Miracle-dz/UniFAR-FTUs huggingface dataset:
Complexity, accuracy and fluency*

The purpose of this chapter is to investigate how complexity, accuracy and fluency interact in early L2 development, when learners’ linguistic means are underdeveloped. Learners then resort to rote-learned formulaic sequences to complement their current grammar when it is unable to meet their communicative needs. The interplay between their nascent grammar and these formulaic sequences provides an interesting window onto how learners resolve tensions between complexity, accuracy, fluency, and communicative needs.Formulaic sequences are very common in early L2 productions and enable learners to communicate in spite of limited linguistic means so that they appear to be more advanced in the L2 than they actually are, in terms of complexity, accuracy and fluency.In spite of their prevalence in early productions, however, the role that these formulaic sequences play in L2 development remains unclear. Are they used as communicative crutches until learners’ grammatical competence enables them to generate these forms productively or do they contribute more directly to learners’ linguistic development? This chapter reports on an empirical study tracing and analysing the development of such sequences over time in the early L2 productions of instructed learners of French. It is argued that these sequences gradually become unpacked during the acquisition process, and are used as models by learners in order to assist them in the construction of a productive L2 grammar. The tension between, on the one hand, complex, accurate and fluent formulaic sequences, but whose internal structure has not yet been analysed into its constituents in order to use them productively elsewhere, and on the other hand, an underdeveloped linguistic system which does not allow communicative needs to be met, seems to be driving the acquisition process forward in these learners. The linguistic status of these sequences seems to be that of single multimorphemic lexical units which have been assigned a semantic representation but are underspecified syntactically. Their intrinsic morphosyntactic complexity, therefore, and the fact that they can be accessed as single units rather than effortfully constructed from scratch in real time communication, gives the misleading impression of complex, accurate and fluent productions at a stage when learners’ productive grammars are in fact very simple, approximate and non-fluent.
Communication with chaos via DSP implementation

In this paper, we propose a practical system of private communication with chaos based on DSP implementation. Many methods that assume analog circuits have been proposed in this field. However, it seems difficult to maintain both the transmitter and receiver with very high accuracy. Therefore, we try to use digital signal processor (DSP) for modulation and demodulation systems by assuming the digital transmission line such as local area networks (LAN). Moreover, since DSP is one of single-chip computers which use finite length variables in computation, it is necessary to consider the overflow problem and computation error problem. In this paper, we try to investigate these problems in practical applications, and propose a new algorithm that has perfect recovery characteristics out of transmitted data.
Designing Low Dropout Regulator with Low Settling Time, High Power Supply Rejection and Low Line and Load Regulation

Low dropout regulators are one of the most important factures of many portable devices. Thus, consider to the complexity of the circuits and increasing request for portable devices, for increasing battery life and minimizing supply noise, regulators with high efficiency, low output noise and small size is required. In these paper two methods to improve the efficiency of LDO regulators is proposed. First method is increasing gain of error amplifier by using cascode technique, to improve steady-state specification. Second method is using a simple subtractor circuit between error amplifier and pass transistor of LDO regulator to improve power supply rejection, slew-rate and steady-state specification. In addition, both methods are used to achieve area efficiency replacing MIM capacitors with MOS transistor. These low dropout regulators has been simulated in TSMC 0. 18μm CMOS process. Simulation results show enhancement settling time, good line and load regulation and power supply compare with others LDO regulators.
A Page Token Prototype of OpenID Single Sign-On (SSO) to Thwart Phishing Attack

Single Sign-on (SSO) authentication was introduced to overcome the problem of password memorability issue by enabling the users to login once using a set of username and password that allows an access into multiple websites. Among several SSO protocol, OpenID is said to offer flexibility and security. Unfortunately, the existing OpenID model is prone to phishing attack due to lack of countermeasures to ensure authenticity of OpenID provider. In view of the proliferation of phishing attack that exposed users to fraud website, information theft and unauthorized disclosure, this study attempts to identify and propose a suitable countermeasure in order to thwart phishing attack in OpenID environment. Therefore, this study intends to develop a prototype that implements Page Token in order to mitigate phishing attack. The findings revealed that the Page Token is possible to minimize the potential risk of phishing attack.
Application of spatial reasoning methods to the extraction of roads from high resolution satellite imagery

By the end of this decade the civilian remote sensing community will have access to timely satellite imagery at high spatial resolution (1-3 meters). This will open the door for new applications, such as detailed topographic mapping. These data can be considered to be in a 'transition' spatial resolution regime between conventional, low resolution satellite imagery and aerial photography. Correspondingly, information extraction methodologies to exploit these data must draw on both the data-driven, per-pixel processing of conventional satellite image analysis and the object-driven, image understanding technologies now being developed for aerial photography. This paper describes experiments to evaluate rule-based recognition algorithms for the purpose of automating planimetric feature extraction from these new satellite data. The overall feature extraction strategy is one drawn from image understanding namely spatial reasoning with a segmented rendition of the image. Recognition involves applying a set of evidence-accumulation attribute (inherent spatial/spectral and context) tests to selected segments in order to identify candidates which may form all or part of an object of interest. Conventional classification 'training' has been modifed to develop a novel approach to evidence weight quantification and to assess inter-test correlation, analogous to conventional covariance. A recognition system has been developed to recognize residential streets in imagery.
Human behavior under concurrent schedules: choice as a matter of procedure

Este estudo procurou investigar o comportamento humano em esquemas concorrentes de intervalo variavel a partir da manipulacao de variaveis outras que nao o comportamento verbal. Quatro universitarios foram expostos a cinco condicoes com pares de esquemas concorrentes (VI VI.Utilizou-se um microcomputador com tela sensivel para apresentacao de estimulos e registro de respostas. A cada cinco reforcos, a sessao era interrompida, e os alunos relatavam como ganhavam os pontos. Os resultados demonstram que a distribuicao de tempo e respostas estava em acordo com as contingencias programadas, embora nenhum participante tenha conseguido descrever a regra. A literatura experimental relata que o comportamento de humanos em esquemas concorrentes em geral nao apresenta igualacao na distribuicao de respostas. Atribui-se este resultado ao comportamento verbal dos sujeitos como variavel diferenciadora do responder de animais humanos e nao-humanos. Verifica-se neste estudo que a igualacao nao esta relacionada a formulacao de regras mas a uma questao de procedimento experimental. Palavras-chave: igualacao em humanos; esquemas concorrentes; escolha; comportamento governado por regras. This research aimed to investigate human behavior under concurrent variable interval, variable interval schedules of reinforcement manipulating variables other than verbal behavior. Four undergraduate students were exposed to five conditions with different concurrent pairs. A PC computer with touch screen programmed sessions and recorded responses. Every five reinforcers the session stopped, and the participants had to report how to win points. The results showed that time and response distributions were in accordance to contingencies, although none of the participants described the rule. Although experimental literature have affirmed that human behavior in concurrent schedules does not show matching in response distribution due to subjects verbal behavior as the critical variable in differences between humans and non-humans, the present study showed that matching is a matter of procedure, not due to rule formulation.
Investigating the Impact of Video Instruction in a High School Chemistry Class

High school chemistry students struggle with the unit on moles due to their lack of algebraic skills and understanding of dimensional analysis. Moles is typically taught using the traditional lecture model, however, change was needed to teach the current generation of students. Therefore, the purpose of this action research project was to investigate the impact of integrating video instruction to increase learning and engagement for high school chemistry students studying a unit on moles. Through constructivist ideals, an intervention was designed where students watched a short video at the beginning of each class and collaborated on practice problems, allowing students to be actively engaged and take ownership of their learning. At the end of each class, students completed an exit ticket to check for understanding to determine the effectiveness of the video instruction. Upon completion of the unit, students took a summative assessment and completed a post-survey to assess learning and engagement. This project was important to show how video instruction can affect learning, engagement, and retention with students. Based on feedback from students, my next steps are to convert my traditional lectures into video instruction in my biology and chemistry classes. In order to increase engagement, learning, and retention, I need to continue to adjust my teaching in order to support the current generation of learners.
Application of machine learning algorithms to mineral prospectivity mapping

In the modern era of diminishing returns on fixed exploration budgets, challenging targets, and ever-increasing numbers of multi-parameter datasets, proper management and integration of available data is a crucial component of any mineral exploration program. Machine learning algorithms have successfully been used for years by the technology sector to accomplish just this task on their databases, and recent developments aim at appropriating these successes to the field of mineral exploration. Framing the exploration task as a supervised learning problem, the geological, geochemical and geophysical information can be used as training data, and known mineral occurences can be used as training labels. The goal is to parameterize the complex relationships between the data and the labels such that mineral potential can be estimated in under-explored regions using available geoscience data. Numerous models and algorithms have been attempted for mineral prospectivity mapping in the past, and in this thesis we propose two new approaches. The first is a modified support vector machine algorithm which incorporates uncertainties on both the data and the labels. Due to the nature of geoscience data and the characteristics of the mineral prospectivity mapping problem, uncertainties are known to be very important. The algorithm is demonstrated on a synthetic dataset to highlight this importance, and then used to generate a prospectivity map for copper-gold porphyry targets in central British Columbia using the QUEST dataset as a case study. The second approach, convolutional neural networks, was selected due to its inherent sensitivity to spatial patterns. Though neural networks have been used for
Eliciting, Analyzing and Modeling Software Security Requirements

Developing secure software is elementary today. This is attributed to the increasing flow of sensitive information, attack attempts and numerous interactions of the software with variety of users. The main theme of the methodology proposed in this paper is the popular view among the researchers of assimilating security within the process of development and not after the development is over. The proposed methodology involves eliciting views of all possible stakeholders over certain security parameters to rank the assets and consequently, the functionalities of the system followed by empirical analysis and developing rank-based diagrams.
Volume reduction consideration in multilevel DC-DC boost converter

This paper discusses the design estimation of an inductor in the 2-level and 4-level DC-DC boost converter for the inductance and volume reductions based on maximum product of the inductor voltage and inductor charging time. Two multilevel DC-DC boost converters were compared in term of inductors design, i.e., 2-level and 4-level DC-DC boost converter. The finding shows that the required inductance and inductor volume of the 4-level DC-DC boost converter are approximately 11.11% and 19.25%, respectively compared to the 2-level DC-DC boost converter. The simulation results show that the inductor current ripple was 0.55 A when the inductors 200 μH and 1800 μH were used in the 2-level and 4-level DC-DC boost converters, respectively. Thus reductions of the inductance and the inductor volume were achieved in the 4-level DC-DC converter.
Implementing a Sparse Matrix Vector Product for the SELL-C/SELL-C-sigma formats on NVIDIA GPUs

Numerical methods in sparse linear algebra typically rely on a fast and efficient matrix vector product, as this usually is the backbone of iterative algorithms for solving eigenvalue problems or linear systems. Against the background of a large diversity in the characteristics of high performance computer architectures, it is a challenge to derive a cross-platform efficient storage format along with fast matrix vector kernels. Recently, attention focused on the SELL-C-σ format, a sliced ELLPACK format enhanced by row-sorting to reduce the fill in when padding rows with zeros. In this paper we propose an additional modification resulting in the padded sliced ELLPACK (SELLP) format, for which we develop a sparse matrix vector CUDA kernel that is able to efficiently exploit the computing power of NVIDIA GPUs. We show that the kernel we developed outperforms straight-forward implementations for the widespread CSR and ELLPACK formats, and is highly competitive to the implementations in the highly optimized CUSPARSE library.
Composable simulations

While there has been much attention paid to the applications of modeling and simulation (M&S) by the Department of Defense (DoD) lately, little has been done to address those broad technology areas that enable application-oriented simulations to be more easily constructed, run and analyzed. Model composabilty is the ability to compose models/modules across a variety of application domains, levels of resolution and time scales. A composability framework for simulations offers a quantum leap in capability and provides the sought after ease of use. However, composability is still a frontier subject in modeling and simulation, and current capability is limited. While model reuse is close to the Holy Grail of M&S, the goal is not within sight. We are discovering that unless models are designed to work together, they don't (at least not easily and cost effectively). Without a robust, theoretically grounded framework for design, we are consigned to repeat this problem for the foreseeable future. This paper outlines the challenges and basic research that needed for composable simulation developments.
Survey on Outlier Detection for Support Vector Machine

- Outlier is the data object which does not comply with the general behaviour or model of data. Which are grossly different from entire set of data. From large data set detecting outliers present different challenge resulting from curse of dimensionality. As the data size is double every year, there is a need to detect outlier in large datasets as early as possible. If there are lot of outliers in data set there might de misclassification of data and outlier data might be classified as normal data. More contrasting outlier score gives by SVM in high dimensional data in which training the data set is relatively easy. SVM mainly focusing on high dimensionality of data, this method will be allowed to use a training data set to train the classifier while detecting outliers from high dimensional data.
A Contrast Between Systematic and Automated Sentiment Analysis

Sentiment analysis mainly focuses on subjectivity and polarity detection. Today consumer makes buying decision based on the customer’s review that is available in each of the online shopping sites. There are some of the specific websites which discuss about positive and negative facts of those products that comes to market. Hence this type of analysis are socially very needed for sellers to undergo market analysis, branding, product penetration, market segmentation and so on. This paper mainly focuses on difference between systematic and automated methods of determining the positive and negative polarity distribution of Samsung Tablet PC.
Design of LOG/LTR controller: application to a turbofan engine

The approach of G. Stein and M. Athans (1987) for the solution of a specific H/sub 2/ optimization problem by LQG/LTR (linear quadratic Gaussian/loop transfer recovery) is revisited. A colored measurement noise whose spectrum only depends on the weighting filter of the H/sub 2/ cost function is introduced in the plant model. A LQG/LTR controller is then designed for this augmented plant model and it is shown that this controller solves the H/sub 2/ optimization problem for the original plant model. The proposed methodology is then applied to design a LQG/LTR controller for the turbofan engine considered by S. Garg (1989). Contrary to the method of Garg, no plant scaling is necessary to meet the design specifications. Good stability margins are obtained at the physical interface of the plant, while this could not be achieved in the work of Garg. <<ETX>>
Design of 1.0V O2 and H2O2 based Potentiostat

In this paper, a unified potentiostat which can measure the current of both O2-based and H2O2-based blood glucose sensors with low supply voltage of 1.0V has been designed and verified by simulations and measurements. Potentiostat is composed of low-voltage operational transconductance amplifier, cascode current mirrors and mode-selection circuits. It can measure currents of blood glucose chemical reactions occurred by O2 or H2O2. The body of PMOS input differentional stage of the operational transconductance amplifier is forward-biased to reduce the threshold voltage for low supply voltage operation. Also, cascode current mirror is used to reduce current measurement error generated by channel length modulation effects. The proposed low-voltage potentiostat is designed and simulated using Cadence SPECTRE and fabricated in Magnachip 0.18um CMOS technology with chip size of 110μm×60μm. The measurement results show that consumption current is maximum 46μA at supply voltage of 1.0V. Using the persian potassium(K3Fe(CN)6) equivalent to glucose, the operation of the fabricated potentiostat was confirmed. 키워드 : 정전압분극장치, 혈당측정센서, 연산 트랜스컨덕턴스 증폭기, 캐스코드 전류거울 Key word : Potentiostat, Glucose Sensor, Operational Transconductance Amplifier, Cascode Current Mirror Received 04 October 2016, Revised 24 October 2016, Accepted 30 October 2016 * Corresponding Author Yeong-Seuk Kim(E-mail:kimys@cbnu.ac.kr Tel:+82-43-261-3137) Department of Semiconductor Engineering, Chungbuk National University, Cheongju 28644, Korea Open Access http://doi.org/10.6109/jkiice.2017. 21.2. 345 print ISSN: 2234-4772 online ISSN: 2288-4165 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License(http://creativecommons.org/li-censes/ by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Copyright C The Korea Institute of Information and Communication Engineering. Journal of the Korea Institute of Information and Communication Engineering 한국정보통신학회논문지(J. Korea Inst. Inf. Commun. Eng.) Vol. 21, No. 2 : 345~352 Feb. 2017
Free, prior and informed consent: Implications for sustainable forest management in the Congo Basin

This paper examines changes that are occurring in t he way local forest populations, particularly Pygmy hunter-gatherers, are consulted an involved in the management of forest concessions in the Congo Basin. Demand for timber c ertified by the Forest Stewardship Council (FSC) is growing and some forest companies operating in the Congo Basin wish to achieve the high standards of FSC forest management to benefit from the market opportunities opened up to FSC certified timber. Th rough this process, forest companies are becoming answerable not just to individual states’ code forestier but to supra-national bodies with international standards of sustainable forest management (which generally encapsulate and surpass national ones). Principle 2 and 3 of the FSC standard demand the ga inin of Free, Prior and Informed Consent (FPIC) for exploitation. Theoretically, FPI C means that communities living in the concessions should be fully informed about intended forestry activities and freely give their consent before any of these activities begin. The a im is to leave the local population room to refuse the company’s exploitation or to negotiate w ith it on the management of forest resources. While this may seem clear on paper, in practice a l ot of problems may arise many of which are specific to the local social context. How do yo u achieve equal participation and free consent from indigenous hunter-gatherers who are po litically marginalised by their farmer neighbours? Can it be said that consent is freely g iven when for local populations agreeing to logging activities is their only means of obtaining education, healthcare and basic infrastructure? How do you ensure complete consulta tion among a mobile hunter-gatherer population? Moreover, can FSC auditors prove this h as been achieved? The paper summarizes preliminary results from a fea sibility study conducted in five major forest concessions in DRC, RDC and Gabon. By lookin g at the possibilities offered by FPIC and the difficulties surrounding its implementation , this paper will evaluate if and under which conditions these FSC criteria could give loca populations control over forest use and improve their living conditions. The paper seeks to pr voke debate on whether the universal theoretical concept of FPIC can respond to specific local human needs and situations.
Quantum neuromorphic hardware for quantum artificial intelligence

The development of machine learning methods based on deep learning boosted the field of artificial intelligence towards unprecedented achievements and application in several fields. Such prominent results were made in parallel with the first successful demonstrations of fault tolerant hardware for quantum information processing. To which extent deep learning can take advantage of the existence of a hardware based on qubits behaving as a universal quantum computer is an open question under investigation. Here I review the convergence between the two fields towards implementation of advanced quantum algorithms, including quantum deep learning.