Miracle-dz/UniFAR-FTUs huggingface dataset:
The development of drowsiness warning devices

The invention pertains to a process for creating triggered stop barriers as an extinguishing means in the galleries of various underground works. It comprises the expulsion, by expansion of gas or of steam under pressure by means of an appropriate source of energy, of an extinguishing product housed in the cells of a mass of a reticular material. The invention further pertains to devices for working above-mentioned process.
Free for All?: Lessons from the RAND Health Insurance Experiment

In the most important health insurance study ever conducted researchers at the RAND Corporation devised all experiment to address two key questions in health care financing: how much more medical care will people use if it is provided free of charge, and what are the consequences for their health? For three- or five-year periods the experiment measured both use and health outcomes in populations carefully selected to be representative of both urban and rural regions throughout the United States. Participants were enrolled in a range of insurance plans requiring different levels of copayment for medical care, from zero to 95 percent. The researchers found that in plans that reimbursed a higher proportion of the bill, patients used substantially more services - indeed, those who paid nothing used 40 percent more services than those required to pay a high deductible - but the effect on the health of the average person was negligible. In addition, participants who were assigned at random to a well-established health maintenance organization used hospitals substantially less than those in the fee-for-service system, again with no measurable effect on the health of the average person. This book collects in one place for the first time results previously dispersed through many journals over many years. Drawing comprehensive, coherent conclusions from an immense amount of data, it is destined to be a classic work serving as an invaluable reference for all those concerned with health care policy - health service researchers, policymakers in both the public and the private sectors, and students.
Exploring the impact of using automated writing evaluation in English as a foreign language university students' writing

A period of expanding globalization has emphasized the significant role played by the English language in both communications and information sourcing. In such an environment, it is understandable that many have sought to enhance available writing skills and the assessment of said writing skills. In recent decades, automated writing evaluation (AWE) has been applied with significant frequency to the evaluation and assessment of English writing performance in EFL environments. The purpose of this study is to examine the impact and effect of using AWE on EFL students' writing. In this study, the subjects were 57 EFL freshmen from the Department of Applied English at one university in the south of Taiwan. Both quantitative and qualitative research methods were used to conduct a quasi-experimental research by employing a t-test technique and a semi-structured interview technique, and the nonequivalent groups' pre-test/post-test control and comparison group design were applied to explore the overall effect of using AWE on the improvement of student writing in terms of accuracy, learner autonomy, and interaction. The research results reveal a significant difference between the experimental group and the control group in terms of writing accuracy following the adoption of AWE. Regarding the overall effect and the exploration of students' perceptions toward their usage of the AWE software, it shows that students who used AWE display obvious writing enhancement in terms of writing accuracy and learner autonomy awareness. The pedagogical implications of fully understanding students' usage of AWE in the writing process in order to improve EFL learners' writing performance are fully discussed.
Forward Fault Identification Element based Transient Power for DC line Protection in UHVDC System

DC line fault is a major threat to the Ultra High-Voltage Direct-Current (UHVDC) system in terms of reliability, since the long-distance bulk-power is involved. This paper proposes a forward fault identification element for DC line protection in the UHVDC system. The forward fault identification element is based on transient power (TP), which is defined as the product of transient voltage and transient current within specific frequency band. Since the DC line attenuation characteristics is fully considered, the forward fault identification element is able to detect the fault along the whole long line from forward external fault. A fault pole selection criterion based on transient power is also proposed. If a directional element is employed, a quick and reliable protection scheme can be formed. The proposed forward fault identification element is able overcome the problem of attenuation effect of long DC line suffered by the some conventional protection methods. Simulation studies carried out in PSCAD/EMTDC and the test with field fault-recording data have validated the effectiveness of the proposed forward fault identification element and fault pole selection criterion.
Abnormal Target Detection of High-Speed Train's Roof

Safety inspections are an eternal theme for high-speed trains. Routine detection methods by inspectors have many disadvantages, and so a method based on digital image processing to detect abnormal targets is introduced in this paper. Considering the length of the train, an image acquisition and stitching method are presented. Since the image we obtain is not aligned to the standard one owing to different speeds, an improved template matching algorithm is proposed to align the two images roughly, and an enhanced correlation coefficient algorithm (ECC) is adopted for precise alignment. Last, an image comparing algorithm based on structural similarity (SSIM) is proposed to detect abnormal targets.
Tackling Virtual Infrastructure Allocation in Cloud Data Centers: a GPU-Accelerated Framework

Allocating IT resources to Virtual Infrastructures (VIs) (i.e., groups of VMs, virtual switches, and their network interconnections) is a problem which belongs to a class known to be NP-hard. Several approaches designed to run on CPUs have been proposed for reducing the search space and finding suitable allocation solutions. Most algorithms, however, face scalability issues when considering current cloud data centers comprising thousands of servers. To overcome these limitations, this work offers a set of allocation algorithms designed for Graphic Processing Units (GPUs). Experimental results evaluate the scalability of the algorithms and demonstrate their ability to handle three large-scale data center topologies.
Automated Tracking System for Identification of Tagged Mice for Automatic Social Behavior Analysis

Monitoring mice social behaviors is extremely important for neurobehavioral analysis. State-of-the-art monitoring systems still require human handling for phenotype characterization with high cost and low standardization. Mice tracking and identity preservation represent the first step for phenotyping. This paper focuses on a new automated tracking system able to identify mice and keep their identities frame by frame, laying the groundwork for automatic social behavior analysis. Our system achieves more than 80% accuracy on metal ear tags identification on one-minute long videos recorded at 30 fps.
Using LOD and Fuzzy Ontology to Annotate Industrial Schemas

Human use texts, images, tables and schemas to present their data, which is difficult for the machine to understand. In the industrial domain, using schemas to present the information is more useful to describe the structure of the components and the mechanism of work. The semantic annotation technology aims at making data understandable by machines. The literature focused on annotating all data types but not schemas. In this paper, we propose an automatic process to annotate industrial schemas, using both of a fuzzy ontology that describes vague knowledge in the industrial domain and Linked Open Data (LOD) as a semantic reference. The schemas have been represented in an XML file. We use the LOD and Bohring rules [1] to link XML elements with the fuzzy ontology elements. At the end, we represent the annotation in a classic/fuzzy RDF file, which is needed in industrial control systems and maintenance systems. An online environment is used to evaluate the file structure.
Learning Driver Braking Behavior Using Smartphones, Neural Networks and the Sliding Correlation Coefficient: Road Anomaly Case Study

This paper focuses on the automated learning of driver braking “signature” in the presence of road anomalies. Our motivation is to improve driver experience using preview information from navigation maps. Smartphones facilitate, due to their unprecedented market penetration, the large-scale deployment of advanced driver assistance systems. On the other hand, it is challenging to exploit smartphone sensor data because of the fewer and lower quality signals, compared to the ones on board. Methods for detecting braking behavior using smartphones exist, however, most of them focus only on harsh events. Additionally, only a few studies correlate longitudinal driving behavior with the road condition. In this paper, a new method, based on deep neural networks and the sliding correlation coefficient, is proposed for the spatio-temporal correlation of road anomalies and driver behavior. A unique deep neural network structure, that requires minimum tuning, is proposed. Extensive field trials were conducted and vehicle motion was recorded using smartphones and a data acquisition system, comprising an inertial measurement unit and differential GPS. The proposed method was validated using the probabilistic Receiver Operating Characteristics method. The method proves to be a robust and flexible tool for self-learning driver behavior.
Blockchain-based Infrastructure Sharing in 5G Small Cell Networks

Recently, the interest in using Blockchain as a secure and distributed ledger has increased dramatically. Although the main purpose of Blockchain by means of Bitcoin was about cryptocurrency and peer to peer transactions, its application to other systems has been widely used. One of the fields that has potential possibilities to benefit from BlockChain features is telecommunication. BlockChain can be applied in case of management of various networks to reduce some expenses. In this position paper, we apply a BlockChain network with smart contract in the cellular mobile networks. The Blockchain can provide a distributed HSS in a way that the core networks of different operators can use it in a secure manner. Moreover, the smart contract can act as a distributed Self Organizing Network features to handle self-transactions among mobile operators in return of sharing small cells' infrastructure.
Cross-Network Behavioral Clustering for Managed Security Service Providers

Managed Security Service Providers (MSSP) oversee and protect customer networks often varying in the level of pre-installed security defense appliances and capabilities. This imbalance makes it challenging to offer a consistent level of service across clients. This paper presents an approach for raising the level of defense by indirectly utilizing the threat detection capabilities of secure networks based on behavioral similarity among cross-network entities. Initially, we present a holistic architectural view of the components we have deployed in order to efficiently ingest, process and analyze massive amounts of raw data from various perimeter network devices of MSSP customers. Our data analysis approach is based on entities clustering and a risk estimation routine leveraging on noisy labeling derived from advanced security appliances. Specifically, entities are clustered according to a list of aggregated metrics, characterizing the communication between local and remote network devices. Finally, we present the rationale behind the adoption of specific clustering approaches, as well as an optimization routine implemented to appropriately select the free parameters of the clustering sub-process.
TPP: Trajectory Privacy Preservation Against Tensor Voting Based Inference Attacks

The popularity of mobile devices with global positioning system (GPS) has boosted various wireless location-based services (LBSs). Certain honest-but-curious or even dishonest LBS servers may learn the users’ trajectories from location trace files, and the users’ privacy can be compromised. In this paper, we propose a quantitative approach to model trajectory inference attacks via tensor voting, which can be widely applied in computer vision and machine learning as a perceptual organization. To counter the tensor voting based attacks, we propose a novel trajectory privacy preservation TPP scheme, in which LBS users will intentionally generate dummy trajectories to obfuscate LBS servers. Meanwhile, the LBS users have the option to disclose their trajectories to trustworthy parties (e.g., users’ parents) by sending those parties a few more encrypted locations. Considering the power constraint of hand-held mobile devices, we mathematically formulate the trajectory privacy preservation problem into a mixed integer linear programming optimization problem and propose the algorithms for optimizing solutions. Through simulations and analysis, we show that the proposed scheme can effectively preserve LBS users’ trajectory privacy against tensor voting-based inference attacks with limited power consumption.
Demand Side Energy Management Under Emergency Conditions

The injection of emergency capacity may lead to Locational Marginal Price (LMP) sag, and the uneconomic LMP signals may further result in dangerous actions for reliable system operations from participants. Moreover, it has been a challenge to value emergency resources since they cannot set LMP under the current pricing and their offers may be even cheaper than regular energy resources. In this paper, an innovative transactive energy market is presented to quantify the value of emergency energy transactions in microgrid (MG) based distribution systems. For clearing the market with different transactions, the distribution system operator (DSO) considers bids/offers of MGs as well as system constraints. In addition, a straightforward method is introduced to determine MGs’ bids/offers considering available resources and flexible demands. Market clearing process and MGs bidding procedure are modeled with Mixed-integer linear programming (MILP). Numerical case studies on an MG based system illustrate the effectiveness of the proposed energy management model for the emergency conditions.
Beauty is in the Eye of the Smartphone Holder A Data Driven Analysis of YouTube Mobile QoE

Measuring the Quality of Experience (QoE) undergone by cellular network users has become paramount for cellular ISPs. Given its overwhelming dominance and ever-growing popularity, this paper focuses on the analysis of QoE for YouTube in mobile networks. Using a large-scale dataset of crowdsourced YouTube QoE measurements collected in smartphones with YoMoApp, we analyze the evolution of multiple relevant QoErelated metrics over time for YouTube mobile users. The dataset includes measurements from more than 360 users worldwide, spanning over the last five years. Our data-driven analysis shows a systematic performance and QoE improvement of YouTube in mobile devices over time, accompanied by an improvement of cellular network performance and by an optimization of the YouTube streaming behavior for smartphones.
Quality of Service Oriented Impact Analysis on Power Systems Considering EV Dynamics

As an important part of transportation electrifycation, the increasing electric vehicles (EVs) have drawn a growing concern about their impacts on power systems. Charging stations (CSs), which aggregate the EVs’ charging demand and provide charging services to them, are suitable for the study of the impacts of the moving EV loads. In this paper, a probabilistic model based on the spatial-temporal dynamics of moving EVs is applied to model the EV charging demand at various locations and time. A network calculus based QoS model, where the CSs’ charging services are modeled as the arrival and departure of energy flows, is proposed and used to evaluate the QoS performance of the CSs. Simulations are conducted within a proposed integrated traffic-power system based on IEEE-30 bus test system and the results are presented to study the impacts of the moving EV loads on each bus voltage and the QoS performance of the CSs’ charging services under different service policies.
Social Media Contact Information Extraction

Extraction of personal information from unstructured data presents itself as a challenge, as location and context of the information are unpredictable. Especially in Thai language where there is no punctuation, capitalization or ending character that separate specific names from the rest of the sentence. We propose a system capable of automatically extracting named entity information from web site snippets, using Thai celebrities as the sample named entity group and then compare the system with popular celebrity websites. We have tested our method on Thai celebrities, and our method achieved better accuracy than MThai.
Optimized Hop Angle Relativity for DV-Hop Localization in Wireless Sensor Networks

Smart multifunctional sensors integrated with wireless connectivity (also known as wireless sensor networks or WSNs) play an important role in the Internet of Things (IoT). Several challenges associated with WSNs have been researched and energy consumption represents the main limitation. Another major challenge is localization because a sensor or node should be self-contained and organized and have a low cost of integration. The range-free approach is promising due to its simplicity. Notably, it does not require additional logics and needs only key parameters, such as the number of hops and node locations. Distance-vector-hop-based localization (DV-Hop) is a pioneering range-free approach, and the corresponding localization approximation method does not require areas to be covered by nodes with known positions (also called known nodes or anchor nodes). However, the precision of this approach relies on several factors, including the node density and the method of determining the relation between the distance and the number of hops between two anchor nodes (i.e., hop size). Thus, this research enhances DV-Hop by: 1) reducing the approximation coverage to a specific area, thereby requiring fewer anchor nodes; 2) further decreasing the area using a bounding box; and 3) adopting particle swarm optimization (PSO) by integrating the number of hops and anchor nodes into the fitness function to improve the approximation precision. To evaluate the efficiency of the proposed scheme, the simulation results are compared with those of five recently proposed DV-Hop localization methods: iDV-Hop, DV-maxHop, Selective 3-Anchor DV-Hop, PSODV-Hop, and GA-PSODV-Hop.
Design and Control of a Soft Combined Actuator

Soft pneumatic actuators (SPAs) are developing rapidly in the fields of human-robot interaction and cooperation, owing to their flexiblility, light weight and safety. They will neither cause harm to human nor break themselves. The main hurdles in application of SPAs are their poor load capability and motion accuracy. Because of the inherent compliance of the materials and structures, they are not able to ensure enough strength, i.e. heavy loads can’t be supported. Even light load will give rise to the considerable deformation, which severely affects their precision. Here, a novel soft actuator is proposed, with the capability of bearing the load heavier than itself, thanks to its variable stiffness structure. When working with heavy load, its movement accuracy can still be guaranteed. Meanwhile, since high nonlinearity exists in the working process of most SPAs, approximate models or finite element analysis should be used to obtain the solution. For our actuator, by contrast, the closedform analytical solutions can be got for the forward and inverse kinematics, making it more stable and reliable. We tested the variable-stiffness characteristics and motion accuracy, the mean error of our actuator is less than 10 mm with different weights, thus it holds prospects for a wide variety of applications.
Investigations on the Degradations of Double-Trench SiC Power MOSFETs Under Repetitive Avalanche Stress

The degradations of electrical parameters for double-trench silicon carbide (SiC) power metal-oxide-semiconductor field-effect transistors (MOSFETs) under repetitive avalanche stress are investigated in this paper. The injection of hot holes into the bottom oxide of the gate trench during avalanche process is demonstrated to be the dominant degradation mechanism, while the channel is rarely influenced by the stress. The injected holes attract extra electrons in the SiC layer along the SiC/SiO<sub>2</sub> interface, decreasing the ON-state drain–source resistance (<inline-formula> <tex-math notation="LaTeX">${R}_{{dson}}$ </tex-math></inline-formula>). Due to this reason, the threshold voltage (<inline-formula> <tex-math notation="LaTeX">${V}_{{th}}$ </tex-math></inline-formula>) of the device also reduces slightly. Moreover, other than static electrical parameters, dynamic characteristics including the gate–drain capacitance (<inline-formula> <tex-math notation="LaTeX">${C}_{{gd}}$ </tex-math></inline-formula>) and the switching characteristics of the device also degrade. After being stressed by repetitive avalanche stress, the depletion region beneath the bottom of the gate trench gets thinner, leading to the increase in <inline-formula> <tex-math notation="LaTeX">${C}_{{gd}}$ </tex-math></inline-formula>, which further influences the switching behaviors. The turn- ON and turn- OFF switching times of the device are calculated. It illustrates that the repetitive avalanche stress results in an obvious delay in the turn- OFF procedure, but hardly influences the turn- ON behaviors of the double-trench SiC MOSFET.
Data-driven Affinely Adjustable Distributionally Robust Unit Commitment

This paper proposes a data-driven affinely adjustable distributionally robust method for unit commitment considering uncertain load and renewable generation forecasting errors. The proposed formulation minimizes expected total operation costs, including the costs of generation, reserve, wind curtailment and load shedding, while guarantees the system security. Without any presumption about the probability distribution of the uncertainties, the proposed method constructs an ambiguity set of distributions using historical data and immunizes the operation strategies against the worst-case distribution in the ambiguity set. The more historical data is available, the smaller the ambiguity set is and the less conservative the solution is. The formulation is finally cast into a mixed integer linear programming whose scale remains unchanged as the amount of historical data increases. Numerical results and Monte Carlo simulations on the 118- and 1888-bus systems demonstrate the favorable features of the proposed method.