Miracle-dz/UniFAR-FTUs huggingface dataset:
Short-term forecasting of regional biospheric CO2 fluxes in Europe using a light-use-efficiency model

Abstract. Forecasting atmospheric CO2 concentrations on synoptic time scales (~ days) can benefit the planning of field campaigns by better predicting the location of important gradients. One aspect of this, accurately predicting the day-to-day variation in biospheric fluxes poses a major challenge. This research aims to investigate the feasibility of using a diagnostic light-use-efficiency model, the Vegetation Photosynthesis Respiration Model (VPRM), to forecast biospheric CO2 fluxes on the time scale of a few days. As input the VPRM model requires downward shortwave radiation, 2 m temperature, and Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI), both of which are calculated from MODIS reflectance measurements. Flux forecasts were performed by extrapolating the model input into the future, i.e. using downward shortwave radiation and temperature from a numerical weather prediction (NWP) model, as well as extrapolating the MODIS indices to calculate future biospheric CO2 fluxes with VPRM. A hindcast for biospheric CO2 fluxes in Europe in 2014 has been done and compared to eddy covariance flux measurements to assess the uncertainty from different aspects of the forecasting system. In total the range-normalized mean absolute error (normalized) of the 5 day flux forecast at daily timescales is 7.1 %, while the error for the model itself is 15.9 %. The largest forecast error source comes from the meteorological data, which fail to accurately predict cloud cover, leading to overestimated shortwave radiation in the model. The error contribution from all error sources is similar at each flux observation site, and is not significantly dependent on vegetation type.
SURVEY ON BLOCKCHAIN: A COMPLETE TECHNOLOGIES COMPARISON

Blockchain is actually a decentralized database without any third party -controller- and could be the ground necessary to guarantee that any committed transaction is strong enough to meet basic expectations such as security and privacy requirements. This is where the blockchain technology comes handy. It is fundamentally a public distributed ledger of all transactions, has the ability to increase secure data exchange and to make that data transfer simpler and easier between entities. Therefore, the need of blockchain would be extensive in the near future. In this paper, we firstly provide an overview of blockchain with emphasis on most famous blokchain technologies. Secondly, we will focus on evaluating them related to fundamental security requirements and different criteria. At the end, we conclude our comparison.
Development of Smart Stirred Machine for Vinegar Solid-State Fermentation Using Embedded System and Cloud Computing Server

Through the analysis of the functional requirements and working schedule of the grains-stirring machine, an intelligent system has been developed for solid-state fermentation in this paper. Firstly, the embedded system for stirred machine is designed, which includes power-supply module, motor-derived circuit, proximity sensor, temperature sensor, 2.4G wireless and GPRS module etc. Then, a cloud computing server platform is presented to monitor the working process and the quality of fermentation. The communication between the cloud computing server and stirred machine is GPRS. The experimental result demonstrates the performance of the developed system. Furthermore, the system increased the automatic cleaning and crushed-material design, avoids caking and over-fermentation, and improves the efficiency and quality of stirred vinegar cultural.
Towards a Deadline-Based Simulation Experimentation Framework Using Micro-Services Auto-Scaling Approach

There is growing number of research efforts in developing auto-scaling algorithms and tools for cloud resources. Traditional performance metrics such as CPU, memory and bandwidth usage for scaling up or down resources are not sufficient for all applications. For example, modeling and simulation experimentation is usually expected to yield results within a specific timeframe. In order to achieve this, often the quality of experiments is compromised either by restricting the parameter space to be explored or by limiting the number of replications required to give statistical confidence. In this paper, we present early stages of a deadline-based simulation experimentation framework using a micro-services auto-scaling approach. A case study of an agent-based simulation of a population physical activity behavior is used to demonstrate our framework.
SPAGHETTI VS. MODULAR CODE - THE CHOICE OF NOVICE PROGRAMMERS

Code modularity is one of the criteria for testing software quality. Nevertheless, novice programmers often tend to avoid writing modular code. In this study, we aim to explore the circumstances in which novice programmers choose to write modular code. To address this aim, two student groups, twenty in each, were given a programming task, each in different configuration. Group-1 was given the task in several phases, each add complexity to the previous one, while Group-2 was given the entire task at once. The entire task was rather complex and its modular solution requires the definition of few classes and methods, each responsible to one of the problem's aspects. The students' solutions were analyzed using the dual-process theory, cognitive dissonance theory and content analysis methods to explore the extent of code modularity. The analysis revealed the following: (1) as regards to Group-1, a minor increase in the number of modular solutions was found while they progressed along the phases; (2) The number of modular solutions of Group-2 was higher than of Group-1. Analysis of students' justifications for lack of code modularity in Group-1 revealed the following. The first phase of the problem was perceived as rather simple hence many students did not find any reason to invest in designing a modular solution. When the task got complex in the following phases, some students realized that a modular solution would fit better, hence a cognitive dissonance was raised. Nevertheless, many of them preferred to continue their course of non-modular solution instead of re-designing a modular new one. In order to decrease the dissonance they claimed that this decision saved time. Students of both groups also attributed their non-modular code to lack of explicit criteria for the evaluation of the code quality that lead them to focus on functionality alone.
Development of a Mobile Phone App for Measuring Striking Response Time in Combat Sports: Cross-Sectional Validation Study

Background TReaction is a mobile app developed to determine strike response time at low cost and with easy application in combat sports. However, the validity and accuracy of the response time obtained by the TReaction app has not yet been evaluated. Objective This study aimed to test the validity and reliability of the TReaction app in measuring motor response time in combat sports. Methods A total of two athletes performed 59 strikes to assess the response time upon visual stimulus using the TReaction app simultaneously with a high-speed camera. Accuracy of the measure was verified using a computer simulator programmed to discharge visual stimuli and obtain the response time. Pearson correlation, Student t test for dependent samples, and the Bland-Altman analysis were performed. Accuracy was verified using the intraclass correlation coefficient. Effect size (g) and the typical error of measurement (TEM) were calculated. The significance level was set at P<. 05. Results No significant difference (P=.56) was found between both systems. The methods presented a very strong correlation (r=0.993). The magnitude of differences was trivial (g<0.25), and TEM was 1.4%. These findings indicate a high accuracy between the computer screen and the mobile app measures to determine the beginning of the task and the response time. Conclusions Our findings suggest that the TReaction app is a valid tool to evaluate the response time in combat sports athletes.
Adrenal tumor characterization on magnetic resonance images

Adrenal tumors occur on adrenal glands and are generally detected on abdominal area scans. Adrenal tumors, which are incidentally detected, release vital hormones. These types of tumors that can be malignant affect body metabolism. Both of benign and malign adrenal tumors can have a similar size, intensity, and shape, this situation may lead to wrong decision during diagnosis and characterization of tumors. Thus, biopsy is done to confirm diagnosis of tumor types. In this study, adrenal tumor characterization is handled by using magnetic resonance images. In this way, it is wanted that patient can be disentangled from one or more imaging modalities (some of them can includes X‐ray) and biopsy. An adrenal tumor image set, which includes five types of adrenal tumors and has 112 benign tumors and 10 malign tumors, was used in this study. Two data sets were created from the adrenal tumor image set by manually/semiautomatically segmented adrenal tumors and feature sets of these data sets are constituted by different methods. Two‐dimensional gray‐level co‐occurrence matrix (2D‐GLCM), gray‐level run‐length matrix (GLRLM), and two‐dimensional discrete wavelet transform (2D‐DWT) methods were analyzed to reveal the most effective features on adrenal tumor characterization. Feature sets were classified in two ways: benign/malign (binary classification) and type characterization (multiclass classification). Support vector machine and artificial neural network classified feature sets. The best performance on benign/malign classification was obtained by the 2D‐GLCM feature set. The best results were assessed with sensitivity, specificity, accuracy, precision, and F‐score metrics and they were 99.17%, 90%, 98.4%, 99.17%, and 99.13%, respectively. The highest classification performance on type characterization was obtained by the 2D‐DWT feature set as 59.62%, 96.17%, 93.19%, 54.69%, and 54.94% for sensitivity, specificity, accuracy, precision, and F‐score metrics, respectively.
Deep and Ensemble Learning to Win the Army RCO AI Signal Classification Challenge

Automatic modulation classification is a challenging problem with multiple applications including cognitive radio and signals intelligence. Most of the existing efforts to solve this problem are only applicable when the signal to noise ratio (SNR) is high and/or long observations of the signal are available. Recent work has focused on applying shallow and deep machine learning (ML) to this problem. In this paper, we present an exploration of such deep learning and ensemble learning techniques that was used to win the Army Rapid Capability Office (RCO) 2018 Signal Classification Challenge. An expert feature extraction and shallow learning approach is discussed in a simultaneous publication. We evaluated multiple state-of-the-art deep learning network architectures and adapted them to work in the RF signal domain instead of the image/computer-vision domain. The best deep learning methods were merged with the best expert feature extraction and shallow learning methods using ensemble learning. Finally, the ensemble classifier was calibrated to obtain marginal gains. The methods discussed are capable of correctly classifying waveforms at -10 dB SNR with over 63% accuracy and signals at +10 dB SNR with over 95% accuracy from an Army RCO provided training set.
Development of a Collective Anomaly Detection Network Intrusion Detection System

As the threat of cyber-attacks increase, the current suite of network intrusion prevention systems (NIPS) are becoming less effective. In order to close open vulnerabilities or isolate critical systems, weaknesses in current NIPS must be identified. This is achieved with network intrusion detection systems (NIDS). Previous NIDS employed signature based detection algorithms, yet as attacks become more advanced and varied the effectiveness of such systems is waning. By implementing anomaly detection algorithms which identify behaviours indicative of an attack, rather than known traffic signatures, a NIDS is able to detect new attacks which have not yet been fully characterised. The aim of this thesis is to develop and implement an anomaly detection based NIDS. The secondary goal to determine viability of the Collective Anomaly Detection (CAD) algorithm on modern datasets and in a real time implementation.
Research on License Plate Character Recognition Technology

In recent years, with the rapid development of China's economy and technology, social life has been progressing and urbanization has become more and more large. Many large and medium-sized cities have built highways, large parking lots, and high-end residential areas. At the same time, the rapidly growing number of cars is increasing the burden of cities, and the traffic problem is becoming more and more serious. People have put forward higher requirements for road use and motor vehicle management. In this situation, automatic license plate recognition system is produced. License plate character recognition technology is one of the key technologies in license plate recognition system. It covers many aspects including artificial intelligence, pattern recognition, computer vision and digital image processing and etc. In this paper, a character recognition algorithm based on two-level combination classification is proposed.
Study on Influences of Rear-Wheel Turn Angle to Performance of Four Wheel Steering (4WS) Vehicle

The main objective of this project is to study on influences of rear-wheel turn angle to the performances of 4-wheel-steering (4WS) vehicle. As to determine either the evaluated performances of 4WS is either an advantage or disadvantage, thus, a datum is required. Therefore 2-wheel-steering (2WS) system is also included in the study since the 2WS system does not utilize the rear-wheel turn angle (zero rear-wheel turn angles). The core problem statement implies in this project is what are the advantages and disadvantages of 4WS vehicle; is the influences of rear-wheel turn angle to 4WS does affecting in good or bad way? Thus, the first scope done in this project is the configuration of the 4WS system shall be established to correlate how the rear-wheel shall turn accordingly with respect to the steering-wheel and the front-wheel turn angle. For this study, Honda’s 4WS system is used. K.N Spentzas and I. Alkhazali stated that to study 4WS vehicle, simplification shall be made and one of it is the used of two-degree-of-freedom bicycle model. This model is very useful for analyzing the kinematics of the vehicle (i.e. turning radius, vehicle side-slip). However, when it comes to dynamic parameters (i.e. wheels side-slip angle, lateral velocity, yaw velocity), three-degree-of-freedom is desired since certain elements is essential to get a qualified data. By referring to recent literatures made related to this project, the quality of the data, methodology and the procedures to produce them are also been checked beforehand to ensure that the project is done professionally. The study includes few critical parameters such as turning radius, yaw velocity, lateral velocity, and side-slip angles that are necessary to evaluate how the rear-wheel turn angle affects the vehicle’s performances by comparing those findings between 4WS and 2WS vehicles.
Reliability Modeling of Microgrid System Using Hybrid Methods in Hot Standby Mode

The purpose of this paper is to accomplish the reliability assessment of hot standby microgrid system by using few hybrid methods. This paper has shown relative analysis on failure probability and reliability of microgrid system by means of Markov model, fault tree analysis (FTA) using reduced binary decision diagram (RBDD), and reduced sequential binary decision diagram (RSBDD) approaches. Hot standby mode is usually employed for the enrichment of reliability to prolong the system availability during any catastrophic situation. By analysis, microgrid which falls under the hot standby mode indicates that both the redundant as well as active components have similar failure rates. An inverter dominated photovoltaic system integrated to an IEEE 5-bus microgrid system is designed for which comparative overall system availability is calculated and analyzed in terms of failure probability and reliability by using hybrid approaches. Markov-based models mostly suffer from state-space explosion problems and it needs exponential probability distribution function only. Again, FTA-RBDD and FTA-RSBDD approaches have neither any constraint on time-to-failure distributions nor any computational burden issue. Moreover, FTA-RSBDD-based reliability assessment dealings are reusable with different time-to-failure rates.
Performance Analysis of Deep Learning Libraries: TensorFlow and PyTorch

Through the increase in deep learning study and use, in the last years there was a development of specific libraries for Deep Neural Network (DNN). Each one of these libraries has different performance results and applies different techniques to optimize the implementation of algorithms. Therefore, even though implementing the same algorithm and using different libraries, the performance of their executions may have a considerable variation. For this reason, developers and scientists that work with deep learning need scientific experimental studies that examine the performance of those libraries. Therefore, this paper has the aim of evaluating and comparing these two libraries: TensorFlow and PyTorch. We have used three parameters: Hardware utilization, hardware temperature and execution time in a context of heterogeneous platforms with CPU and GPU. We used the MNIST database for training and testing the LeNet Convolutional Neural Network (CNN). We performed a scientific experiment following the Goal Question Metrics (GQM) methodology, data were validated through statistical tests. After data analysis, we show that PyTorch library presented a better performance, even though the TensorFlow library presented a greater GPU utilization rate.
Restricting Answer Candidates Based on Taxonomic Relatedness of Integrated Lexical Knowledge Base in Question Answering

This paper proposes an approach using taxonomic relatedness for answer‐type recognition and type coercion in a question‐answering system. We introduce a question analysis method for a lexical answer type (LAT) and semantic answer type (SAT) and describe the construction of a taxonomy linking them. We also analyze the effectiveness of type coercion based on the taxonomic relatedness of both ATs. Compared with the rule‐based approach of IBM's Watson, our LAT detector, which combines rule‐based and machine‐learning approaches, achieves an 11.04% recall improvement without a sharp decline in precision. Our SAT classifier with a relatedness‐based validation method achieves a precision of 73.55%. For type coercion using the taxonomic relatedness between both ATs and answer candidates, we construct an answer‐type taxonomy that has a semantic relationship between the two ATs. In this paper, we introduce how to link heterogeneous lexical knowledge bases. We propose three strategies for type coercion based on the relatedness between the two ATs and answer candidates in this taxonomy. Finally, we demonstrate that this combination of individual type coercion creates a synergistic effect.
Informational Support of Decision-Making Based on Intellectual Adaptive Forum

This paper focuses on the issue of online forum information overload. Web forum technology are considered, the disadvantages of existing implementations which leads to the problem of information overload are defined. Also, the paper described the existing methods of solutions to this problem. The algorithm of structural and semantic analysis on the web forum that allows combine messages into logical units (subtopics) was offered. Structural web forum adaptation methods based on this algorithm, to automatically structure the web forum posts, according to their semantic content, was proposed. The prospects of using this approach to deal with information overload were shown.
Penerapan IoT untuk Kota Cerdas

The Internet of Things (IoT) should be able to handle heterogeneous systems in a transparent and stable manner by providing open access to the selected data subset for the development of a large number of digital services. Building a common architecture for IoT is a very complex task, mainly because of the huge variety of devices, different technologies and services required. In this paper, we focus exclusively on large urban IoT systems. IoT's development is designed to support smart city’s vision to utilize the most advanced communications technologies to support value-added services for city administration and for citizens. The paper provides a comprehensive survey of technologies, protocols, and architectures that allow for urban IoT.
An effective empirical approach to VoIP traffic classification

It is beneficial for telecommunication authorities and Internet service providers (ISPs) to classify and detect voice traffic. It can help them to block unsubscribed users from using their services, which saves them huge revenues. Voice packets can be detected easily, but it becomes complicated when the application or port information in the packet header is hidden due to some secure mechanism such as encryption. This work provides effective voice packet classification and detection based on behavioral and statistical analysis, which is independent of any application, security protocol, or encryption mechanism. First we have made initial assessments through packet feature analysis followed by the implementation of a voice detection algorithm to perform statistical analysis for classifying traffic over IP networks. The proposed voice detection algorithm is executed in three phases: registering of packet flow traces, signature-based analysis, and voice classification. In the first phase, new packets are registered. In the second phase, registered packets are tested if they are already marked as detected. In the third phase, the voice detection algorithm works at distinguishing encrypted and nonencrypted voice flows by fine-tuning the parameters, which are chosen after a detailed statistical analysis of datasets on security protocols such as secure socket layer, secure session initiation protocol, and secure realtime transport protocol. Our results demonstrate a high true positive rate (TPR) and very low false alarm rate (FAR). The proposed methodology achieves a TPR of 93.6% for offline traces, 100% for the self-configured voice setups, and 95% for the online traffic. The FAR is 0.000084% for offline traces and 0.00020% for online traces, which shows that the proposed methodology is highly efficient and can be incorporated in contemporary telecommunication systems.