Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of the Korean Aviation Turbulence Guidance (KTG) System using the Operational Unified Model (UM) of the Korea Meteorological Administration (KMA) and Pilot Reports (PIREPs)

Korean aviation Turbulenc Guidance (KTG) system is developed using the operational unified model (UM) of the Korea Meteorological Administration (KMA) and pilot reports (PIREPs) over East Asia. The KTG system comprised of twenty turbulence diagnostics that represent various turbulence potentials and have the best forecasting skills, which are combined into a single ensemble-averaged index, namely KTG, at upper-(above FL250) and mid-(below FL250) levels. It is found that the overall performance of the KTG is higher than those produced from the one single best index, and satisfies the minimum criteria (80% accuracy) that the system is operationally useful in aviation industry.
Gamification of complex morphology learning: the case of Turkish

Abstract Grammatical information, embedded at the word level, makes the acquisition of morphologically rich languages quite complex as well as the language exercise generation process for teachers. This study introduces a new gamification approach for complex morphology learning and aims to analyze the students’ perceptions towards it. In this approach, the morphology components and their interactions are gradually introduced to the learners within a gamified environment through automatically generated exercises. Although not specific to any language, the approach has been applied to Turkish which is a strong representative of morphologically rich languages. The study was conducted for three weeks with international students in an introductory level Turkish language course via a mobile application developed using finite-state transducer technology to model morphology. Questionnaires, e-journals and semi-structured interviews were employed to examine the perceptions and experiences of the students in terms of perceived efficacy, system usage, engagement, loyalty, perceived enjoyment, attitude, and willingness to recommend. The findings of the study revealed that the students had positive perceptions towards the proposed approach and found it effective for their learning process. The approach is considered to fill an important gap in grammar exercises for learning morphologically rich languages.
Cotton Stalks Cement-Based Block Material and its Preparation Method

In Xinjiang, by making use of cotton stalks after cotton picked, people researched and developed a kind of walling material that is cotton stalks cement-based blocks, elaborated its preparation methods, and evaluated its technology effect of cotton stalks cement-based block material. Results show that the cotton stalks cement-based block material have good properties like wide source, easy processing and wonderful performance; by soaking cotton straw broken material with alkali liquor, made cotton straw plant fiber reinforced material and the cement matrix have good interface adhesion; the block material is provided with load-bearing, heat preservation performance and cracking resistance, it can be used for low-rise buildings bearing wallboard and retaining wall of multiple floors in cotton planting region.
University departments evaluation: a multivariate approach

Aim of the paper is to present a new model, based on multivariate statistic analyses, allowing to express a synthetic judgement on Departments activities by taking into consideration the whole set of indicators describing them both as aggregations of researchers and as University autonomous organs. The model, based on Principal Component Analysis and Cluster Analysis, allows both to explain the determinants of Departments performances, and to classify them into homogeneous groups. The paper shows the results obtained by testing the proposed model on University of Naples “L’Orientale” Departments, using data extracted by the 2007 assessment report to the Ministry of University and Research.
Guided Feature Identification and Removal for Resource-constrained Firmware

IoT firmware oftentimes incorporates third-party components, such as network-oriented middleware and media encoders/decoders. These components consist of large and mature codebases, shipping with a variety of non-critical features. Feature bloat increases code size, complicates auditing/debugging, and reduces stability. This is problematic for IoT devices, which are severely resource-constrained and must remain operational in the field for years. Unfortunately, identification and complete removal of code related to unwanted features requires familiarity with codebases of interest, cumbersome manual effort, and may introduce bugs. We address these difficulties by introducing PRAT, a system that takes as input the codebase of software of interest, identifies and maps features to code, presents this information to a human analyst, and removes all code belonging to unwanted features. PRAT solves the challenge of identifying feature-related code through a novel form of differential dynamic analysis and visualizes results as user-friendly feature graphs. Evaluation on diverse codebases shows superior code removal compared to both manual feature deactivation and state-of-art debloating tools, and generality across programming languages. Furthermore, a user study comparing PRAT to manual code analysis shows that it can significantly simplify the feature identification workflow.
CGRA-based Deep Neural Network for Object Identiﬁcation

The objective of this work is to accelerate an object detection application using the VersatCNN, a Coarse Grained Reconﬁgurable Array (CGRA) architecture developed for eﬃcient inference of Convolutional Neural Network (CNN). The performance objective is to enable real-time execution - 30 frames per second - on low-end embedded systems. The object detection application accelerated is the Tiny YOLOv3, based on a CNN developed for constrained environments. The Tiny YOLOv3 network is composed of Convolutional, Maxpool, Route, Upsample and Yolo layers. The application also includes pre and post-processing routines. This thesis work uses the IOb-SoC, a RISC-V based system on a chip (SoC) from IObundle as a baseline hardware platform for the project development. Peripheral modules for measuring performance and enabling ethernet communication are integrated into the IOb-SoC. The embedded version of Tiny YOLOv3 is proﬁlled for RISC-V only execution to identify the parts that require acceleration. The VersatCNN is integraded into the system as a peripheral. The work mainly focuses on the design of CGRA dataﬂow conﬁgurations, as the diﬀerent parts of the application require distinct conﬁguration strategies for the multiple layer types and the pre and post-processing routines. The ﬁnal system achieves over 30 FPS for the complete Tiny YOLOv3, with a clock frequency of 143 MHz and a 832x parallelism factor for convolutional operations.
Knowledge Graph Guided Simultaneous Forecasting and Network Learning for Multivariate Financial Time Series

Financial time series forecasting is challenging due to limited sample size, correlated samples, low signal strengths, among others. Additional information with knowledge graphs (KGs) can allow for improved prediction and decision making. In this work, we explore a framework GregNets for jointly learning forecasting models and correlations structures that exploit graph connectivity from KGs. We propose novel regularizers based on KG relations to guide estimation of correlation structure. We develop a pseudo-likelihood layer that can learn the error residual structure for any multivariate time-series forecasting architecture in deep learning APIs (e.g. Tensorflow). We evaluate our modeling and algorithmic proposals in small sample regimes in real-world financial markets with two types of KGs. Our empirical results demonstrate sparser connectivity structures, runtime improvements and high-quality predictions.
Frequency-domain analysis of intermittent control

Abstract Intermittent control is a feedback control design method that combines both continuous-time and discrete-time domains. A recent result shows that this form of intermittent control can be rewritten as a sampled-data feedback system with a particular vector generalized hold. This paper builds on this result to give, for the first time, a frequency-domain analysis of the closed-loop system containing an intermittent controller. This analysis is illustrated using two examples. The first example is related to the human balance control system and is thus physiologically relevant. The second example gives a theoretical explanation of the phenomenon of self-induced oscillations in intermittent control systems.
Nondissipative current diverter using a centralized multi-winding transformer

Series connected battery strings are prone to dramatic reduction in life if individual cells are not maintained at the same charge level. This can be achieved by diverting the excess energy around fully charged cells while continuing to charge undercharged ones. Nondissipative current diverters provide a means to divert the charging current away from a fully charged cell to the weak cells in the stack in a nondissipative manner. This allows the rest of the stack to be fully charged while avoiding overcharging healthy cells. This paper presents a new technique for equalizing a series battery stack using a nondissipative current diverter.
Experimental Facility for the Study of Liquid Overfeed Fin-and-Tube Evaporators: Validation of Numerical Models

An experimental facility specially designed for the study of liquid overfeed fin-and-tube evaporators was developed. It is intended for the experimental validation of a detailed liquid overfeed evaporator model, permitting testing as either a part of the refrigeration system or in a loop without a compressor. The facility can also be used for the experimental evaluation of refrigeration system performance-prediction models. A detailed description of the facility and its instrumentation, emphasizing the data uncertainty analysis and the verification techniques, is provided. A method for experimentally estimating the systematic uncertainties in the measurement of the temperature sensors after calibration, accounting for the whole signal path, is also presented. Results from a detailed numerical model of liquid overfeed evaporators, developed for design optimization, were compared with the experimental data. Six well-known correlations for the two-phase flow heat transfer coefficients were studied in order to assess their influence in the overall evaporator performance prediction.
Independent Wheelchair Securement

The goal of this Innovations Deserving Exploratory Analysis (IDEA) project was to develop an aisle-side containment device to enable users of wheelchairs to safely and independently use rear-facing containment on large buses, in lieu of the current standard, a four- point tie-down wheelchair securement system. The device includes a backrest and a movable aisle-side containment structure. The operation would involve the user backing into the securement space until the rear of the chair is in contact with the backrest. Activation of the aisle-side device would provide the second side of containment and the bus wall acts as the third side. This project was completed in two stages. The first of these stages included research and analysis to determine design requirements for aisle-side containment. The second stage used those requirements to complete detailed development of a backrest and aisle-side containment system. The concept was developed to the point of having a complete computer-based 3-D model from which a working prototype was built. In field tests using a manual wheelchair and a four-wheel scooter, the prototype successfully demonstrated the effectiveness of this retractable aisle-side containment system. Wheelchair rotation and tipping during extreme vehicle maneuvering were both shown to be prevented by the aisle-side containment system.
Multiple strategy generation for wargaming

In this paper we present a framework for the automated generation of strategies that accounts for the multiple kinds of uncertainty found in war games, provides for a domain independent approach to strategy generation, and results in robust strategies. Our approach is to sample over multiple trials for varying victory conditions, different threat profiles, and variable system performance to achieve a degree of independence in the resulting strategy. This allows a search for robust strategies versus those that are effective only under specific conditions. War games have uncertainty in what is needed to achieve victory, in system performance, and in threat behavior. There are multiple options for forces, employment, and warfare styles. All these factors combine to produce a large, complex space of possible solutions or strategies. Through the use of powerful search techniques like evolutionary computation and modern computing assets it has become practical to search this space for strategies with robust performance. Our framework is modular in nature, allowing a variety of search techniques, warfare scenarios, system models, and other parameters to be interchanged. In the paper the framework described above is demonstrated using an antisubmarine warfare scenario. Evolutionary programming techniques are used to search the space of possible strategies.
Corporate Social Responsibility Communication and Company Reputation in Pandemic Era

Corporate Social Responsibility (CSR) is a way to build a company’s positive reputation. In the new media era and during the COVID-19 pandemic, online media has become an effective communication medium for companies to communicate CSR and build a positive reputation among stakeholders. This study examines BRI Peduli CSR communication during the COVID-19 pandemic through online media on the bri.co.id website that can increase BRI’s positive reputation. The research method used in this research is descriptive qualitative research with case study method, data collection is done by in-depth interview and literature study. The data analysis technique was carried out by triangulating sources to get accurate results regarding CSR communications during the COVID-19 pandemic. The results show that effective CSR communication between companies and communities that are beneficiaries of CSR programs will greatly determine the program’s success. The CSR program is an official communication channel so that all stakeholders get accountable and reliable information. BRI has also applied the principle of digitization to new media but has not yet reached the level of interactivity and audience-generated media. Online media can build a positive image and be reported CSR Program massively during the COVID-19 pandemic.
Identifying Network Application Layer Protocol with Machine Learning

Within modern network management system, application identifying plays an import ant role. Transport layer analysis is used by most systems. The most common tech nique is to inspect the transport layer source and destination port number attri butes. However, this technique is ineective, due to applications use various p orts and users’ behaviors. In this paper, we uses a machine learning approach wi th classiers that produces a tree like model as result. This is dierent than the existing methods, in that only the packet payload length is used as an attr ibute for classiers instead of other network characteristics. The assumption i s that the sequence of network packets in a ow gives a signature that can iden tify application layer protocols. The results of experiments with dierent clas siers conrm this assumption.
Multi-path Routing Improved Protocol in AODV Based on Nodes Energy

Aiming at energy-constrained of Ad Hoc network, this paper proposes a multi-path routing protocol (EM-AODV)I n AODV that based on nodes energy. EM-AODV designs methods of obtaining nodes energy by upgrading the route discovery and route maintenance process of AODV, calculates the path of comprehensive energy derived path priority by routes total hops and nodes energy to format the multi-path routing mechanism. The energy as the metric prerequisite during the routing process, by setting nodes energy bound and balancing nodes data forwarding to postpone network lifetime. Simulation results show that EM-AODV has lower average end-to-end delay, well improve the energy consumption.
Quasi-stationary analysis of the electric field in anisotropic laminated composites

This paper deals with the analysis of the quasistationary electric field in the presence of anisotropic laminated composites. Both cylindrical and box composite structures are examined in the two-dimensional domain. Two methods are proposed to solve the problem. The first is a numerical procedure employing the finite-difference time domain which uses a direct implicit scheme; the second is an equivalent circuit network which is solved by the use of the standard circuit simulator SPICE. For the composite box structure, the results obtained by means of the two approaches are compared; it is possible to observe a good agreement between them.
Modelling of the human vasomotor control system and its application to the investigation of arterial disease

A control systems approach has been made to the investigation of the peripheral vasomotor control system. The thermal vasomotor control of digital bloodflow has been investigated in 46 subjects. Fourteen were controls, 26 were suffering from Raynaud's phenomenon and the rest had suspected malfunctions of the sympathetic nervous system. To assess the frequency response of the system a square-wave temperature stimulus was applied externally to one hand while the blood flow in the forefinger of the contralateral hand was recorded. The frequency and phase response of the control system was investigated between 0.1 and 0.0125 Hz. The results show that patients suffering from Raynaud's phenomenon have a different frequency response from the normal control group. At low frequencies (<0.025 Hz) the difference between the groups is highly significant (p < 0.001, student-t). A mathematical model of the control system was devised, and its use has indicated that the differences between patients with Raynaud's and normals may be explained in terms of both the gain and time delay in the control loop. Using this model and the data obtained it should be possible to develop a new method of differential diagnosis.
DeepDi: Learning a Relational Graph Convolutional Network Model on Instructions for Fast and Accurate Disassembly

Disassembly is the cornerstone of many binary analysis tasks. Traditional disassembly approaches (e.g., linear and recursive) are not accurate enough, while more sophisticated approaches (e.g., Probabilistic Disassembly, Datalog Disassembly, and XDA) have high overhead, which hinders them from being widely used in time-critical security practices. In this paper, we propose D EEP D I , a novel approach that achieves both accuracy and efﬁciency. The key idea of D EEP D I is to use a graph neural network model to capture and propagate instruction relations. Speciﬁcally, D EEP D I ﬁrstly uses superset disassembly to get a superset of instructions. Then we construct a graph model called Instruction Flow Graph to capture different instruction relations. Then a Relational Graph Convolutional Network is used to propagate instruction embeddings for accurate instruction classiﬁcation. D EEP D I also provides heuristics to recover function entrypoints. We evaluate D EEP D I on several large-scale datasets containing real-world and obfuscated binaries. We show that D EEP D I is comparable or superior to the state-of-the-art disassemblers in terms of accuracy, and is robust against unseen binaries, compilers, platforms, obfuscated binaries, and adversarial attacks. Its CPU version is two times faster than IDA Pro, and its GPU version is 350 times faster.
Numerical validations of a nonlinear PML scheme for absorption of nonlinear electromagnetic waves

There have been several algorithms which extend the finite-difference time-domain (FDTD) solution of Maxwell's equations to nonlinear electromagnetic problems. Relative to other methods, FDTD achieves robustness by directly solving for the fundamental quantities, electric field E, and magnetic field A in space and time, rather than performing asymptotic analyses or assuming paraxial propagation and nonphysical envelope functions. As a result, the FDTD method is almost completely general and can account for any type of electromagnetic problems. As in linear cases, for a practical simulation, nonlinear FDTD modeling also requires the development of absorbing boundary conditions (ABCs) to effectively absorb the nonlinear electromagnetic waves for open nonlinear structures. In this paper, based on the Berenger's perfectly matched layer (PML), a nonlinear PML (nPML) absorbing scheme is presented and then implemented in the transmission-line matrix (TLM)-based FDTD method. Numerical results are given to demonstrate the effectiveness of the nPML proposed.
When we move together : the neural correlates of joint action

Moving a set dinner table often takes two people, and doing so without spilling the glasses requires the close coordination of the two agents’ actions. It has been argued that the mirror neuron system may be the key neural locus of such coordination. Instead, here we show that such coordination recruits two separable sets of areas: one that could translate between motor and visual codes and one that could integrate these information to achieve common goals. The former includes regions of the putative mirror neuron system, the latter, regions of the prefrontal, posterior parietal and temporal lobe adjacent to the putative mirror neuron system. Both networks were more active while participants cooperated with a human agent, responding to their actions, compared to a computer that did not, evidencing their social dimension. This finding shows that although the putative mirror neuron system can play a critical role in joint actions by translating both agents’ actions into a common code, the flexible remapping of our own actions with those of others required during joint actions seems to be performed outside of the putative mirror neuron system.
Synthesis of ZnO Nanorods on Silicon for Methanol Gas Sensor

Zinc Oxide nanorods (ZnO NRs) were successfully synthesized via hydrothermal method. The growth process was conducted with a seed layer concentration of 30 millimolar (mM). The as-synthesized nanostructures were characterized by x-ray diffraction (XRD), scanning electron microscope (SEM), atomic force microscope (AFM), and ultraviolet-visible (UV-VIS) spectrophotometer. The analysis of the present work results revealed pure Wuartzite ZnO hexagonal nanostructures with preferred orientation (002) along c-direction. It was found that the calculated band gap and the crystallite size are 3.2 eV and 53.18 nm respectively. A methanol gas sensor was fabricated based on the annealed ZnO NRs on the silicon substrate. The optimized sensitivity at 250 ppm methanol vapor with fast response and recovery time was achieved. So, ZnO NRs film can serve as a good candidate for a methanol gas sensing device. KeywordsHydrothermal, ZnO NRs, Methanol gas sensors, Crystallite size. How to cite this article: A.D. Faisal, M.A. Jaleel, and F.Z. Kamal, “Synthesis of ZnO Nanorods on Silicon for Methanol Gas Sensor,” Engineering and Technology Journal, Vol. 37, Part B, No. 3, pp. 74-81, 2019.