Miracle-dz/UniFAR-FTUs huggingface dataset:
ANN Based Adaptive Successive Cancellation List Decoder for Polar Codes

Combined with cyclic redundancy check (CRC), SC list (SCL) decoder can achieve outstanding error correction performance, which is more obvious with increasing list size. However, the corresponding decoding complexity and latency increase with the list size. To this end, the selection of list size becomes essential for practical applications. A new artificial neural network (ANN) based framework is proposed in this paper to design a hardware-friendly adaptive SCL (DL-ASCL) decoder. First, the list size at each stage is predicted by an ANN predictor. The performance achieved based on the proposed DL-ASCL algorithm is close to the optimal SCL decoder with the same list size, especially in the high signal-to-noise ratio (SNR) region. Meanwhile, the computational complexity is significantly reduced compared with the conventional ones. Numerical results have demonstrated that the proposed deep learning based adaptive SCL decoder can achieve 56% computational complexity reduction compared with the conventional SCL decoder for the polar code with length 128 and rate 1/2. The hardware architecture of the adaptive SCL decoder based on the predicted list size is proposed and the folding technique is also adopted, which helps reduce the hardware cost by about 25%.
Atom-thick transistors

IF THERE'S ONE THING ABOUT Moore's Law that's obvious to anyone, it's that transistors have been made smaller and smaller as the years went on. Scientists and engineers have taken that trend to an almost absurd limit during the past decade, creating devices that are made of one-atom-thick layers of material.
Reinforcement-Learning-Based Energy Storage System Operation Strategies to Manage Wind Power Forecast Uncertainty

Currently, renewable-energy-based power generation is rapidly developing to tackle climate change; however, the use of renewable energy is limited owing to the uncertainty related to renewable energy sources. In particular, energy storage systems (ESSs), which are critical for implementing wind power generation (WPG), entail a wide uncertainty range. Herein, a reinforcement leaning (RL)-based ESS operation strategy is investigated for managing the WPG forecast uncertainty. First, a WPG forecast uncertainty minimization problem is formulated with respect to the ESS operation, subject to ESS constraints, and then, the problem is presented as a Markov decision process (MDP) model, with the state-action space limited by the ESS characteristics. To achieve the optimal solution of the MDP model, an expected state–action–reward–state–action (SARSA) method, which is robust toward the dispersion of the system environment, is employed. Further, frequency-domain data screening based on the k-mean clustering method is implemented to improve learning performance by reducing the variance of the WPG forecast uncertainty. Extensive simulations are conducted based on practical WPG generation data and forecasting. Results indicate that the proposed clustered RL-based ESS operation strategy can manage the WPG forecast uncertainty more effectively than conventional Q-learning-based methods; additionally, the proposed method demonstrates a near-optimal performance within a 1%-point analysis gap to the optimal solution, which requires complete information, including future values.
Multi-User Offloading and Resource Allocation for Vehicular Multi-Access Edge Computing

By providing computation capability in the vicinity of vehicle terminals (VTs), multi-access edge computing (MEC) enables resource-demanding in-vehicle applications with significantly lower latency and less energy consumption. In this paper, we investigate the problem of offloading decision and resource allocation among multiple VTs to achieve the optimal system-wide user utility. Under the constraints of computation and wireless channel resources and VTs' mobility, this problem is mixed-integer non-linear programming (MINLP), which is generally NP-hard. A heuristic offloading decision method (HODM) is proposed, which decomposes the original problem into two subproblems, i.e., a convex computation allocation subproblem and a non-linear integer programming (NLIP) offloading decision subproblem, and settles them respectively. The convex subproblem is solved with a numerical method to obtain the optimal computation allocation among multiple offloading VTs, and a genetic algorithm (GA) based search algorithm is designed for the NLIP subproblem to determine the offloading decision. Several methods are utilized to reduce the enormous search space of this problem to make our solution more efficient. Extensive simulations are conducted by comparing with four baseline algorithms to demonstrate the superior performance of the proposed HODM.
Precision Aquaculture

Precision aquaculture is founded on a set of disparate, interconnected sensors deployed within the marine environment to monitor, analyze, interpret, and provide decision support for farm operations. Recent technological innovations facilitate aquaculture becoming part of the Internet of Things (IoT) -- modern farms are characterized by hundreds of interconnected sensors that store and serve data, interact with other sensors and devices, and connect with a fog and cloud ecosystem. We describe the implementation of the precision aquaculture concept to a number of farms in eastern Canada. The work combines partners from industry, technology, and academia to provide data-driven insight and decision that promotes ecologically sustainable intensification of aquaculture. The article presents a first case study on how IoT can instrument, inform, and impact the aquaculture industry. Challenges related to connectivity, interoperability, and standardization are discussed, and we elucidate how our experiences can inform future activities.
Unbiased Mortality Prediction for Unbalanced Data Using Machine Learning

Mortality defines ‘to be in the state of being prone to death’. The mortality rate is the measure of the frequency of occurrence of death in a defined population during a specified interval. The major causes of death in the recent decade is due to the Cardiovascular diseases, Cancers and Respiratory diseases. The problem is hosted on CodaLab as an ongoing competition. In this work, we have been provided with a data set that contains 342 features. The training data consists of 80,000 patient's records which are highly biased as 90 percent of the labels belong to the class ‘0’. Due to its skewed nature, various oversampling techniques were tried. Certain Machine learning techniques were then applied to detect the death chances of a person during their stay at the hospital based on their medical records. Accuracy of 76.68% percent was achieved on the testing data (20,000 records).
DeePEL: Deep learning architecture to recognize p-lncRNA and e-lncRNA promoters

Promoter regions of long non-coding RNA (lncRNA) genes are crucial to understand their transcriptional regulatory pattern. LncRNA genes, being more cryptic than protein-coding genes in terms of their functionality and biogenesis divergence, are lacking in number of existing studies to elucidate the roles of their promoters compared to their counterparts. Based on the overlap between epigenetic marks and transcription start sites, human lncRNAs were categorized into two broad categories: enhancer-originated lncRNAs (e-lncRNAs) and promoter-originated lncRNAs (p-lncRNAs) and hence these two groups are subject to distinct transcriptional regulatory programs. To understand the difference in the transcriptional regulatory mechanisms that governs p- and e-lncRNAs, we studied the promoter sequences of these two groups of lncRNAs including distinct transcription factor (TF) proteins that favor p-over e-lncRNA (and vice versa). In addition, we developed a convolution neural network (CNN) based deep learning (DL) framework DeePEL (deep p-, e-lncRNA promoter recognizer), to classify the promoter of p- and e-lncRNAs. To the best of our knowledge, this is the first attempt to classify these two groups of lncRNA promoters, using sequence and TF information, based on DL framework. We report several sequence specific signatures in the promoter regions as well as several distinct TFs specific to groups of lncRNAs that will help in understanding the promoter-proximal transcriptional regulation of p-lncRNAs and e-lncRNAs.
Towards a Gamified Support Tool for Requirements Gathering in Bahasa Indonesia

In this modern world, software products have significant impacts on every aspect of peoples' life. Requirements engineering is one of the most critical activities in software development in which the foundation of the product is laid. In order to provide structure in requirements, the user stories concept is often used. The user story concept emphasizes information in three central parts, the stakeholder's role, the requirement, and the business value which will be satisfied by the requirement. Stakeholders' participation is crucial in gathering, eliciting, and analyzing requirements; however, frequently due to circumstances, stakeholders provide minimum information related to the software requirements. In order to increase user's participation in providing stories related to the software requirements, our research aims to develop a gamified web-based support tool for the stakeholders and engineers to use in gathering requirements while implementing the user story concept. The gamified support tool is then evaluated to assess the system adoption regarding its use to support users in providing requirements regarding a specific software product. Results show that the support tool receives scores as follow, 81.1% for Joy, 73.4% for Control, 67.2% for Focused Immersion, 60.6% for Temporal Dissociation, 80% for Curiosity, 76.8% for Perceived Ease of Use, 72.9% for Perceived Usefulness, and 76.2% for Behavioral Intention to Use. Based on the interview with the users, the support tool shows excellent potential to be further developed to support the requirements engineering activity in software development.
Handling Irregularly Sampled Longitudinal Data and Prognostic Modeling of Diabetes Using Machine Learning Technique

Clinical researchers use prognostic modeling techniques to identify a-prior patient health status and characterize progression patterns. It is highly desirable to predict future health condition especially to implement preventive and intervention strategies in pre-diabetic individuals. Hidden Markov Model (HMM) and its variants are a class of models that provide predictions concerning future condition by exploiting sequences of clinical measurements obtained from a longitudinal sample of patients. Despite the advantages of using these models for prognostic modeling, it still face barriers and significant challenges, to effectively learn dynamic interactions, when using irregularly sampled longitudinal Electronic Medical Records (EMRs) data. Newton’s divide difference method (NDDM) is a classical approach for handling irregular data in terms of divided difference. However, as it is polynomial approximation technique, it suffers with Runge Phenomenon. The problem can be even more severe when the interval is a bit extended. Therefore, to tackle this problem, we proposed a novel approximation method based on NDDM as a component with HMM in order to estimate the 8 years risk of developing Type 2 Diabetes Mellitus (T2DM) in a particular individual. The proposed method is evaluated on real world clinical data obtained from CPCSSN. The results demonstrated that our proposed technique has the ability to exploit the available irregularly sampled EMRs data for effective approximation and improved prediction accuracy.
An Optimum Clustered Grid-Based Particle Swarm Optimization to Enhance Efficiency Energy in Wireless Sensor Networks

Wireless sensor nodes are small but heavy-duty devices. This small device is tasked with monitoring and measuring environmental conditions with limited resources. The main problem is the limited battery capacity. While changing batteries or recharging is not possible routinely. Clustering is a technique that can be utilized to overcome problems in wireless sensor networks effectively. However, to set the optimal amount of clusters and to select the optimal cluster heads (CH) are non-deterministic polynomial-time hard (NP-hard) problem. Optimum Clustered Grid-Based (OCGB) protocol proposed in order to enhance energy efficiency and to protract the working time of sensor nodes. At the beginning operating time, OCGB splits network area into grids. At the next step, OCGB set the optimal amount of clusters in every grid. Furthermore, the OCGB utilizes the Particle swarm optimization (PSO) for forming clusters and selecting the optimal CHs. Evaluation is performed with MATLAB and compared with other protocols, namely LEACH. Simulation results present that our proposed protocol OCGB has better performance in terms of energy efficiency and network lifetime compared to LEACH protocol.
Multistage Cooperative Trajectory Planning for Multimissile Formation via Bi-Level Sequential Convex Programming

Owing to the highly nonlinear dynamics and high number of nonlinear constraints, it is exceedingly difficult and computationally expensive to solve the cooperative trajectory planning problem of multimissile formation using existing approaches. To address this issue and improve the convergence property and computational efficiency, a bi-level sequential convex programming (SCP) method consisting of a system coordination level and an individual optimization level is proposed to solve the cooperative trajectory planning of missile formation. At the system level, the time consensus constraints are determined, the cooperative constraints that should be considered in the next iteration of the individual trajectory optimization level are identified, and the members that have converged are removed from the optimization sequence. As the number of members in the optimization sequence and the number of cooperative constraints considered in the individual SCP are clearly decreased, the convergence property and the computational efficiency of cooperative trajectory planning are evidently improved. At the individual level, the proposed method creatively proposes the innovative idea: based on the updated information of the system level, each member solves its individual trajectory optimization sub-problem independently and sequentially by gradually adding and tightening the cooperative constraints with the evolution of optimization iteration of SCP, which can further enhance the convergence property. Numerical simulations show that the proposed bi-level SCP method can effectively solve the multistage cooperative trajectory planning of multimissile formation with good convergence property, exhibiting the excellent scalability to the number of members and higher effectiveness. The comparison with the generation optimal control software (GPOPS) method further demonstrates the high efficiency of the proposed method.
Photovoltaic Power Generation with Module-Based Capacitive Energy Storage

Module-based electrochemical energy storage can be used to reduce the ramp rate of PV generation with fluctuating insolation. As the capacitance of the module-based capacitive energy storage decreases, large fluctuations on the DC link voltage are expected caused by the variation in the PV power. It is important to design and implement effective control methods to reduce this undesired effect on the DC link voltage, especially with the use of lower capacitance (1 F). A comparison of feedforward compensation, set-point weighting and anti-windup control methods based on a proportional integral (PI) controller for DC link voltage is reported. Our case studies verify the effectiveness of a PI controller combined with feedforward compensation and anti-windup scheme, and demonstrate the improvements in the DC link voltage dynamics of a single-phase grid connected system.
Single Phase PV-Fed Current Source Inverter with Sinusoidal Grid Current Injection Control

A single phase Grid Connected Current Source Converter (GCCSC) for photovoltaic (PV) systems with sinusoidal grid current injection control is presented in this paper. The Voltage Source Converter (VSC) topologies are extensively used for connecting Renewable Energy Sources (RES) to the utility grid. The grid current injection control structure is quite tough or dependent on outerloop in case of VSC based PV compared with current source converter (CSC) based PV. The presented control structure and overall system achieves following functionalities (i) Always PV panel is operating at Maximum Power Point (MPP) mode by using Perturbation and Observation (P&O) algorithm (ii) Ripple current in the DC link is very less (iii) Unity Power Factor (UPF) (iv) Very less Total Harmonic Distortion (THD) of grid currents. To verify the effectiveness of control structure and overall system, simulation studies were done in MATLAB/SIMULINK environment.
Exploring in-Service Teachers' Acceptance of Augmented Reality

Augmented reality (AR) is an emerging technology that has been shown to have positive impact on the cognitive and affective learning outcomes for students of all grades. However, the use of AR has been limited to self-study space in India and not percolated into school classrooms. A precondition for this to happen is teacher acceptance of AR as a teaching resource. In this paper, we report a pilot study done with six secondary school teachers to test their acceptance of AR through semi-structured interviews. These interviews explored teachers' AR acceptance, based on the three core constructs of the Technology Acceptance Model (TAM2) – perceived usefulness, perceived ease of use and behavioral intention to use. Thematic analysis of interviews show teachers have positive acceptance for AR. The analysis also throws light on two additional contextual factors that govern teachers' intention to use and the type of teacher support needed for effective integration of AR in school education in India. The findings of this paper will be of interest to AR researchers and learning solutions companies.
Contact Resistivity and Sheet Resistance Measurements of Cells Extracted from Field-aged Modules

The electrical performance of a photovoltaic (PV) module is greatly hindered by the existence of parasitic resistance losses, such as high series resistance (Rs) and low shunt resistance (Rsh). Contact resistance at metal grid/semiconductor interface and emitter sheet resistance are two major contributors to cell Rs. Transmission Line Measurement (TLM) is a powerful method to estimate these resistance components. This paper presents the application of the TLM method to the cell strips extracted from field-aged PV modules at two different climates (Arizona and Florida) of the same design to investigate the influence of encapsulant material and microcracks on the contact resistivity and sheet resistance of the solar cell. The cells were initially extracted from the module laminate by dissolving the encapsulant polymer in trichloroethylene (TCE) solvent at 80oC for around 3 hours. The recovered cell was then used to cut into rectangular strips of 1.5 cm wide. The effective contact resistivity and emitter sheet resistance between two fingers in each strip were measured and compared to quantify the contact degradation induced from longer field aging. The Arizona module suffered from higher resistance as compared to the Florida module due probably to longer field exposure and higher operating temperatures. This method serves as a good diagnostic tool to anticipate and understand the severity of the contact degradation of solar cells in the fielded modules.
Design of convolution neural network for facial emotion recognition

Facial emotion recognition has too much demand in different areas, such as computing, psychology, study of human emotion, medical diagnosis, etc. The convolutional neural networks (CNN) a sub-machine learning and artificial intelligence sub-sector in the last years have exerted a great work in the methods of facial recognition by computer, The approach and purpose of this research is to buy the existing methods to attack the problem and develop an algorithm for the recognition of facial expression by computer implementing CNN, in order to evaluate the service of customers and consumers in different services or products.
Understanding E-health Application Utilizing Internet of Things (IoT) Technologies

E-Health applications make room for exchange of information between medical professionals and patients, hospitals to hospitals, lower cost, increased scope and extent of medical facilities, as well as improving the quality of services. Despite all these advantages, healthcare system is still being challenged with the factors such as increased number of ageing population, need for reduced cost of health services, the shifting of healthcare services from provider-centric to patient-centric, the type of new diseases available and their treatments. All these require Internet of Things (IoT) for its transformation. This work seek to explore Internet of Things in e-Health domain and determine how its operations can transform healthcare application system. We examined technological and application trends in healthcare, Convergent e-Health-IoT Works, attacks and issues in e-Health utilizing Internet of Things technologies, applications of Internet of Things (IoT) in e-Health, as well as challenges and open problems in the e-Health domain.
Development of the Online Collaborative Summarizing Feature on Student-Centered E-Learning Environment

Universitas Indonesia has implemented a Learning Management System (LMS) known as Student-Centered E-Learning Environment (SCeLE). The SCeLE facilitates collaborative learning through an online discussion forum. Collaborative learning can promote critical thinking. However, students will have difficulties contributing and understanding discussions when the discussion process is fast, lengthy and when diverse ideas arise. This could lead to uncertainty and demotivation among the students. On the other hand, teachers have difficulties in monitoring students’ understanding and the discussion progress. Consequently, providing constructive feedback promptly becomes challenging. This study proposed an online collaborative summarizing feature to be integrated with the online discussion forum to facilitate the construction of a discussion summary. The tool was developed using the User-Centered Design (UCD) approach and tested using the usability evaluation. The participants (both students and lecturers) found that the usability of the developed system was good. This was indicated by the fact that more than 80% of the students and more than 80% of the lecturers were able to finish the tasks given easily. Furthermore, the SUS score exceeded the average score, meaning that students and lecturers found that the developed system had a good usability.
Regularized Adversarial Training (RAT) for Robust Cellular Electron Cryo Tomograms Classification

Cellular Electron Cryo Tomography (CECT) 3D imaging has permitted biomedical community to study macromolecule structures inside single cells with deep learning approaches. Many deep learning-based methods have since been developed to classify macromolecule structures from tomograms with high accuracy. However, several recent studies have demonstrated the lack of robustness in these models against often-imperceptible, designed changes of input. Therefore, making existing subtomogram-classification models robust remains a serious challenge. In this paper, we study the robustness of the state-of-the-art subtomogram classifier on CECT images and propose a method called Regularized Adversarial Training (RAT) to defend the classifier against a wide range of designed threats. Our results show that RAT improves robustness for CECT image classification over the previous methods.