Miracle-dz/UniFAR-FTUs huggingface dataset:
The Eﬀect of Using PRAAT Software on Pre-Intermediate EFL Learners’ Supra Segmental Features

The present study investigated the eﬀect of using PRAAT as a free computer software package for the scientific analysis of speech in phonetics on pre-intermediate Iranian English as foreign language (EFL) learners’ supra segmental features (i.e., intonation and stress). The design of the study was a Quasi-experimental research design with a pre and post-test. In doing so, 40 male and female majoring in English translation with the age ranging from 19 to 37 years old were selected. They were studying EFL at the Islamic Azad University of Abadan, Iran. The participants were selected among 55 students through the Oxford Placement Test (OPT) and their level of proficiency was determined as the pre-intermediate level. Then they were divided into two experimental and control groups through convenience sampling method. Then they took a pre-test to evaluate their proficiency in supra segmental features (i.e. intonation and stress). The control group (n= 20) was exposed to the traditional method and the experimental group (n=20) was exposed to the use of PRAAT software in learning supra segmental features (i.e. intonation and stress). After the treatment sessions, both groups took the post-test that was a modified pre-test. Data were analyzed through Independent and Paired Samples t-tests. Result indicated the experimental group outperformed the control group. Implications of the study suggest that the EFL teachers can use PRAAT software to teach supra segmental features eﬀectively.
Association of IGF 1 and IGFBP 3 polymorphisms with colorectal polyps and colorectal cancer risk

Purpose Insulin-like growth factor 1 (IGF1) is a peptide growth factor that promotes cell proliferation and inhibits apoptosis. The bioavailability of IGF1 is regulated by the insulin-like growth factor binding protein 3 (IGFBP3). The purpose of this study was to examine the association of genetic variants in IGF1 (rs6214, rs6220, and rs35767) and IGFBP3 (rs2854744 and rs2854746) with risk of colorectal polyps and colorectal cancer. Methods In this ongoing colorectal cancer study of Austria (CORSA), a total of 3,360 Caucasian participants, consisting of 178 colorectal cancer patients, 328 patients with high risk polyps, 1,059 patients with low risk colorectal polyps, and 1,795 colonoscopy-negative controls, were recruited within a large colorectal screening project in the province Burgenland and from three hospitals in Vienna. Multiple logistic regression was applied to compare individuals of the control group against three different risk groups, namely, colorectal cancer group, high risk polyp group, and low risk polyp group. Results Carriers of the homozygous polymorphic genotype of the SNP rs6214 were associated with an increased colorectal risk (OR = 1.79, 95% CI 1.04–1.90) compared to the colonoscopy-negative controls; this was also found when combining colorectal cancer cases and high risk polyp group (OR = 1.39, 95% CI 1.01–1.90). Conclusion Our results suggest that the SNP rs6214 of IGF1 could have an impact on developing colorectal cancer and colorectal polyps with villous elements.
Implementation of wireless LAN access point with quality of service support

The maintaining of the quality of service (QoS) of the data transfers in wireless local area networks (WLAN) is an extensive task. An implementation of an access point (AP) providing an access from WLANs to backbone wired networks is presented in this paper. The AP adapts the QoS signalling of the connected networks. The AP has been implemented in Windows NT workstation as a protocol driver and its operation has been verified. The evaluation of the driver performance encourages the usage of the AP in demanding environments.
Floating Potential of a Floating Electron Emitting Electrode that Terminates a Bounded Plasma System : Kinetic Model and PIC Simulation

A bounded plasma system is studied by a one-dimensional fully kinetic model and PIC simulation. The system is terminated by a floating electron emitting collector. The effects of an increasing electron emission to the potential formation in the system are studied. It is found that with increasing electron emission the floating potential of the collector increases also but then it saturates when the space charge limited emission is reached. The saturation level is between 1.7 and 1.8 electron temperatures below the source potential.
A Tool for Detection and Analysis of a Human Face for Aesthetical Quality Using Mobile Devices

A tool for mobile devices is presented to detect a human face in an image and analyze the detected face for its aesthetical quality. For the purpose of face detection, the tool uses two well-known classifiers: Haar feature-based cascade classifier and Local Binary Patterns (LBP) cascade classifier. Once a face is detected, the tool uses the Active Shape Model (ASM) algorithm to identify facial feature points and obtain raw data accordingly for aesthetical quality analysis. The analysis is carried out on the notion of Golden Ratio by finding ratios on the facial feature points obtained by using the ASM algorithm. To determine its effectiveness and limitations the tool is tested on animal faces, beautiful faces, ordinary faces, and unattractive faces. The statistics of results exposes distinctions between unattractive faces and beautiful/ordinary faces. However, no such distinction exists to separate beautiful faces from ordinary faces.
Multi-Scale Recalibrated Features Fusion for Acoustic Scene Classification Technical Report

We investigate the effectiveness of multi-scale recalibrated features fusion for acoustic scene classification as contribution to the subtask of the IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE 2019). A general problem in acoustic scene classification task is audio signal segment contains less effective information. In order to further utilize features with less effective information to improve classification accuracy, we introduce the Squeeze-and-Excitation unit to embed the backbone structure of Xception to recalibrate the channel weights of feature maps in each block. In addition, the recalibrated features of multiscale are fused and finally fed into the full connection layer to get more useful information. Furthermore, we introduce Mixup method to augment the data in training stage to reduce the degree of over-fitting of network. The proposed method attains a recognition accuracy of 77.5%, which is 13% higher compared to the baseline system of the DCASE 2019 Acoustic Scenes Classification task.
ISLAMIC BANKING-GROWTH NEXUS IN MALAYSIA

The growth of the Islamic finance in Malaysia has increased enormously and consistent with the Malaysia determination to become a regional hub of Islamic finance. This paper will empirically observe the nexus between the Islamic banking performance and economic growth in Malaysia. The objectives of this study are to investigate the co-integration and short-run relationships between Islamic bank performance and economic growth. The data was collected using quarterly data from the Monthly Bulletin Statistics of Bank Negara Malaysia (BNM) and Department of Statistic Malaysia from 2007 to 2015. The autoregressive distribution lag (ARDL) method is utilized in this study. The findings show the financial development of the Islamic bank has a positive influence on the growth in Malaysia in the short run and long run. Based on the results, total Islamic financing and investment have a positive association with economic growth. This paper also provides clear evidence that the improvement in Islamic banking development in Malaysia may encourage economic growth. © 2018 Published by Future Academy www.FutureAcademy. org.UK
NICHING TECHNIQUES BASED ON SEXUAL CONFLICT IN CO-EVOLUTIONARY MULTI-AGENT SYSTEM

Evolutionary algorithms often suffer from premature loss of population diversity what limits their adaptive capacities in dynamic environments and leads to location of single solution in case of multi-modal fitness landscapes. Niching techniques for evolutionary algorithms are aimed at locating more than one optima of multi-modal functions. Sexual selection resulting from sexual conflict and coevolution of female mate choice and male display trait is considered to be one of the ecological interactions responsible for speciation. This paper introduces the coevolutionary multi-agent system with speciation by sexual conflict and its formal model. Such system is applied to multi-modal function optimization and the results from runs against commonly used test functions are presented.
Estimation of the Normal Contact Stiffness for Frictional Interface in Sticking and Sliding Conditions

Modeling of frictional contact systems with high accuracy needs the knowledge of several contact parameters, which are mainly related to the local phenomena at the contact interfaces and affect the complex dynamics of mechanical systems in a prominent way. This work presents a newer approach for identifying reliable values of the normal contact stiffness between surfaces in contact, in both sliding and sticking conditions. The combination of experimental tests, on a dedicated set-up, with finite element modeling, allowed for an indirect determination of the normal contact stiffness. The stiffness was found to increase with increasing contact pressure and decreasing roughness, while the evolution of surface topography and third-body rheology affected the contact stiffness when sliding.
Time and Causality by Russell Davidson

The understanding of causal chains and mechanisms is an essential part of any scientific activity that aims at better explanation of its subject matter, and better understanding of it. While any account of causality requires that a cause should precede its effect, accounts of causality in physics are complicated by the fact that the role of time in current theoretical physics has evolved very substantially throughout the twentieth century. In this article, I review the status of time and causality in physics, both the classical physics of the nineteenth century, and modern physics based on relativity and quantum mechanics. I then move on to econometrics, with some mention of statistics more generally, and emphasise the role of models in making sense of causal notions, and their place in scientific explanation.
SURROUNDING ENVIRONMENT MONITORING BY A VIDEO SURVEILLANCE VEHICLE WITH 2-CAMERA OMNI-IMAGING DEVICES

New vision-based methods for monitoring the surrounding environment around a video surveillance vehicle via the use of two 2-camera omni-imaging devices on the vehicle roof are proposed. To inspect blind spots around a vehicle, a method of analyzing automatically the vehicle moving direction by optical flow analysis in omni-images and generating corresponding perspective-view images is proposed. Techniques for monitoring a nearby car from a static or moving video surveillance vehicle are also proposed. The 3D data of a detected car are computed using a space-mapping approach, and a top-view surround map is generated to help observation of the vehicle surrounding conditions. Good experimental results show the feasibility of the proposed system and techniques.
Modeling and simulation of rumor spread in C2 network organization

Rumor spread in C2 organization is dynamic,adaptive,and nonlinear.This paper studied the relationships among the rumor infect ratio,the influence degree,the spreaders characteristic,and command span based on multi agent simulation experiments and sensitivity analyses.Some conclusions are got:The influence degree of subordinate upon superior is more sensitive to infect ratio than the degree of superior upon subordinate.The primal rumor spreaders are more near the center node,the infect ratio is higher.The command span is larger,the infect ratio is higher.
Differentiation of Certification Standards : The trade-off between generality and effectiveness in certification systems

The following study provides a conceptual framework explaining certification trends in the food sector. A growing number of certification systems indicates the importance of third party audits. On the other hand, this development also leads to an escalating variety of certification schemes implying the danger of “audit tourism” and, as a consequence, rising transaction costs. Therefore, the driving forces of this differentiation process will be analysed. The trade-off between generality of a system and its effectiveness is revealed which can be traced back to the disadvantages of general management system audits.
How will we know if the Auckland Unitary Plan is working? Accounting for the redevelopment premium in house price measurement

One of the stated goals of the Auckland Unitary Plan (AUP) is to ensure that housing in Auckland is a⁄ordable. To achieve this, the plan encourages housing supply by relaxing a variety of land use regulations in targeted areas of the region, including constraints on residential density. However, relaxing restrictions on density increases the value of land, which in(cid:135)ates the value of properties that can be redeveloped to support additional dwellings. Disentangling the de(cid:135)ationary e⁄ect of increased housing supply from the in(cid:135)ationary e⁄ect of an increased redevelopment premium will therefore be key to evaluating the e¢ cacy of the plan. In this paper we present a set of price indices that are designed to assist policymakers in assessing whether the AUP is restoring a⁄ordability to the housing market. Our approach is based on sorting property transactions according to their potential for redevelopment and constructing a di⁄erent price index for each group. The indices show that residential properties with the greatest potential for redevelopment under the AUP have experienced substantially more in(cid:135)ation over the 2011 to 2015 period. This result is consistent with upzoning increasing the redevelopment premium. Going forward, the price indices for properties that have less potential for redevelopment will tell us whether intensi(cid:133)cation is bringing a⁄ordable housing options to the market.
Uncertainty Estimation in Bayesian Neural Networks And Links to Interpretability

Bayesian neural networks, a hybrid of deep neural networks and probabilistic models, combine the flexibility of deep learning with estimates of uncertainty in predictions. However, like deep neural networks, they are often difficult to interpret – we do not know how correct predictions are made and what makes the prediction uncertain. Numerous approaches to interpreting neural network predictions have been studied, but there is limited work on interpreting uncertainty in model predictions. Here, we propose a method to visualise the contribution of individual features to predictive uncertainty, epistemic uncertainty (from the model weights), and aleatoric uncertainty (inherent in the input). Our approach measures the change in uncertainty when a given feature of the input is known, compared to when it is unknown. Applying our approach to the CIFAR10 and ISIC2018 skin lesion diagnosis datasets, we generate smooth visualisations highlighting pixels in the input image that most impact each type of uncertainty. On inputs that are difficult to classify, different areas of the image contribute to epistemic and aleatoric uncertainties. This suggests that some areas of the test image determine its classification (aleatoric uncertainty), other areas distinguish it from the training distribution (epistemic uncertainty), and both contribute to overall predictive uncertainty.
JUVENILE PLANKTONIC CEPHALOPODS SAMPLED OFF THE COASTS OF CENTRAL GREECE ( EASTERN MEDITERRANEAN ) DURING WINTER

Cephalopod early life stages were identified from plankton samples taken with bongonets (500 μm mesh size) off the coasts of central Greece (N: 39o 18 ́–37o 25 ́, E: 20o 15 ́–24o 33 ́) in December 2000–February 2001. The sampling was not directed at cephalopods but provided valuable information on the distribution of cephalopod planktonic stages in Greek waters. Oblique tows were carried out over a grid of 100 stations from a depth of 200 m to the surface on a 24 hour basis. At each station vertical profiles of temperature and salinity were also taken using a SBE-25 CTD profiler. A total of 9 taxa belonging to five families were caught at 21 stations. Among these, the Ommastrephidae was the most numerically abundant (75.7%) family followed by the Octopodidae (15.7%), Loliginidae (4.3%), Sepiolidae (2.9%) and Enoploteuthidae (1.4%). Frequency of occurrence (number of positive tows/total number of tows) of cephalopods was highest during evening tows and in waters with surface temperature >18°C. Most positive tows yielded less than 3 specimens.
Attention-based Hierarchical Deep Reinforcement Learning for Lane Change Behaviors in Autonomous Driving

Performing safe and efficient lane changes is a crucial feature for creating fully autonomous vehicles. Recent advances have demonstrated successful lane following behavior using deep reinforcement learning, yet the interactions with other vehicles on-road for lane changes are rarely considered. In this paper, we design a hierarchical Deep Reinforcement Learning (DRL) algorithm to learn lane change behaviors in dense traffic. By breaking down overall behavior to sub-policies, faster and safer lane change actions can be learned. We also apply temporal and spatial attention to the DRL architecture, which helps the vehicle focus more on surrounding vehicles and leads to smoother lane change behavior. We conduct our experiments in the TORCS simulator and the results outperform the state-of-the-art deep reinforcement learning algorithm in various lane change scenarios.
A Probabilistic Analysis of Traffic Separation in Shared Ethernet Using the h-BEB Collision Resolution Algorithm 1

Ethernet is a widely used network technology, with a Medium Access Control protocol based on the collision detection between randomly initiated transmissions. It has a non-deterministic behaviour, due to the use of a probabilistic contention resolution algorithm, which impairs the support of real-time communications. In this paper, it is proposed the use of a modified algorithm for the collision resolution in shared Ethernet networks. Such algorithm, referred as high priority Binary Exponential Backoff (h-BEB), provides high priority traffic separation, enabling the support of real-time communications. It allows multiple Ethernet standard devices to coexist with one h-BEB modified station in the same network segment, imposing a higher priority for the transfer of h-BEB related traffic. The probabilistic analysis of traffic separation provided by the proposed algorithm shows that it guarantees an access delay significantly smaller for an h-BEB station, when compared with the access delay for BEB stations.
DETECTION OF HIGH URBAN VEGETATION WITH AIRBORNE LASER SCANNING DATA

Airborne laser scanning (ALS) is known as an operational tool for collecting high resolution elevation information (> 4 pt/m2). The characteristics of the emitted pulses, i.e. their spatial extent, allow the detection of multiple echoes, which occur especially in areas covered with high vegetation. In the case of forested areas this means that not only the first reflection on the canopy but also reflections on or near the ground surface are recorded. The detection of high vegetation in urban areas (single trees, groups, and small forests next to residential areas) is needed for several applications. Classified vegetation and derived parameters, such as height, volume and density, are used in urban planning, urban ecology and 3D city modeling. The here presented algorithm follows the principle of object-based point cloud analysis (OBPA), which consists of (i) segmentation of the original ALS point cloud, (ii) feature calculation for the delineated segments and (iii) classification to label the objects of interest. The segmentation is based on an intelligent seed point selection by surface roughness, initializing a region growing process. Point features for the segmentation and classification, respectively, are e.g. roughness, the ratio between 3D and 2D point density, or statistics on first an last echo occurrence within the segments. The advantage of the developed algorithm is that no calculation of a digital terrain model is needed and that it solely works in the original point cloud, maintaining the maximal achievable accuracy. For the evaluation of the method a flight campaign of the city of Innsbruck/Austria is used as test site.
Cancer: Strange Attractors and Complexity

Cancer is a complex adaptive ecosystem and remains the lead cause of ‘disease’-related death in pediatric patients in North America. Machine learning, Network science, Fluid dynamics and Quantum Mechanics are hereby discussed as tools to advance oncology and cancer reprogramming. The fluid dynamics of cell-fate transitions, cancer pattern formation and invasion are reviewed in this paper. Cancer cell decision-making is investigated through dynamical systems and complexity theory. A fluid-dynamics grid-scheme based Deep Learning neural networks is proposed as the solution to identify strange attractors in time-series multi-omics (scRNA-Seq) data and time lapse imaging of cancer stem cell differentiation. Cancer is discussed within the context of two unsolved foundational problems in science: the three-dimensional Navier-Stokes equations global regularity, smoothness and existence and the P vs. NP problem.
TEACHER’S WAYS OF WORKING WITH GIVING FEEDBACK: PRACTICES IN ELT CLASSROOM IN PRIMARY SCHOOLS

In relation to assessment, giving feedback is considered a valuable form of input for students to enable them to improve. Feedback in the form of comments may have the purpose to indicate the correct or incorrect responses (Hattie & Timperley, 2007) or it may have other purposes, depending on what the teachers want to focus on with the feedback. Since feedback might be given for different purposes, it might also come in different formats. This means that different forms of feedback might be used for different purposes. The teachers’ ways of giving the feedback may be influenced by their beliefs about the feedback itself. This study aimes at investigating primary schoolteachers’ knowledge and beliefs about the English program in primary schools in Indonesia, focusing on their pedagogical knowledge and how it influences the way they give feedbacks and work with the feedbacks. The study reveals that All teachers believe that feedback is given for correction. This is why most of the teachers focus their giving of feedback on indicating the students’ errors. They know how to indicate correctness or incorrectness is by using codes and most of the teachers also provide the correct answers, and this way of giving correction is called as ‘direct feedback’. By doing this, the teachers expect that the students will be able to learn from the correction. Keywords: teachers’ knowledge, teacher’s beliefs, feedback, assessment, English language teaching
Fast and Precise Positioning Method Based Double-digital-Camera

At first,we choose suitable target to solve the problem of digital camera calibration.In order to improve the precise,we use sub-pixel measure method.Then we establish the mathematic model based distorts calibration,use sequential approach method to find the parameters,so we fulfill the digital camera calibration.For the position based double-digital-camera,we use basic formula of vector to establish a simple mathematic model,so that it is gained,which is a system about the coordinates of camera.We apply Wu elimination to solve the system.Finally,the experiment shows our method is good.