Miracle-dz/UniFAR-FTUs huggingface dataset:
(Non)Native English-Speaking Teachers in Pronunciation Teaching: A Case Study Through the Lens of English as an International Language

Although Turkey is a country where English is taught as an international language (EIL), very few research studies have focused specifically on learners’ attitudes toward different accents and pronunciations. Therefore, this paper attempts to highlight Turkish university preparatory school students’ perceptions on different English pronunciations and accents and on native and non-native speakers’ and teachers’ English pronunciation. It also aims to point out the in-class/learning environment factors that impact pronunciation learning in English classes. Based on a review of the literature analyzing attitudes towards different pronunciation and accents, online surveys and focus group interviews with 10 volunteers were conducted to collect data for this study. The survey results revealed that a great number of participants believe that correct pronunciation is crucial in communication. Also, all the inclass/learning environment factors had significant impact on the participants’ perceived pronunciation. Interview results indicated that most learners agreed that as long as a pronunciation is intelligible, it can be considered as good. In addition, despite admitting the positive effect of native English-speaking teachers (NESTs) on their pronunciation, most participants did not ignore the positive influence of non-native English-speaking teachers (NNESTs) on their pronunciation improvement. Because most participants indicated that they wanted to receive more pronunciation-focused instruction, this study has some implications for the institutions and material designers that may reconsider the weighing and variety of pronunciation practice when compared to other language skills.
Ski Resorts Recommendation using Deep Neural Networks

In this work, we present a recommender system based on deep neural networks for a novel application that predicts suitable ski resorts for a web agency customers. A special focus is paid to the features construction where they are created from historical heterogeneous data according to a user-friendly questionnaire. The evaluation is performed through the accuracy metric and the obtained results show a promising performance with a TOP-10 accuracy that reaches 98.95% and 99.30% for French and non-French customers, respectively. CCS Concepts: • Computing methodologies → Artificial intelligence ; Machine learning approaches .
Updates on ACE

ACE is a permutation based authenticated encryption (AE) and hash algorithm which provides 128-bit security for AE and hash with a single hardware circuit. In this note, we report updates on ACE since its selection as a round 2 candidate of the NIST lightweight cryptography standardization competition [3]. This report includes: new third party cryptanalysis and implementation results, applications of ACE in IEEE 802.11X and CoAP handshake protocols for Internet of Things, and comparisons with current NIST standards. We do not plan any future tweaks for ACE .
A NOVEL FUZZY CORNER APPROACH FOR BRAIN TUMOR SEGMENTATION AND CLASSIFICATION

: Abnormal development of cells in human body contributes to the formation of cancer or tumor. The uncontrolled expansion of cells in human brain leads to the formation of brain tumors. These brain tumors are classified into two types, benign and malignant. This project presents an automatic segmentation method based on fuzzy corner metric markers and classification method using Convolutional Neural Network. The segmentation method uses a fuzzified corner metric, in view of image intensity, is proposed to recognize the component markers to be enclosed by the contour. In order to improve the accuracy level, Whale Optimization Algorithm (WOA) is used. The WOA is used to tune the hyper parameters of hybrid CNN. These parameters include the number of convolution kernels, the size of convolution kernels, activation function, batch size and learning rate. CNN models, one of the deep learning networks, are utilized for the diagnosis process. The proposed work is implemented and simulated in MATLAB. The proposed work gives exceptionally good results, when compared to recently proposed techniques and this work provide better accuracy of 98.92%, specificity of 97%, and sensitivity of 97%.
The Effectiveness of Adaptation Methods in Improving User Engagement and Privacy Protection on Social Network Sites

Abstract Research finds that the users of Social Networking Sites (SNSs) often fail to comprehensively engage with the plethora of available privacy features— arguably due to their sheer number and the fact that they are often hidden from sight. As different users are likely interested in engaging with different subsets of privacy features, an SNS could improve privacy management practices by adapting its interface in a way that proactively assists, guides, or prompts users to engage with the subset of privacy features they are most likely to benefit from. Whereas recent work presents algorithmic implementations of such privacy adaptation methods, this study investigates the optimal user interface mechanism to present such adaptations. In particular, we tested three proposed “adaptation methods” (automation, suggestions, highlights) in an online between-subjects user experiment in which 406 participants used a carefully controlled SNS prototype. We systematically evaluate the effect of these adaptation methods on participants’ engagement with the privacy features, their tendency to set stricter settings (protection), and their subjective evaluation of the assigned adaptation method. We find that the automation of privacy features afforded users the most privacy protection, while giving privacy suggestions caused the highest level of engagement with the features and the highest subjective ratings (as long as awkward suggestions are avoided). We discuss the practical implications of these findings in the effectiveness of adaptations improving user awareness of, and engagement with, privacy features on social media.
Effects Of Economic Condition And Inflation On Business Performance Of Small And Medium Enterprise In Thailand

There are several factors effecting on business performance. Along with the other factors, the factors at global level has major influence which may include; inflation and economic conditions. Therefore, the objective of this study is to examine the effect of inflation and economic conditions on business performance. In this study, the business performance of small and medium-sized enterprises (SMEs) is considered. Thus, the population of this study is the SMEs of Thailand. By using cross-sectional research design, data were collected from the managers of these SMEs. Primary data were collected by using survey questionnaire and analyzed by using SmartPLS. Results of the study shows that both the inflation and economic conditions has critical role in SMEs business performance. Economic conditions have major effect on inflation which further lead to the price of commodities and business performance. Therefore, both the factors have central importance to increase and decrease the business performance of SMEs in Thailand. Hence, the current study has key insights for the practitioners to make strategies for business performance.
Evolutionary decarbonization of transport: a contiguous roadmap to affordable mobility using sustainable organic fuels for transport

The paper discusses the use of methanol as a key enabler of the pragmatic, affordable and utter decarbonization of transportation. The ease with which it can be synthesized from carbonaceous feed stocks provides a contiguous route to a transportation system which can ultimately eliminate fossil-sourced CO 2. The recycling of water and CO2 to provide the hydrogen, carbon and oxygen feed stocks required has the potential to provide full energy security and to ensure the continued affordability of personal transportation. Simultaneously, the use of this methanol in methanol-to-gasoline, -diesel and kerosene processes to synthesize drop-in fuels that can fully decarbonize all forms of transport without the need for the vehicles to change significantly is shown. It is pointed out that this is the only practical means to fully decarbonize aviation. The paper is divided into several parts. Firstly a background to the desirability of methanol as a liquid transport fuel is provided, followed by a discussion of the process employed in a self-contained demonstration plant which uses air-captured CO2 (and hydrogen obtained from water electrolysis) to produce methanol and gasoline as carbon-neutral electrofuels. How methanol can gradually be introduced into current-technology and widely-affordable flex-fuel vehicles is then discussed. Some results from engines operating on pure methanol are presented, which illustrate that such engines can have higher thermal efficiencies than diesel engines. How the taxation system can be used to assist attainment of the decarbonization of transport, while still maintaining overall taxation revenue, is then discussed. Finally, a roadmap to the eventual decarbonization of all transport sectors via higher blend rates of methanol in gasoline is then outlined, eventually bringing pure methanol to forecourts for use in engines and fuel cells. © The author(s) and/or their employer(s), 2012.
SCOTOPIC VISUAL RECOGNITION Sequential Reasoning without the Probabilistic Model

Our second project is scotopic visual recognition, which aims to recognize objects with as little light as possible. This project is motivated by real-world applications ranging from biological imaging to astrophysics. Unlike visual search (Ch. 3), most practical vision applications do not have the luxury of knowing the full probabilistic model for the task at hand. To circumvent this problem we proposed techniques to train a sequential algorithm directly to optimize the speed versus accuracy tradeoff (SAT).
IkBz is a key player in the antipsoriatic effects of secukinumab

Background: IkBz plays a key role in psoriasis by mediating IL-17A–driven effects, but the molecular mechanism by which IL-17A regulates IkBz expression is not clarified. Objective: We sought to explore the molecular transformation in patients with psoriasis during anti-IL-17A (secukinumab) treatment with a focus on IkBz. Methods: The study was an open-label, single-arm, single-center secukinumab treatment study that included 14 patients with plaque psoriasis. Skin biopsy specimens and blood samples were collected on days 0, 4, 14, 42, and 84 and processed for microarray gene expression analysis. Furthermore, in vitro experiments with human keratinocytes and synovial fibroblasts were conducted. Results: Secukinumab improved clinical scores and histologic psoriasis features. Moreover, secukinumab altered the skin transcriptome. The major transcriptional shift appeared between day 14 and day 42 after treatment initiation, although 80 genes were differentially expressed already at day 4. Expression of nuclear factor of kappa light polypeptide gene enhancer in B cells inhibitor (IkB) z (NFKBIZ, the gene encoding IkBz) was reduced already after 4 days of treatment in the skin. NFKBIZ expression correlated to Psoriasis Area and Severity Index score, and NFKBIZ mRNA levels in the skin decreased during anti–IL-17A treatment. Moreover, specific NFKBIZ signature genes were significantly altered during anti– IL-17A treatment. Finally, we identified NF-kB activator 1 (Act1), p38 mitogen-activated protein kinase (MAPK), Jun NH2-terminal kinase (JNK), and nuclear factor kappa-light-
A new single-phase active power filter with reduced energy-storage capacity

The paper presents an active power filter (APF) circuit which employs a new control method, using an integration and sampling technique, to simplify the calculation algorithm for the real fundamental component of load current. In addition, a new simple control scheme, based on the energy balance concept, is proposed to control the voltage of an energy-storage capacitor. Since the energy change in the energy-storage capacitor can be compensated in the next cycle, and a larger DC bus voltage ripple can be tolerated if a sampling technique is used, a relatively smaller energy storage capacitor is required. The advantages of this APF circuit are simplicity of control circuits, low cost (a smaller energy-storage capacitor) and good transient response. In theory, the time delay for the compensation of reactive power and harmonic currents is zero. The feasibility of this theory is veriffied by using a PSPICE simulation and experimental results.
NBER WORKING PAPER SERIES EXPOSURES AND BEHAVIORAL RESPONSES TO WILDFIRE SMOKE

The impacts of environmental change on human outcomes often depend on local exposures and behavioral responses that are challenging to observe with traditional administrative or sensor data. We show how data from private pollution sensors, cell phones, social media posts, and internet search activity yield new insights on exposures and behavioral responses during large wildfire smoke events across the US, a rapidly-growing environmental stressor. Health-protective behavior, mobility, and sentiment all respond to increasing ambient wildfire smoke concentrations, but responses differ by income. Indoor pollution monitors provide starkly different estimates of likely personal exposure during smoke events than would be inferred from traditional ambient outdoor sensors, with similar outdoor pollution levels generating >20x differences in average indoor PM2.5 concentrations. Our results suggest that the current policy reliance on self protection to mitigate health risks in the face of rising smoke exposure will result in modest and unequal benefits. Marshall Burke Department of Earth System Science Stanford University Stanford, CA 94305 and NBER mburke@stanford.edu Sam Heft-Neal Center on Food Security and the Environment Stanford University 473 Via Ortega Stanford, CA 94305 sheftneal@stanford.edu Jessica Li Center on Food Security and the Environment Stanford University 473 Via Ortega Stanford, CA 94305 jbli@stanford.edu Anne Driscoll Stanford University 616 Serra St Stanford, CA 94305 annedriscoll@cmu.edu Patrick W. Baylis Vancouver School of Economics University of British Columbia 6000 Iona Drive Vancouver, BC V6T1L4 Canada patrick.baylis@ubc.ca Matthieu Stigler Stanford University Y2E2 Building Stanford, CA 94305 mstigler@stanford.edu Joakim Weill Department of Agricultural and Resource Economics University of California, Davis One Shields Ave Davis, CA 95616 jweill@ucdavis.edu Jennifer Burney University of California San Diego 9500 Gilman Dr. #0519 La Jolla, CA 92093 jburney@ucsd.edu Jeff Wen Department of Earth System Science Stanford University 473 Via Ortega Stanford, CA 94305 jlwen@stanford.edu Marissa Childs Stanford University marissac@stanford.edu Carlos Gould Department of Earth System Science Stanford University 473 Via Ortega Stanford, CA 94305 cfgould@stanford.edu
2.5.4 Modeling Objectives on Cost: Insight for Practitioners and Academicians

Following a previous paper entitled Modeling Objectives on Cost in a Decision Maker's Value Structure, the authors present the results of survey research conducted to determine whether or not the objective of minimizing cost should be included as part of the fundamental objectives hierarchy when modeling a decision maker's value structure. Conclusions are drawn for both practitioners and academicians. For practitioners, the discussion addresses which technique those who know and use decision analysis prefer and why. Suggestions for using the two competing techniques both separately and together are offered. For academicians, as a by‐product of the surveys, survey results illuminate how much and what kind of decision analysis training and education is received by systems engineers, operations researchers, engineering managers, decision analysts, and others. Standard contingency table hypothesis testing is used to validate certain points.
Forthcoming in American Economic Review. CONSTRAINED PSEUDO-MARKET EQUILIBRIUM

We propose a pseudo-market solution to resource allocation problems subject to constraints. Our treatment of constraints is general: including bi-hierarchical constraints due to considerations of diversity in school choice, or scheduling in course allocation; and other forms of constraints needed to model, for example, the market for roommates, combinatorial assignment problems, and knapsack constraints. Constraints give rise to pecuniary externalities, which are internalized via prices. Agents pay to the extent that their purchases affect the value the of relevant constraints at equilibrium prices. The result is a constrainedefficient market-equilibrium outcome. The outcome is fair to the extent that constraints treat agents symmetrically. (Echenique) Division of the Humanities and Social Sciences, California Institute
Mining Flow of Information Among User Networks in Social Networking Services

: Social networking services is a web based platform which is used by the people to evolve social networks or social relationships with each other who share similar profession or individual interests, activities, events, ideas or real life connections. The popularity of SNS is emerging rapidly and has become a huge date source to analyze social networks. The widespread content by users is very important in social networking services. In this, I proposed a method for finding information diffusion models for the data in social networking sites which can be obtained by using process mining techniques . For this, first we filer the information by expelling inconsistent users. Further, to decrease the complexity nature of SNS filtered information a user-centric clustering approach accomplished utilizing a novel measure called user intimacy value that quantifies the degree of relationship between users ,depending on modularity maximization strategy is carried out. This clustered information is converted to an event log which is utilized as input for process discovery algorithm Infoflow miner. The Infoflow miner utilizes response weight, that can be characterized as, the degree of impact or effect that one user activities have on other user activities. Final outcome of the technique is a graph which represents the flow of information among user networks present in the original information.
Dual-wavelength laser generation using highly concentrated erbium-doped fibre coupling with microfibre knot resonator

A dual-wavelength fibre laser is demonstrated using a 2 m-long erbium-doped fibre (EDF) with a microfibre knot resonator (MKR) structure at the output end. The MKR is fabricated by heating and stretching an EDF to form a microfibre with diameter of around 2 m, which is then manipulated to create a knot. At a loop diameter of 2 mm for the MKR, dual-wavelength laser is obtained at 1533.8 and 1555.4 nm with peak powers of 28.1 and 27.1 dBm, respectively. Both laser output is stable with a signal-to-noise ratio of more than 18.5 dB at room temperature.
Who Belongs in School? Using Statistical Learning Techniques to Identify Linear, Nonlinear and Interactive Effects

The sense of school belonging refers to students’ feelings of being accepted and connected to their particular school. School belonging has been considered an important determinant of a range of academic and socioemotional outcomes. Yet despite an extensive literature on the topic, it is not clear what factors are more strongly related to the students’ sense of school belonging. Using a nationally representative dataset, we investigated the extent to which school belonging in fifth grade can be predicted by a wide range of individual and contextual-level factors using two statistical learning techniques (Lasso andMARS). The strongest predictor of school belonging across all models was students’ feelings of peer social support, followed by students’ feelings of loneliness at school. These results suggest that peer social relationships are a key component of students feeling of being connected to their school.
Analysis of COVID-19 Misinformation in Social Media using Transfer Learning

Most major events are often accompanied by misinformation on online Social Networking platforms. Due to its nature, the COVID-19 pandemic was bound to lead to an explosion of information online, much of it false or misleading. This information explosion, termed "infodemic" by the World Health Organization (WHO), has revealed the need for automatic fake news detection to help with the exponentially growing flow of unverified information. The objective of this study is to explore combinations of different supervised classification models trained on different general and domain-specific embeddings, and compare the effects of the iterations on the results. We also analyze the results to determine whether the differences in weighted F1-score performance metrics are statistically significant. Ultimately, we demonstrate that concatenation of general and context-specific embeddings improves performance. Our research shows promise for health misinformation detection and formulation of effective public health responses.
Automated Theorem Proving in the Classroom

We report on several scenarios of using automated theorem proving software in university education. In particular, we focus on using the Theorema system in a software-enhanced logic-course for students in computer science or artificial intelligence. The purpose of using logic-software in our teaching is not to teach students the proper use of a particular piece of software. In contrast, we try to employ certain software in order to spark students' motivation and to support their understanding of logic principles they are supposed to understand after having passed the course. In a sense, we try to let the software act as a logic-tutor, the software is not an additional subject we teach.
An Online SMC-Based Adaptive Wavelet-Neural Controller for Uncertain Nonlinear Systems with only Output Measurement

This paper addresses the SMC-based adaptive wavelet-neural tracking control problem for a class of uncertain nonlinear systems. The adaptive wavelet-neural controller is designed under constrain that not all atate variables are available for measurement. A state observer is used to estimate unmeasured states of the systems. The global asymptotic stability of the closed-loop system is guaranteed according to the Lyapunov stability criterion. The simulation presented in the inverted pendulum system control indicates that the proposed approach is capable of achieving a good trajectory following performance without the knowledge of plant parameters.
Automatic Segmentation of Archaeological Fragments with Relief Patterns using Convolutional Neural Networks

The recent commodiﬁcation of high-quality 3D scanners is leading to the possibility of capturing models of archaeological ﬁnds and automatically recognizing their surface reliefs. We present our advancements in this ﬁeld using Convolutional Neural Networks (CNNs) to segment and classify the region around a vertex in a robust way. The network is trained with high-resolution views of the 3D models captured at different angles. The views represent both the model with its original textures and a colorization of the patches according to the value of the Shape Index (SI) in their vertices. The SI encodes local surface variations and we exploit the colorization of the model driven by the SI to generate other view and enrich the dataset. Our method has been validated on a relief recognition benchmark on archaeological fragments proposed within the SHape REtrieval Contest (SHREC) 2018.