Miracle-dz/UniFAR-FTUs huggingface dataset:
Knowledge-based Taxonomic Scheme for Full-Reference Objective Image Quality Measurement Models

In this paper, we propose a knowledge-based taxonomic scheme of the objective image quality assessment metrics including the key concepts involved for each approach. Our classification is constructed according to six criteria based on the information available at each stage of the design process. The novelty of the present classification scheme is that the six layers are linked via a single concept where each layer represents a single type of knowledge about: 1) the reference image, 2) the degradation type, 3) the visual perception field, 4) the human visual physiology and psychophysical mechanisms, 5) the processes of the visual information analysis, and finally 6) knowledge about perceptual image representation and coding. The first layer helps delineate boundaries between full-reference image quality assessment metrics, that are further classified trough layers 2 to 6, and other families (reduced-reference and no-reference). In addition, gradual degrees are considered for knowledge about specific areas related to visual quality evaluation processes. The proposed taxonomic framework is intended to be stepwise, to help sorting out the fundamental ideas behind the development of objective image quality metrics often working on the luminance channel or marginally on the RGB channels. The aim is to congregate the already published classification schemes and to methodologically expand new aspects according to which an efficient and straightforward classification of the image quality assessment algorithms becomes possible. This is significant because of the increasing number of developed metrics. Furthermore, a systematic summarisation is necessary in order to facilitate the search and application of image quality techniques.
Piling up and Spelling out! - Repair work in Challenged Interaction

Troubles in intersubjective understanding are, according to Schegloff (1992), overwhelmingly dealt with immediately and are solved after a few turns at talk, e.g. in the turn which contains the trouble (a self-repair) or in the next turn (an other-repair) (Schegloff et al. 1977). In the present study we investigate cases of repair in interactions in which the co-participants are challenged in some way, partly due to an uneven distribution of language and communication skills. In interactions in which one of the participants speaks a foreign or second language or is communicatively, linguistically and/or cognitively impaired, repair sequences regularly are expanded beyond the simple format of a few turns. In other words, attempts at repairing are piled up in that several repair operations occur on the same trouble source. Furthermore, we observe that participants, as a result of the extended repair work, end up spelling out what they tried to convey from the onset. They make use of remedial resources such as loud, clear and slow speech, while reusing the (linguistic) materials that, either in the original trouble source turn (TST) or in the repair cycles have been unsuccessfully employed. Our study aims at describing both piling up and spelling out as practices across different types of challenged interaction. We discuss these repair operations in relation to the relevancy of the social categories hearing, speaking, or cognitive impaired person, as well as non-native
Simulation of a balanced low-voltage electrical grid using a simplified network model

A simulation method for low-voltage balanced distribution networks is proposed in this article. The novel method of node powers is based on the general calculation technique of node voltages. By researching only balanced networks, single-phase equivalents of the three-phase system are applicable. For the description of power lines, various parameters and matrices are available. In this work a simplified model is applied by using a purely resistive one. The active power results are solved through an iterative process. A main accomplishment is that the number of iterations needed is independent of the size of the network, and the process rapidly converges. Validation of the method is performed on the IEEE European Low-Voltage Test Feeder network. The simulation results confirm the achievements described in this paper.
Dynamics Of Atmospheric Flight

Thank you very much for reading dynamics of atmospheric flight. As you may know, people have look hundreds times for their chosen readings like this dynamics of atmospheric flight, but end up in harmful downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they juggled with some harmful virus inside their desktop computer. dynamics of atmospheric flight is available in our book collection an online access to it is set as public so you can download it instantly. Our digital library hosts in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the dynamics of atmospheric flight is universally compatible with any devices to read.
Automation and Optimisation of Humor Trait Generation in a Vocal Dialogue System

This study pertains to our ongoing work about social artiﬁcial vocal interactive agents and their adaptation to users. In this regard, several possibilities to introduce humorous productions in a spoken dialogue system are investigated in order to enhance naturalness during interactions between the agent and the user. Our goal is twofold: automation and optimisation of the humor trait generation process. In this regard, a reinforcement learning scheme is proposed allowing to optimise the usage of humor modules in accordance with user preferences. Some simulated experiments are carried out to conﬁrm that the trained policy used by the humor manager is able to converge to a predeﬁned user proﬁle. Then, some user trials are done to evaluate both the nature of the produced humor and its timely and proportionate usage.
Design consideration in low dropout voltage regulator for batteryless power management unit

Harvesting energy from ambient Radio Frequency (RF) source is a great deal toward batteryless Internet of Thing (IoT) System on Chip (SoC) application as green technology has become a future interest. However, the harvested energy is unregulated thus it is highly susceptible to noise and cannot be used efficiently. Therefore, a dedicated low noise and high Power Supply Ripple Rejection (PSRR) of Low Dropout (LDO) voltage regulator are needed in the later stages of system development to supply the desired load voltage. Detailed analysis of the noise and PSRR of an LDO is not sufficient. This work presents a design of LDO to generate a regulated output voltage of 1.8V from 3.3V input supply targeted for 120mA load application. The performance of LDO is evaluated and analyzed. The PSRR and noise in LDO have been investigated by applying a low-pass filter. The proposed design achieves the design specification through the simulation results by obtaining 90.85dB of open-loop gain, 76.39o of phase margin and 63.46dB of PSRR respectively. The post-layout simulation shows degradation of gain and maximum load current due to parasitic issue. The measurement of maximum load regulation is dropped to 96mA compared 140mA from post-layout. The proposed LDO is designed using 180nm Silterra CMOS process technology.
Towards the qualification of an AADL model transformation tool with contracts

Model-Based Engineering (MBE) can be used to build complex and critical systems. At the core of MBE, model transformation allows for the automatic processing of models to automatically generate code of the application or to perform analysis. In the context of safety-critical applications, qualification of MBE tools must provide evidence that the model transformation process is ``correct''. Its implementation must (i) document the transformation process itself, and support validation (ii) to infer properties on this process. We propose to use contracts to support qualification of the model transformation process. Contracts, by providing a formal specification of a model transformation, can be used in different ways, either to detect contract violations at run time or to demonstrate properties of the model transformation at the design time. This approach has been experimented in the context of Ocarina, an AADL toolset (Architecture Analysis and Design Language).
Accelerate proposal generation in R-CNN methods for fast pedestrian extraction

Purpose The purpose of this study is to develop a novel region-based convolutional neural networks (R-CNN) approach that is more efficient while at least as accurate as existing R-CNN methods. In this way, the proposed method, namely R2-CNN, provides a more powerful tool for pedestrian extraction for person re-identification, which involve a huge number of images and pedestrian needs to be extracted efficiently to meet the real-time requirement. Design/methodology/approach The proposed R2-CNN is tested on two types of data sets. The first one the USC Pedestrian Detection data set, which consists of three sub-sets USC-A, UCS-B and USC-C, with respect to their characteristics. This data set is used to test the performance of R2-CNN in the pedestrian extraction task. The speed and performance of the investigated algorithms were collected. The second data set is the PASCAL VOC 2007 data set, which is a common benchmark data set for object detection. This data set was used to analyze characteristics of R2-CNN in the case of general object detection task. Findings This study proposes a novel R-CNN method that is both more efficient and more accurate than existing methods. The method, when used as an object detector, would facilitate the data preprocessing stage of person re-identification. Originality/value The study proposes a novel approach for object detection, which shows advantages in both efficiency and accuracy for pedestrian detection task. It contributes to both data preprocessing for person re-identification and the research on deep learning.
Improving the deterministic reserve requirements method to mitigate wind uncertainty

P ower grids are subject to a variety of uncertainties that may expose them to potential safety issues. Interruptions in electricity supply for instance, may result from an unseasonable temperature fluctuations or a power station outage, which are events of stochastic nature involving the weather or the failure of a component in the grid. The result may be sudden imbalances in supply and demand, leading to load interruptions. To plan for such unforeseen events, the grid carries ’reserve’, i.e., additional capacity above that needed to meet actual demand. However, scheduling the appropriate amount of reserve needed for a reliable and cost-effective grid operation is very challenging, especially in the context of increased uncertainties due to liberalization and the large-scale wind electric generators (WEGs) penetration to grid. Traditional grids assume a fixed knowledge of system conditions for the next day. Wind power being very poor to predict, an extra reserve generation to accommodate its uncertainty is required. Because WEGs aren’t built around spinning turbines, conventional units have been left stressed while responding to large and fast variations in the system net load. Given the temporal operating restrictions that limit their flexibility, the properly functioning of the electricity market can be altered as the energy transactions may not be carried out in realtime, exactly as agreed for security reasons. In this context, the use of the deterministic criteria alone as is the case today, may not be economical or reliable in limiting the risk of uncertainty; calling for sophisticated methods based on more-complex characteristics of uncertainty. This thesis proposes reliable and sound solutions to the increased variability and uncertainty in short-term power grid operations emanating from increasing the share of WEGs in the generation mix and competition from electricity markets. The conservativeness of the deter-
The case of using negative (deceiving) information in data protection

In this paper we develop a novel taxonomy of methods and techniques that can be used to protect digital information. We explore complex relationships among these protection techniques grouped into four categories. We present analysis of these relationships and discuss how can they be applied at different scales within organizations. We map these protection techniques against the cyber kill-chain model and discuss some findings. Moreover, we identify the use of deceit as a useful protection technique that can significantly enhance the security of computer systems. We posit how the well-known Kerckhoffs’s principle has been misinterpreted to drive the security community away from deception-based mechanisms. We examine advantages these techniques can have when protecting our information in addition to traditional methods of denial and hardening. We show that by intelligently introducing deceit in information systems, we not only lead attackers astray, but also give organizations the ability to detect leakage; create doubt and uncertainty in leaked data; add risk at the adversaries’ side to using the leaked information; and significantly enhance our abilities to attribute adversaries. We discuss how to overcome some of the challenges that hinder the adoption of deception-based techniques.
Short Text Topic Discovery Based on BTM Topic Model

With the further development of the online social platform, the research techniques of hot topic related to short text data which are represented by Weibo, instant messaging, news commentary and so on, are not extensive enough, and the research efforts are not deep enough either. Moreover, short text data set has many characteristics such as high noise, sparsity, and irregular specification, which makes the performance of traditional topic research techniques insufficient. Therefore, for the data characteristics of short text, this paper uses a short text topic discovery method based on BTM (Bi-term Topic Model) theme model. Firstly, the BTM of the processed short book is modeled to meet the probability distribution of the subject obtained after the data language features of the essay are modeled. Then JS distance is used as the text similarity measure, combined with the improved Single-pass clustering algorithm to find out the hot topic of short text data set. The comparison experiments show that the short text modeling and improved single-pass algorithm use BTM making the clustering efficiency improved, and it can effectively solve the problem of data sparsity in short texts. There has been a remarkable improvement in the quality of the topic discovery. Introduction The short text on various social platforms is not only tremendous, but also the growth rate is extremely fierce. Individuals life, work and learning have been fully infiltrated by all kinds of information on the network, affecting the formation of individuals three views. Therefore, timely grasping the topics of concern to netizens will help the public quickly identify and track hot topics in the society, and take appropriate measures to guide them. The short text is simple and clear, most of which is less than two hundred words, and the data in the short text has a large data sparseness. Taking microblog short text data as an example, the context between texts is not large, and the number of words is small and refined. Commonly used topic models are Vector Space Model (VSM) and LDA (Latent Dirichlet Allocation) topic models, but these models are often applied to traditional long text, without considering the particularity of short text[1,2]. It is not suitable for microblog short text data, thus affecting the quality of microblog short text hotspot discovery. Combining with the shortcomings of short text modeling, the method of short text topic hotspot discovery based on Bit Topic Model (BTM) is adopted to solve the problem of data sparsity in short text hotspot discovery, so as to improve the quality of topic discovery. The method uses the BTM model to model the obtained short text, the topic discovery stage, the subject-vocabulary distribution and the probability distribution of the topic's distribution vector, and then uses the JS distance as the text similarity measure. Finally, the improved single-pass clustering algorithm for text topic discovery is used.
First in-depth analysis of enterprise architectures and models for higher education institutions

Enterprise Reference Architectures (ERAs) and Reference Models (RMs) have emerged over the last years as relevant instruments for improving the quality and effectiveness of enterprise architecture (EA) practice. Whilst a wide variety of different ERAs and RMs have been proposed for different industries and types of business, only few of them have been devoted to the Higher Education (HE) sector. In this paper, we propose an in-depth analysis process which we then critically apply to review, compare and classify 20 existing ERAs and RMs targeted to the HE domain. Our process uses a common set of 12 definitional attributes. In so doing, we contribute to the existing body of knowledge by providing a unified, structured and comprehensive analysis process and catalog of these abstract EA artifacts. With this we aim to create awareness on their potential practical utility and to increase their visibility, transparency and opportunity for their reusability by different HE stakeholders. Hence, the proposed process and catalog is expected to be useful both for practitioners and researchers by providing a panoramic view of more or less ready-to-use existing ERAs and RMs for HE, as well as a structure way to regard them. Moreover, and by specifying their main scope, coverage and extend of knowledge captured, the process and catalog might become a valuable tool for providing guidance to HE stakeholders on making better-informed decisions on the selection of suitable architectural artifacts for being conveniently adapted or applied in different EA practices conducted at their respective institutions.
Hedging in Academic Writing and

Academic writing is rich in hedged propositions. By allowing writers to express their uncertainty concerning the factuakty of their statements or to indicate deference to their readers, epistemic devices are a significant charac- teristic of academic writing. While there is clear pedagogical justification for assisting learners to develop an awareness of the significance of hedging and the principles of its correct use, tentative language continues to be an important source of pragmatic failure in the writing of second language science students. This paper discusses the importance, functions, and expression of epistemic modality in scientific discourse in order to evaluate the treatment given to hedging devices in a range of EAP and EST writing textbooks. It is suggested that despite the interest hedging has attracted in the research literature, a number of widely used textbooks display an ignorance of empirical usage, and that pedagogic writing materials would benefit from revisions based on authentic data.
FORHYCS v1.0: A spatially distributed model combining hydrology
and forest dynamics

Abstract. We present FORHYCS (FORests and HYdrology under Climate Change in Switzerland), a distributed ecohydrological model to assess the impact of climate change on water resources and forest dynamics. FORHYCS is based on the coupling of the hydrological model PREVAH and the forest landscape model TreeMig. In a coupled simulation, both original models are executed simultaneously and exchange information through shared variables. The simulated canopy structure is summarized by the leaf area index (LAI), which affects local water balance calculations. On the other hand, an annual drought index is obtained from daily simulated potential and actual transpiration. This drought index affects tree growth and mortality, as well as a species-specific tree height limitation. The effective rooting depth is simulated as a function of climate, soil and simulated above-ground vegetation structure. Other interface variables include stomatal resistance and leaf phenology. Case study simulations with the model were performed in the Navizence catchment in the Central Swiss Alps, with a sharp elevational gradient and climatic conditions ranging from dry inneralpine to high alpine. In a first experiment, the model was run for 500 years with different configurations. The results were compared against observations of vegetation properties from national forest inventories, remotely sensed LAI and high-resolution canopy height maps from stereo aerial images. Two new metrics are proposed for a quantitative comparison of observed and simulated canopy structure. In a second experiment, the model was run for 130 years under idealized climate change scenarios: daily temperature was increased by up to 6 K, and precipitation altered by 10 %, with a gradual change over 35 years. The first experiment showed that model configuration greatly influences simulated vegetation structure. In particular, simulations where height limitation was dependent on environmental stress showed a much better fit to canopy height observations. Spatial patterns of simulated LAI were more realistic than for uncoupled simulations of the forest landscape model, although some model deficiencies are still evident. Under idealized climate change scenarios, the effect of the coupling varied regionally, with the greatest effects on simulated streamflow (up to 40 mm y−1 difference with respect to a simulation with static vegetation parameters) seen at the valley bottom and in regions currently above the treeline. This case study shows the importance of coupling hydrology and vegetation dynamics to simulate the impact of climate change on ecosystems. Nevertheless, it also highlights some challenges of ecohydrological modelling, such as the need to realistically simulate plant response to increased CO2 concentrations, and process uncertainty regarding future land cover changes.
Hardware attack detection and prevention for chip security

Hardware security is a serious emerging concern in chip designs and applications. Due to the globalization of the semiconductor design and fabrication process, integrated circuits (ICs, a.k.a. chips) are becoming increasingly vulnerable to passive and active hardware attacks. Passive attacks on chips result in secret information leaking while active attacks cause IC malfunction and catastrophic system failures. This thesis focuses on detection and prevention methods against active attacks, in particular, hardware Trojan (HT). Existing HT detection methods have limited capability to detect small-scale HTs and are further challenged by the increased process variation. We propose to use differential Cascade Voltage Switch Logic (DCVSL) method to detect small HTs and achieve a success rate of 66% to 98%. This work also presents different fault tolerant methods to handle the active attacks on symmetric-key cipher SIMON, which is a recent lightweight cipher. Simulation results show that our Even Parity Code SIMON consumes less area and power than double modular redundancy SIMON and Reversed-SIMON, but yields a higher fault -detection-failure rate as the number of concurrent faults increases. In addition, the emerging technology, memristor, is explored to protect SIMON from passive attacks. Simulation results indicate that the memristor-based SIMON has a unique power characteristic that adds new challenges on secrete key extraction.