Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparison of Subjective Mental Workload Measures in Driving Contexts

Objective: This study aims to compare the usefulness of subjective measures which are comprised of existing methods like NASA-TLX, Bedford-scale and ZEIS and newly developed method like DALI in measuring drivers' mental workload in terms of validity, sensitivity and diagnosticity. Background: Nowadays, with the development of intelligent vehicle and HMI, mental workload of driver has become more and more important. For this reason, the studies on drivers' mental workload about driving situation and the use of information technology equipment such as mobile phones and navigations were conducted intensively. However, the studies on measuring drivers' mental workload were rarely conducted. Moreover, most of studies on comparison of subjective measures were used with performance based measure. However, performance based measures can cause distraction effect with subjective measures. Method: Participants (N=19) were engaged in a driving simulation experiment in 2 driving contexts (downtown driving and highway driving context). The experiment has 2 sessions according to driving contexts. The level of difficulties by driving contexts were adjusted according to existence of intersections, traffic signs and signals, billboards and the number of doublings. Moreover, as criteria of concurrent validity and sensitivity, the EEG data were recorded before and during the sessions. Results: The results indicated that all subjective methods were correlates with EEG in high-way driving. On the contrary to this, in downtown driving, all subjective methods were not correlates with EEG. In terms of sensitivity, multi-dimensional scales (NASA-TLX, DALI) were the only ones to identify differences between high way and downtown driving. Finally, in terms of diagnosticity, DALI was the most suitable method for evaluating drivers' mental workload in driving context. Conclusion: The DALI as newly developed method dedicated to evaluate driver's mental workload was superior in terms of sensitivity and diagnosticity. However, researchers should consider the characteristics of each subjective method synthetically according to research objective by selecting the method in subjective measures. Application: The results of this study could be applied to the intelligent vehicle and next generation of HMI design to decrease mental workload of driver and for the development of new subjective method in vehicle domain.
Self-tuning adaptive neurocontroller for brushless DC motors

This paper describes a self-tuning adaptive neurocontroller for brushless DC motors. Nonlinear and unknown motor dynamics are identified by using a multilayer neural network and the control input for the motor is derived from the identified model. The effect of the load torque on the control system is damped by filtering the control input. Simulation and experimental results show that the self-tuning adaptive neurocontrol has a good tracking performance but needs an adaptive filter and a parallel PI controller in the case of disturbances.
Building a Geo-Demographic Segmentation Model: the Case of Hanoi City, Vietnam

Location is one of the most crucial factors determining enterprises’ strategies when entering, exploiting or expanding into a new market or a new area. The study illustrates how to create a detailed analytical model of the market segmentation in all districts of Hanoi using K-means clustering and principal component analysis (PCA). The model describes the demographic characteristics of each area such as age, occupation, education level, etc.; thereby giving enterprises precise sources of information about geographic location, which helps reducing the cost and time in decision-making process to enter or expand the businesses in a new area.
Security Gateway System Team High-Performance Intrusion Detection in FPGA-based Reconfiguring Hardware

The fast extension of inexpensive computer networks has increased the problem of unauthorized access and tampering with data. Many Intrusion Detection Systems(IDSs) are developed till now to respond these network attacks, which is increasing more and more in number and has polymorphic/metamorphic nature. Most of these systems, however, have problem to process traffic volume that is increasing continuously whereby systems are designed and developed only for real-time security analysis over Fast Ethernet network. As network technology presses forward, Gigabit Ethernet has become the actual standard for large network installations. Therefore, it is necessary to research on security analysis mechanism, which is capable to process high traffic volume over the high-speed network such as Gigabit Ethernet network. This paper proposes Gigabit Intrusion Detection System to detect and respond attacks on the high-speed network. It is possible through the functions that is processed in FPGA(Field Programmable Gate Array) Logic. Our proposed Gigabit Intrusion Detection System has hardware architecture that can provide efficient way to detect and respond variant attack behaviors on high-speed and high volume large-scale network. We have made effort to design and implement IDS that is run as a lower branch of our system named ‘Next Generation Security System’. Our IDS named ‘Security Gateway System’ has detection mechanisms that can be applied to Gigabit-Ethernet links. However, the real-time traffic analysis and high-speed intrusion detection is not an easy task. In this paper, we briefly introduce the whole architecture of our system designed to perform intrusion detection on high-speed links. And then, we present the efficient detection mechanism that is run in FPGA-based Reconfiguring Hardware. In other words, we focus on the network intrusion detection mechanism applied in a lower branch of our system.
Sensor Fault Detection and Estimation for Nonlinear Process with Time-Delayed Variables

Time-delayed variables is of potentially critical impact on product quality within the processes of Metallurgical industry, Aviation industry and Chemical industry. It is very important to detect the sensor fault and estimate the values of time-delayed variables immediately as soon as faults occurred. Any fault of the time-delayed variable implies that a detrimental effect on the process will go unnoticed until the next sample become available. During this time, of course, poor quality product continues to be manufactured, a situation which is clearly unacceptable from a cost-effectiveness point of view. For nonlinear process with time-delayed variables, kernel partial least squares is introduced to monitor and estimate the fault of the Tennessee Eastman benchmark. Simulation results demonstrate the effectiveness of the proposed algorithm.
Highly Modularized Learning System for Behavior Acquisition of Functional Robots

An essential ability for highly functional robots, typified by humanoid robots, is acquiring a behavior by itself. This ability enables the end-users to teach their wishes easily. This thesis aims to realize an objective-based task design. Concretely, this thesis proposes a “highly modularized learning system” where reinforcement learning (RL) methods and related methods are modularized to construct a learning system for a robot. The proposed system has two distinct features: (1) learning strategies (LSs), methods to improve RL, are also modularized, and (2) multiple LSs are applied to each task of the robot multiple times. Examples of LSs are dimension reduction, hierarchical RL, and transfer learning. Using the LSs, the system automatically generates multiple behavior modules to learn policies of a task. For feature (2), Boltzmann selection method with upper confidence bound (UCB) is introduced to select a behavior module that is actually used to control the robot. Some transfer LSs generate new modules from previously learned modules. Thus, each behavior module is generated through a sequence of LSs. The core algorithm is referred to as LS fusion. This thesis also proposes some elemental technologies to define LSs; mainly, a discrete action set named DCOB and a method to decompose a dynamics model. The discrete action set DCOB has a capability for large domains. The name is derived from the fact that an action is a trajectory Directed to the Center Of a target Basis function. DCOB is extended to learn continuous actions keeping the exploration ability of the discrete set; the method is named WF-DCOB because of using wire-fitting. Some transfer LSs are defined by using WF-DCOB. The method to decompose a dynamics model can extract a task invariant element, which enables to transfer a dynamics model of a task to one of the other tasks. ∗Doctoral Dissertation, Department of Information Systems, Graduate School of Information Science, Nara Institute of Science and Technology, NAIST-IS-DD0861020, March 17, 2011.
Exploring Ensemble-Based Class Imbalance Learners for Intrusion Detection in Industrial Control Networks

Classifier ensembles have been utilized in the industrial cybersecurity sector for many years. However, their efficacy and reliability for intrusion detection systems remain questionable in current research, owing to the particularly imbalanced data issue. The purpose of this article is to address a gap in the literature by illustrating the benefits of ensemble-based models for identifying threats and attacks in a cyber-physical power grid. We provide a framework that compares nine cost-sensitive individual and ensemble models designed specifically for handling imbalanced data, including cost-sensitive C4.5, roughly balanced bagging, random oversampling bagging, random undersampling bagging, synthetic minority oversampling bagging, random undersampling boosting, synthetic minority oversampling boosting, AdaC2, and EasyEnsemble. Each ensemble’s performance is tested against a range of benchmarked power system datasets utilizing balanced accuracy, Kappa statistics, and AUC metrics. Our findings demonstrate that EasyEnsemble outperformed significantly in comparison to its rivals across the board. Furthermore, undersampling and oversampling strategies were effective in a boosting-based ensemble but not in a bagging-based ensemble.
Analisis Perbandingan Tax Amnesty Jilid I dan Jilid II (Program Pengungkapan Sukarela) Serta Peluang Keberhasilannya

The government has implemented the tax amnesty volume I in July 2016 and ended in March 2017. In early 2022, the government will plan to implement the tax amnesty volume II, which was introduced as the Voluntary Disclosure Program (PPS). This program refers to the Tax Harmonization Law Number 7 of 2021. Although the results obtained during the tax amnesty volume, I did not meet the target, in 2022 the government still plans to implement the tax amnesty volume II. For this reason, it is necessary to analyze the difference between the tax amnesty volume I and the tax amnesty volume II (PPS), as well as predict its effectiveness if this tax amnesty will be implemented later. This study method focuses on a comparative qualitative descriptive method in which data collection is carried out by taking from the library, website, law and articles discussing this tax amnesty. The results obtained are that there are fundamental differences between tax amnesty I and tax amnesty II. The differences are found in the data, rates, and tax subjects. For taxpayers who have participated in the 2016 tax amnesty volume I, the tariff charged is cheaper than those who do not participate in the first volume I tax amnesty and there are restrictions on subjects who may participate in the second tax amnesty. The effectiveness of this second volume of tax amnesty is predicted to be successful with a record that socialization must be maximal, there is no perception that the tax amnesty endangers honest taxpayers, and the view of taxpayers that this tax amnesty is the last tax amnesty so that there is a fear of not paying taxes and being subject to fines.
Crawling Strategy Based on Domain Ontology of Emergency Plans

In order to build an effective emergency plans crawler, this paper affords a new algorithm for emergency plans identification and a new idea of URL predict using URL pattern library. Through the experiment, we found the crawler achieves efficient collection of emergency plans from the web. The emergency plan crawler is proper for emergency plan collection. Background and significance Emergency plans guide precaution of potential disasters. There is no emergency plan base which makes the emergency plans be easily shared. In the research we crawl emergency plans and then save them to the server. It is the foundation to share and search emergency plans. There are different kinds of web pages analysis methods. In TF/IDF it is thought that words appear in less documents, and appear in one document more times represent the document better[1]. Naive Bayesian classifier method assumes each feature of the samples cannot be associated with other features [2]. C4.5 decision tree needs be given a bunch of samples[3]. Ehrig uses the ontology into the subject crawling in his work [4]. Ye Yuxin [5] introduces ontology reasoning into theme crawler. BestFirst [6] believes that if a web page A is related to a theme, then the page pointed by the web page A should also be related to this theme. PageRank algorithm is based on network topology [7]. HITS assesses the quality of the web by two weights—the Authority and the Hub [8]. In this paper we learn from the HITS and construct our own page crawling method. Theme crawler of emergency plans domain Links that were handled Pages base URL pattern libraray Seed URLs Emergen cy plan ontolgoy
Cross-Domain Modality Fusion for Dense Video Captioning

Dense video captioning requires localization and description of multiple events in long videos. Prior works detect events in videos solely relying on the visual content and completely ignore the semantics (captions) related to the events. This is undesirable because human-provided captions often also describe events that are visually non-present or subtle to detect. In this research, we propose to capitalize on this natural kinship between events and their human-provided descriptions. We propose a Semantic Contextualization Network (SC-Net) to encode the visual content of videos by representing it in a semantic space. The representation is further refined to incorporate temporal information and transformed into event descriptors using a hierarchical application of short Fourier transform. Our proposal network exploits the fusion of semantic and visual content enabling it to generate semantically meaningful event proposals. For each proposed event, we attentively fuse its hidden state and descriptors to compute discriminative representation for the subsequent captioning network. Thorough experiments on the standard large-scale ActivityNet Captions dataset and additionally on YouCook-II dataset show that our method achieves competitive or better performance on multiple popular metrics for the problem.
A Bioinformatics Approach to Identifying Potential Biomarkers for Cryptosporidium parvum: A Coccidian Parasite Associated with Fetal Diarrhea

Cryptosporidium parvum (C. parvum) is a protozoan parasite known for cryptosporidiosis in pre-weaned calves. Animals and patients with immunosuppression are at risk of developing the disease, which can cause potentially fatal diarrhoea. The present study aimed to construct a network biology framework based on the differentially expressed genes (DEGs) of C. parvum infected subjects. In this way, the gene expression profiling analysis of C. parvum infected individuals can give us a snapshot of actively expressed genes and transcripts under infection conditions. In the present study, we have analyzed microarray data sets and compared the gene expression profiles of the patients with the different data sets of the healthy control. Using a network medicine approach to identify the most influential genes in the gene interaction network, we uncovered essential genes and pathways related to C. parvum infection. We identified 164 differentially expressed genes (109 up- and 54 down-regulated DEGs) and allocated them to pathway and gene set enrichment analysis. The results underpin the identification of seven significant hub genes with high centrality values: ISG15, MX1, IFI44L, STAT1, IFIT1, OAS1, IFIT3, RSAD2, IFITM1, and IFI44. These genes are associated with diverse biological processes not limited to host interaction, type 1 interferon production, or response to IL-gamma. Furthermore, four genes (IFI44, IFIT3, IFITM1, and MX1) were also discovered to be involved in innate immunity, inflammation, apoptosis, phosphorylation, cell proliferation, and cell signaling. In conclusion, these results reinforce the development and implementation of tools based on gene profiles to identify and treat Cryptosporidium parvum-related diseases at an early stage.
Perspective of Direct Search for Dark Components in the Universe with Multi-Wavelengths Stimulated Resonant Photon-Photon Colliders

We explore a possibility to detect dark components in the Universe via stimulated photon–photon collisions by focusing two-frequency coherent electromagnetic fields in a vacuum. Those fields are assumed to be pulsed reaching Fourier transform limits in near-infrared, THz, and GHz frequency bands, respectively. The numbers of signal photons as a result of exchange of a pseudoscalar-type pseudo Nambu–Goldstone boson have been evaluated in the individual frequency bands. Within presently available beam intensities, we found that the QCD axion scenarios are thoroughly testable in the mass range 10−6–100 eV based on the common method. Furthermore, we show a possibility to reach the weak coupling domain even beyond the gravitationally weak coupling strength if pulse compression in the GHz band is realized in the near future development.
The enhancement of image based on modulus maxima for GPR targets detection

The WTMM-based descriptors, unlike global contour descriptors such as the Fourier descriptors, provide precise local sharp information. This paper introduces an algorithm of image enhancement based on modulus maxima for ground-penetrating radar (GPR) targets detection. By tracking the wavelet transform local maxima at fine scales, singularities can be detected, and the important information of image is also selected. According to the singularities of GPR A-scan signal and the characteristic of GPR signal, GPR image can be commendably given prominence to the targets. Finally, the enhancement of image based on modulus maxima was successfully realized.
Cancer in Anti‐Neutrophil Cytoplasm Antibody‐Associated Vasculitis and Polyarteritis Nodosa in Australia: A Population‐Based Study

The study objective was to compare incident cancer rates among patients with anti‐neutrophil cytoplasm antibody‐associated vasculitis (AAV) and polyarteritis nodosa (PAN) in Western Australia (WA) with the general population and perform time‐varying analyses to identify periods with greatest excess cancers.
A combined cavity for high sensitivity THz signal detection

Detection of signals in the THz frequency region is important for applications of THz waves in many areas, such as in medical imaging, forbidden-combined sensing, weapon monitoring, and wireless communications. Cooling system operating under very low temperature, for eliminating the unwanted background THz radiation that exists everywhere in room temperature, sets an obstacle for applications of conventional THz signal detecting systems. We present a combined cavity that can pick out the useful signal in high sensitivity, while the influence of the background THz radiation can be neglected in the detection. The combined cavity consists of a point-defect photonic-crystal resonator and a photonic-crystal WGR. The two resonators are coupled together through optical tunneling to form the combined cavity. Under proper operating parameters, the two resonators are in simultaneous resonance, and the field intensity in the point-defect resonator can be thousands of times of that of an incoming THz signal for a given frequency, so that the sensitivity of detection can be very high. Since the background THz radiation is not in resonance with the cavity, the influence of it to the detection of THz signals wanted can be neglected, and thus cooling systems can be omitted. Plane wave expansion method is used to determine the resonance wavelengths and mode patterns of the cavity. Finite-difference- time-domain method is used to find the quality factors and to simulate the resonance process. Parameter optimization and the conditions for simultaneous resonance of the two cavities are studied.
Sismos à l’Ecole: A Worldwide Network of Real‐Time Seismometers in Schools

After each large destructive earthquake in the world, shocking images are presented to the adults and children on the TV and through the Internet. All sorts of questions arise: Some of them are very rational, whereas others are driven by fear of a global catastrophe or even a curse. For example, in March 2011, after the giant Tohoku, Japan, earthquake, seismologists everywhere were asked to provide explanations and often to give their opinion on what had happened. How could there be such a disaster? Can it happen to us? Many took the time to talk to students in classrooms to make clear what we know and what we are still trying to understand and also tried to limit irrational fears. Teachers, as well as researchers, were on the front line, particularly those who actively participate in networks of educational seismology in the United States (e.g., Levy and Taber, 2005), England (e.g., Denton, 2009), Italy (e.g., Cantore et al. , 2003; Solarino and Eva, 2009), Switzerland (e.g., Sornette and Haslinger, 2009), and other countries in the world. Thanks to Sismos a l’Ecole (SaE) network stations, accessible in real time in various parts of the world, students saw the waves of the many aftershocks passing through their school and through other schools in the world. Thus, they immediately understood that the waves of Japanese earthquakes first arrived at the high school stations in Taipei, then in Canberra, and then in Istanbul before arriving under their feet at their school. Let us see how this network is structured and how it allows …
An evaluation of current environmental management systems as indicators of environmental performance

Purpose – This paper aims to evaluate current environmental management systems as indicators of the environmental performance of an organisation.Design/methodology/approach – It considers, in particular, the development of current environmental management systems BS EN ISO 14001:1996 and the eco‐management and audit scheme (EMAS) 761/2001 EEC.Findings – Currently, organisations implementing either BS EN ISO 14000:1996 or EMAS do not need to comment on overall environmental performance. Environmental management is viewed as the control of all human activities that have significant impact on the environment. Neither standard comments on the degree of control exercised, the approach taken, or the effectiveness of that control.Originality/value – Environmental performance evaluation guidelines are appraised, and quality awards as a conceptual framework for classification of environmental performance are introduced.