paper, we use grid-connected solar photovoltaics panels combined with the energy storage system (ESS) to produce continuous electricity consumption signals in order to follow the RegD signal. The participation of solar energy in real-time regulation provision process is emphasized, which is modeled based on a variety of operation modes in accordance to Australian Standard. Through a particular case study, with the integration of solar energy, the proposed method poses cost-effectiveness in industrial plant scheduling and a favorable load following capability, helping ensure the frequency stability of the electric grid. The proposed methodology is more economically advantageous compared to identical industrial loads only equipped with on-site ESS, and requires less switchings on machines compared to industrial plants with passive use of solar energy.
Energy Efficiency Modeling of Parallel Applications

Energy efficiency has become increasingly important in high performance computing (HPC), as power constraints and costs escalate. Workload and system characteristics form a complex optimization search space in which optimal settings for energy efficiency and performance often diverge. Thus, we must identify trade-off options for performance and energy efficiency to find the desired balance between them. We present an innovative statistical model that accurately predicts the Pareto optimal performance and energy efficiency trade-off options using only user-controllable parameters. Our approach can also tolerate both measurement and model errors. We study model training and validation using several HPC kernels, then explore the feasibility of applying the model to more complex workloads, including AMG and LAMMPS. We can calibrate an accurate model from as few as 12 runs, with prediction error of less than 10%. Our results identify trade-off options allowing up to 40% improvement in energy efficiency at the cost of under 20% performance loss. For AMG, we reduce the required sample measurement time from 13 hours to 74 minutes (about 90%).
COPD Management by Symptom and Activity Tracking

In this paper, we introduce a home-based COPD management system to objectively track the disease progress during the daily-living of patients. The selection of the devices is performed to map the COPD assessment test questions and the patients lung function at home. A CAir Desk was developed to improve the usability of the many connected devices. We explain the IT architecture, data flow, as well as the data aggregation and visualization concept of our solution. Details on the usage during the trial is provided as well.
An Efficient Filtered Classifier for Classification of Unseen Test Data in Text Documents

Rapid development of information technology has increased the availability of Electronic documents and the task of automatic classification of e-documents play an important role for organizing the information in large data repositories. In addition, many researchers proposed various algorithms for classification, but these approaches need to filter the data before classification. Keeping these limitations, we address the problem of classifier for classification of unseen data in text documents where document data distribution is not homogeneous. In this study, we used a Filtered Classifier on text documents that has passed through an arbitrary filter. To classify the text documents C4.5 classifier was used, the structure of the filter is based on training and testing data sets that are processed by the filter without changing the structural behavior. In addition, Fayyad and Irani's discretization method is used as a preprocessing that discretize a range of numerical attributes in the text document data set into nominal attributes. For classification, we use C4.5 decision tree classifier. Four datasets such as CNAE-9, 20 Newsgroups, Twitter and Reuter-21578 were employed to test the unseen test documents and test the efficiency of the Filtered Classifier. Experimentation is described in detail and the results show improved classifier accuracy for classification.
Embedded Stethoscope for Real Time Diagnosis of Cardiovascular Diseases

This paper focuses on the initial assessment of cardiac abnormalities with heart murmurs. At times monitoring the patient Heart auscultations and murmur using a conventional stethoscope may lead to false diagnosis since they are low frequency and amplitude signal, as this procedure requires highly skilled expertise. This paper provides a solution by detecting the abnormality of heart sound earlier and easier by medical expertise. In this paper, an embedded acoustic stethoscope has been designed and implemented to provide a platform for the detection of cardiac disorders using computer aided diagnosis (CAD) of cardiac sound. This handheld device has the capability to diagnose heart beats in real-time which employs signal conversion of analog acoustic signals into digital signal and at the same time can be displayed on a PC using MATLAB GUI for visual elucidation thereby enabling critical analysis of the interpreted data. In addition, a pulse rate monitoring circuit is also incorporated to ensure proper blood circulation.
Tokenization and N-Gram for Indexing Indonesian Translation of the Quran

Tokenization is an important process used to break the text into parts of a word. N-gram model now is widely used in computational linguistics for predicting the next item in such a contiguous sequence of $\mathbf{n}$ items from a particular sample of text. This paper focuses on the implementation of tokenization and n-gram model using RapidMiner to produce unigram and bigram word for indexing Indonesian Translation of the Quran (ITQ). This study uses ITQ data sets consisting of 114 documents. The methods are data extracting and preprocessing text including tokenization, stemming, stopword removal, transformation cases, and n-grams. The results of this study showed the model produces the 6794 and 60323 tokens combination unigram and bigram use for index ITQ. Significant the contribution of this study is to enhance the digital index of ITQ.
Programmer Productivity Analyzer Tool

Software companies seek for productive programmer who writes codes that are reusable, maintainable, efficient and documented with coding standards. But companies face challenges in finding and monitoring such programmers due to lack of programmer productivity measuring tools. The available tools are limited to measure parameters like coding standards, bugs and some rule violation. Hence there is need for tools that can measure bad programming constructs, reusability and specific demands of companies in coding to judge a programmer. This paper proposes a novel method and tool to measure programmer productivity based on parameters such as efficient usage of memory and machine cycles. It also assist programmer to improve their productivity. The tool is able to detect 3–4 types and 1–2 types of bad programming constructs in code written by novices and experienced programmers respectively. In future, the tool shall be designed to measure the productivity using other parameters reusability and maintainable.
Color Image Steganography Using Random Key Matrix

The proposed steganographic technique is for the space domain steganography. The pixel value of cover image is changed to odd or even value according to the incoming bit. The proposed algorithm can store one data bit in one pixel of the cover image. Here, along with the data hiding, security is provided by the proposed Random Key Matrix (RKM). Different pixel values of the steganographic image are re-arranged according to the different elements of the secret Random Key Matrix (RKM). The proposed data embedding algorithm ensures a high visual steganographic image with added layers of security. Results show that with the proposed algorithm high Peak Signal to Noise Ratio (PSNR) value is obtained with less mean square error value.
Computation of 2D 8x8 DCT Based on the Loeffler Factorization Using Algebraic Integer Encoding

—This paper proposes a computational method for 2D 8 × 8 DCT based on algebraic integers. The proposed algorithm is based on the Loefﬂer 1D DCT algorithm, and is shown to operate with exact computation—i.e., error-free arithmetic—up to the ﬁnal reconstruction step (FRS). The proposed algebraic integer architecture maintains error-free computations until an entire block of DCT coefﬁcients having size 8 × 8 is computed, unlike algorithms in the literature which claim to be error-free but in fact introduce arithmetic errors between the column-and row-wise 1D DCT stages in a 2D DCT operation. Fast algorithms are proposed for the ﬁnal reconstruction step employing two approaches, namely, the expansion factor and dyadic approximation. A digital architecture is also proposed for a particular FRS algorithm, and is implemented on an FPGA platform for on-chip veriﬁcation. The FPGA implementation operates at 360 MHz, and is capable of a real-time throughput of 3 . 6 · 10 8 2D DCTs of size 8 × 8 every second, with corresponding pixel rate of 2 . 3 · 10 10 pixels per second. The digital architecture is synthesized using 180 nm CMOS standard cells and shows a chip area of 7.41 mm 2 . The CMOS design is predicted to operate at 893 MHz clock frequency, at a dynamic power consumption 13.22 mW/MHz · V 2 sup .
Empirical Investigation of Trends in NoSQL-Based Big-Data Solutions in the Last Decade

The usage and popularity of NoSQL databases have sharply risen over the past decade due to their ability to handle a huge amount of data by employing scalable architecture, high availability and better performance than traditional relational database systems (RDBMS). In addition to reporting dynamics in NoSQL-database world, this paper focuses on presenting results from the perspective of developers. Stack Overflow provides a comprehensive technical niche with about 15 million technical questions, 8.1 million users and 25 million answers. In this paper, we aim to study variation in yearly trends of 20 NoSQL databases. To reveal the interest of the programmers we have investigated questions-asked and presented an unbiased Normal Interest Score by employing three parameters, first, the number of questions asked, second, mean views on a question and third, the mean score on a question. MongoDB, Cassandra, Redis, and Neo4j emerged as most popular databases in their respective families while NIS of all four of them is decreasing 2015 onwards. Additionally, we have also discussed how real-world events like publications, open-sourcing, mention in critical bills, version-release, acquiring ventures etc affect the interest corresponding to NoSQL databases over Stack Overflow. Results of this work will help database developers, database administrators for database selection, upgradation and maintenance.
Self-Adaptive Woman Health Monitoring System Using MAPE Components

In the model-driven software development, various models are generated during development process. In traditional software engineering, due to stable requirements development models were fixed during complete development. But some of these models are changed at runtime if requirements specifications are changed for modern software systems. To manage the changes at runtime, runtime models are developed and used. The developed software will be self-adaptive with self-optimizing and self-healing capabilities. Based on feedback loops, uncertainty is identified and MAPE components are used to implement self-adaptive plan. In this paper, the approach of self-adaptivity is applied to woman health monitoring system to make monitoring system responsive to new scenarios and autonomic. The system is useful for any age woman to monitor health data and make it available to physician for treatment. The state chart models are generated to describe how self-adaptation is analyzed, planned and executed when requirements specifications are changed.
Pattern Reconfigurable Antenna Using A Functional Material for MMW Application

This paper introduces a novel reconfigurable antenna for controlling radiation beam direction by using integrated phase-change material (PCM). A PCM GeTe-based parasitic patch considered as a switchable element between insulator and conductive states is applied to operate with a driven patch radiator. The direction of the radiation pattern of the antenna can be tilted by changing the state of the GeTebased patch from its insulator to conductive state. Obtained resultshows that the proposed technique is effective to control the beam direction of the antenna. Around a 40° titled angle is achieved by the insulator-to-conductor activation of the GeTe patch which can be realized by an optical source, making the antenna simple and compact without extra bias circuit. Hence, the antenna is suitable for millimeter-wave applications.
A $2 \times 2$ Inset Feed Circular Patch Antenna Array for 1.8GHz LTE Application

This paper presents the design and characterization of $2\times 2$ circular patch antenna array for 1.8GHz Long Term Evolution (LTE) application. The antenna array consists of 4 circular patches designed on a 1.6mm thick FR4 epoxy dielectric substrate with relative permittivity $(\epsilon_{r})$ of 4.2. An inset feed method is applied to feed each circular patch to improve overall performance of the antenna array. The physical parameter of proposed antenna array is analyzed through simulation to obtain the optimum performance satisfying the requirement of application. The experimental characterization is conducted for the realized antenna array to be compared with the simulation. It shows that realized antenna array resonates at the frequency of 1.8GHz with the gain of 6.36dBi in which this is agreed well with the simulation result.
Spatial Blockchain-Based Secure Mass Screening Framework for Children With Dyslexia

In this paper, we present a novel method, process, and system for calculating dyslexic symptoms, generating metric data for an individual user, community, or group in general. We present a mobile multimedia Internet of Things (IoT)-based environment that can capture multimodal smartphone or tab-based user interaction data during dyslexia testing and share it via a mobile edge network, which employs auto-grading algorithms to find dyslexia symptoms. In addition to algorithm-based auto-grading, the captured mobile multimedia payload is stored in a decentralized repository that can be shared with a medical practitioner for replay and further manual analysis purposes. Since the framework is language-independent and based on Blockchain and a decentralized big data repository, dyslexic patterns and a massive amount of captured multimedia IoT test data can be shared for further clinical research, statistical analysis, and quality assurance. Notwithstanding, our proposed Blockchain and off-chain-based decentralized and secure dyslexia data storage, management, and sharing framework will allow security, anonymity, and multimodal visualization of the captured test data for mobile users. This paper presents the detailed design, implementation, and test results, which demonstrate the strong potential for wider adoption of the dyslexia mobile health management globally.
StationPlot: A New Non-stationarity Quantification Tool for Detection of Epileptic Seizures

A novel non-stationarity visualization tool known as StationPlot is developed for deciphering the chaotic behavior of a dynamical time series. A family of analytic measures enumerating geometrical aspects of the non-stationarity & degree of variability is formulated by convex hull geometry (CHG) on StationPlot. In the Euclidean space, both trend-stationary (TS) & difference-stationary (DS) perturbations are comprehended by the asymmetric structure of StationPlot’s region of interest (ROI). The proposed method is experimentally validated using EEG signals, where it comprehend the relative temporal evolution of neural dynamics & its non-stationary morphology, thereby exemplifying its diagnostic competence for seizure activity (SA) detection. Experimental results & analysis-of-Variance (ANOVA) on the extracted CHG features demonstrates better classification performances as compared to the existing shallow feature based state-of-the-art & validates its efficacy as geometry-rich discriminative descriptors for signal processing applications.
Software Conceptual Integrity: Deconstruction, Then Reconstruction

Conceptual Integrity is the most important consideration for software system design, as stated by Frederick Brooks. Brooks also suggested that Conceptual Integrity can be attained by means of design principles, such as Propriety, and Orthogonality. However, Brooks' principles have not been formalized, posing obstacles to their application in practice, and to a deeper comprehension of Conceptual Integrity. This paper has three goals: first, to achieve deeper comprehension of Conceptual Integrity by deconstructing it into two phases, viz. Conceptualization and Modularization, iteratively applied during software system design; second, to show that the algebraic Linear Software Models already provide the hitherto lacking formalization of Brooks' design principles, which surprisingly belong mainly to the Modularization phase; third, to reconstruct Conceptualization and Modularization, preserving the desirable tension between: a- phases separation, each with its own specific formal manipulation techniques; b- precise transition between these phases, consisting of explicit mutual relationships. The tension stems from the Modularity Matrix linking two very different kinds of entities - system concepts and abstract mathematical constructs - as seldom linked before. The paper motivates the two software design phases, illustrating Conceptualization with examples, and characterizing Modularization by its more mature mathematical theory.
Intracoronary tenecteplase versus abciximab as adjunctive treatment during primary percutaneous coronary intervention in patients with anterior myocardial infarction.

AIMS We sought to compare the effects of intracoronary administration of a fibrinolytic drug (tenecteplase) to those of a glycoprotein IIb/IIIa inhibitor (abciximab) in patients with ST-elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PPCI). METHODS AND RESULTS In this pilot trial, 76 patients (59 male) with anterior STEMI were randomised to intracoronary infusion of reduced-dose tenecteplase or abciximab during PPCI. Angiography was repeated at 48 hours to assess corrected TIMI frame count (cTFC) and TIMI myocardial perfusion grade (TMPG). The primary endpoint was infarct size as assessed by cardiac MRI. The abciximab group showed lower cTFC (median 14.1 [IQR 9.4-17.1]) than the tenecteplase group (18.2 [10.0-28.2]) (p=0.02), and the proportion of patients with TMPG grade 2/3 was higher in the abciximab group (90.3% vs. 67.7%; p=0.03). Major cardiac and cerebrovascular event rates did not differ; however, notably, 2/38 patients in the tenecteplase group experienced subacute stent thrombosis. At four months, there were no significant differences in infarct size between the tenecteplase and abciximab groups (17.0 g [9.6-27.5] vs. 21.1 g [11.3-35.0], p=0.33). CONCLUSIONS Intracoronary administration of tenecteplase did not reduce infarct size compared to abciximab in STEMI patients undergoing PPCI. Tenecteplase exhibited poorer myocardial reperfusion and might be associated with increased subacute stent thrombosis.
Pattern Based Temporal Inference In Forward Search Temporal Planning

Temporal Planners are developed to address problem domains of a more realistic nature, where time is a factor. Some of the most difficult problems to solve are those containing required concurrency since the planner must execute actions in parallel. Forward search planners are generally good at finding plans using search with heuristic guidance. However, temporal planners still face limitations in their use of inference. This paper discusses patterns of required concurrency and how we can solve problems of this nature, with reduced search and more inference, in a forward search framework.
Weighted Patches Based Face Super-Resolution Via Adaboost

To alleviate the blurring effect of super-resolved faces in super-resolution (SR) field, a number of sparse representation methods have been proposed recently. However, current researches normally treat all facial patches equally and do not consider the fact that different facial patches may have unequal contributions to the SR. Regarding that AdaBoost, a classical ensemble method, has a natural weighted update scheme, this paper aims to develop a weighted-patch super-resolution approach based on the framework of AdaBoost. In the training phase, each facial patch is weighted automatically according to the difference between reconstructed patch and original patch in current iteration, which can assign more weights to the worse performed patches with lower reconstruction power in next iteration. The experimental results on two benchmarks demonstrate the effectiveness of the proposed approach.
Generative Dual Adversarial Network for Generalized Zero-Shot Learning

This paper studies the problem of generalized zero-shot learning which requires the model to train on image-label pairs from some seen classes and test on the task of classifying new images from both seen and unseen classes. In this paper, we propose a novel model that provides a unified framework for three different approaches: visual->semantic mapping, semantic->visual mapping, and metric learning. Specifically, our proposed model consists of a feature generator that can generate various visual features given class embeddings as input, a regressor that maps each visual feature back to its corresponding class embedding, and a discriminator that learns to evaluate the closeness of an image feature and a class embedding. All three components are trained under the combination of cyclic consistency loss and dual adversarial loss. Experimental results show that our model not only preserves higher accuracy in classifying images from seen classes, but also performs better than existing state-of-the-art models in in classifying images from unseen classes.
Performance of Assigning Pilot Sequences in Cell Free Massive MIMO under SINR Constraints

In this paper, we consider the uplink (UL) of Cell-Free (CF) Massive MIMO system where a large number of service antennas act as access points (APs), serving distributed users. APs have multiple antennas, while each user has only one antenna. The target is to minimize users’ power consumption under imperfect channel state information (CSI) by optimally allocating the UL transmit power to each user, while maintaining the Quality of Service (QoS) for every user in the network. We investigate also the effect of assigning pilot sequences to the SE performance, when the system analysis is performed under maximum ratio combining. The results are derived by an offline training phase using Rayleigh fading channels. It has been indicated that joint pilot assignment and power allocation have a positive impact on the maximum network QoS value as well as overall power consumption of users in the system.