Miracle-dz/UniFAR-FTUs huggingface dataset:
CHARACTERIZING UNCERTAINTY IN LOW-DIMENSIONAL MODEL SELECTION

In the context of big and often high-dimensional data, valid procedures for assessing variable importance and identifying accurate model representations are essential tools, especially in the presence of substantial instability. Instead of seeking to find only a single set of covariates that form the empirically optimal model, we propose an automated procedure for identifying an entire collection of stable and predictively similar models. Within each iterate of the selection method, we develop a procedure to identify covariates that are predictively similar with regard to a chosen loss function, thereby providing multiple options as to which covariate should be added to the final model. By construction, our procedure acts a wrapper method that can be applied to any statistical or machine learning technique. Furthermore, we provide a natural and intuitive graphical display of these model paths that makes apparent potential underlying relationships between covariates as well as the relative importance of the covariates selected.
Fast Region Convolutional Neural Network Lane and Road Defect Detection for Autonomous Vehicle Application

Road markers are the main landmarks and provide critical information for traffic guidance and safety for drivers. These markers are especially crucial for an autonomous vehicle as this type of vehicle needs to make automated decisions to ensure the safety of other road users. Therefore, for an autonomous vehicle to avoid traffic accidents, these markers should be detected and localized accurately. Additionally, road defects may also present a hazard to autonomous vehicles. This paper proposes a Faster Region Convolutional Neural Network (FRCNN) to detect and localize road markers and potholes on the bird's eye view images of roads. Data were collected using the Point Grey Blackfly camera mounted on the roof of a car. The Inverse Perspective Mapping (IPM) algorithm was used to transform the images into a bird's eye view perspective. The bird's eye view perspective is particularly important as it is easier to detect objects of interest from the top view. The data was then used to train an FRCNN on MATLAB R2018a. The results indicate that the FRCNN was successful in the detection of the objects of interest with some overlapping issues, which will be addressed in future works.
Power- and Cache-Aware Task Mapping with Dynamic Power Budgeting for Many-Cores

Two factors primarily affect the performance of multi-threaded tasks on many-core processors with logically-shared and physically-distributed Last-Level Cache (LLC): the LLC latencies of threads running on different cores and the per-core power budgets that aim to guarantee thermally safe operation. Two knobs affect these factors: First, the mapping of threads to cores affects both the LLC latencies and the power budgets. Second, dynamic power budgeting refines the power budgets during task execution. A mapping that spatially distributes threads across the many-core increases the power budgets, but unfortunately also increases the LLC latencies. Contrarily, mapping all threads near the center of the many-core minimizes the LLC latencies, but unfortunately also decreases the power budgets. Consequently, both metrics cannot be simultaneously optimal, which leads to a Pareto-optimization for task mapping that has formerly not been exploited. Dynamic power budgeting reallocates the power budgets according to the tasks’ execution phases. This results in a dynamically changing non-uniform power budget, which further increases the performance. We are the first to present a run-time algorithm PCGov combining task-agnostic task mapping and task-aware dynamic power budgeting for many-cores with shared distributed LLC. PCGov yields up to 21 percent lower response time and 13 percent lower energy consumption compared to the state-of-the-art, with a low overhead of less than 0.5 percent.
Word Sense Disambiguation: Survey Study

The process of identifying the correct sense of a given word in a particular sentence is called Word Sense Disambiguation (WSD). It is complex problem because it involves drawing knowledge from various sources. Significant amount of effort has been put into resolving this problem in machine learning since its inception but the toil is still ongoing. Many techniques were used in WSD and implemented on different corpora for almost all languages. In this paper, WSD algorithms were classified to three categories as Knowledge-based, supervised and unsupervised techniques. Each category will be studied in details with explanation of almost all the algorithms in each category. Hence work examples for each method were taken with the used language, the used corpora and other factors. The benefits and drawback of each method were recorded. Some of these techniques have limitations in some situations, therefore this work will helps the researchers in the field of natural language processing to select the suitable algorithms to solve their particular problem in WSD. The novelty of the work can be seen in the comparison of the used works and the used algorithms. From this work, it was concluded that (i) some methods give high accuracy for language but low for other, (ii) the size of the used data set affects the performance of the used algorithm, (iii) some of these approaches can be run fastly but with limitation of the accuracy and (iv) most of these approaches are implemented for many languages successfully.
DETECTION AND CONTROL OF SLIPPAGE AT THE ROBOTIC GRIPPER

A study is presented which utilizes a tactile system using force sensing resistor (FSR) material as the tactile elements, for detection and control of slippage at the robotic gripper. The characteristics at the output of the FSR element during the presence of a dynamic phenomenon are studied. The distinguishing features of these characteristics are identified and used for slippage detection. The development of the necessary hardware to identify these salient features for slippage detection is described. The possibility of using these characteristics to control slippage and their limitations for effective slippage control are discussed. A modified control strategy for slippage detection is presented. The testing of detection and control
Health Index for Transformer Condition Assessment

This research is performed in order to estimate the overall condition of power Transformers, which is considered by 19 test: Site acceptance test, quality oil test and insulation acceptance test. One way is using the Health Index (HI), this technique is useful for maintenance strategy planning. The aim of this research is to represent the new method to assess the conditions of transformer by applying a new developed HI table, which was improved from the conventional method, with Wavelet network and validate by date mining. A case study was used to evaluate the performance of the new develop HI. Therefore, the proposed method can create an efficient preventive maintenance plans for power transformers.
Accounting for Tube Hematocrit in Modeling of Blood Flow in Cerebral Capillary Networks

The aim of this paper consists in the derivation of an analytic formula for the hydraulic resistance of capillaries, taking into account the tube hematocrit level. The consistency of the derived formula is verified using Finite Element simulations. Such an effective formula allows for assigning resistances, depending on the hematocrit level, to the edges of networks modeling biological capillary systems, which extends our earlier models of blood flow through large capillary networks. Numerical simulations conducted for large capillary networks with random topologies demonstrate the importance of accounting for the hematocrit level for obtaining consistent results.
Analysis of Network Coding Nodes Selection in Wireless Mesh Networks

Wireless mesh networks can significantly improve the transmission performance of multi-hop links by using network coding techniques.However,there is the cost of network coding,how to choose encoding nodes in order to reduce the cost of network coding is the focus of study.This paper discussed network coding nodes selection in wireless mesh networks,proposed a algorithm of network coding node selection based on super-key nodes.When Ford-Fulkerson labeling algorithm finds augmented chain,the algorithm statistics in-degree of each node on path and saves the obtained information from different link in node,then confirms which nodes are super-key nodes.These super-key nodes are encoding nodes.Simulation results show that the proposed algorithm reduces the number of encoding nodes obviously while achieving the multicast maximum flow.
Effect of sequencing rules on order release policies in a stochastic and dynamic job shop with sequence-dependent setup times – a simulation study

Order review and release in job shop with sequence-dependent setup times is one of the most complex job shop scheduling problems. This article analyses the effect of sequencing rules on different order release policies for performance measures i.e. mean throughput time, total setups, mean setup time, number of tardy jobs and makespan in a stochastic and dynamic job shop with sequence-dependent setup time. Four sequencing rules, viz. first-come-first-serve, planned release date, earliest due date, shortest processing time and five order review and release policies i.e. aggregate workload trigger, workcentre workload trigger, corrected workload trigger, upper bound release and lancaster university management school corrected order release are taken into consideration. A simulation model using Promodel® is developed for experimental purpose. Results indicate that for a given order review and release policy, there is an effect of sequencing rule in a stochastic and dynamic environment with consideration of sequence-dependent setup time and the best performing sequencing rule is different for different order review and release policies for a given performance measure.
Recognition of milk somatic cells based on dichotomy model

The number of various milk somatic cells can be calculated accurately by using image processing technology. In order to identify four kinds of cells,a method of milk somatic cell recognition based on dichotomy model was proposed. Firstly, eight typical morphological features were extracted from cells. Some morphological features were selected by feature optimization and Bayes classifier was used twice to carry out two binary classification. The texture feature described by HOG and BP neural network were uesd on the last classify. Finally, 96.02% of the average classification accuracy was achieved in the actual experiment of milk somatic cell image data.
Application of entropy formulas in detection of denial‐of‐service attacks

The paper compares five entropy formulas (Shannon, Tsallis, Rényi, Bhatia‐Singh, and Ubriaco) and their application in the detection of distributed denial‐of‐service (DDoS) attacks. The Shannon formula has been used extensively for this purpose for more than a decade. The use of the Tsallis and Rényi formulas in this context has also been proposed. Bhatia‐Singh entropy is a novel information metric with promising results in initial applications in this area. Ubriaco proposed an entropy function based on the fractional calculus. In this paper, flow size distribution was used as the input for detection. The type of DDoS attack is SYN flood, and simulation was used to obtain the input dataset. The results show that the Rényi and Bhatia‐Singh detectors perform better than the rest. Rényi and Tsallis performed similarly with respect to the true positive rate, but Rényi had a much lower false positive rate. The Bhatia‐Singh detector had the best true positive rate but a higher false positive rate than Rényi. The Ubriaco detector performed similar to the Shannon detector. With respect to detection delay, Tsallis, Ubriaco, and Shannon produced similar results, with a slight advantage associated with the Ubriaco detector, while Rényi and Bhatia‐Singh had a larger detection delay than the former three.
Tibetan studies of corpus description method

Tibetan corpus multistage process is based on description of the basic attributes of the Tibetan lexicon,according to the actual need of the Tibetan corpus multistage process,through the syntax and semantics of Tibetan information description and Tibetan words after the analysis,it can provide a feasible method for the computer to process automatically the Tibetan collocations structure,grammatical information,semantic information and POS-tagging.
How Children Individualize Numerical Routines: Elements of a Discursive Theory in Making

Conceptualizing numbers as discursive constructs generated in, and for the sake of, communication, we investigated the development of the numerical discourse of Milo, a boy who was 2 years and 8 months old when we first met him and whom we then followed for 18 months. Our analyses of the child’s evolving responses to the question “Where is there more X? ” (WiTM) corroborated the basic theoretical tenet, according to which numerical thinking begins in our culture with the independent appearance of (1) the quantitative–non-numerical discourse and (2) numerical–non-quantitative discourse. In Milo’s case, these two discourses, although constantly evolving, remained separate for months. A number of clearly distinguishable developments preceded the eventual consolidation of the independent numerical and quantitative “rituals” the child performed in response to WiTM-question into one compound routine of quantitative–numerical comparison. It is at this point that the numerical and quantitative ways of thinking began coalescing into a single discourse and the initial binary relation of order gave rise to the unary relation of cardinality. All this is summarized in a three-stage model of the development of numerical discourse. Three additional case studies that corroborated this model are reported briefly.
OBJECT DETECTION USING VIOLA-JONES ALGORITHM

In this paper it has been described and applied method for detecting face and face parts in images using the Viola-Jones algorithm. The work is based on Computer Vision Systems, artificial intelligence that deals with the recognition of two-dimensional or three-dimensional objects. When Cascade Object Detector script is trained, multimedia content is assigned for recognition. In this work the content will be in the form of an image, where the program will have the task of recognizing the objects in the images, separating the parts of the images in the head area, and on each discovered face, separately mark the area around the eyes, nose and mouth.Algorithm for detection and recognition is based on scanning and analyzing front part of human head. Common usage of face detection and recognition can be find in biometry, photography, on autofocus option which is implemented in professional photo cameras or on smiling detectors (Keller, 2007). Marketing is also popular field where face detection and recognition can be used. For example, web cameras that are implemented in TVs, can detect every face in near area. Calculating different type of algorithms and parameters, based on sex, age, ethnicity, system can play precisely segmented television commercials and campaigns. Example of that kind of systems is OptimEyes. (Strasburger, 2013)In other words, every algorithm that has as its main goal to detect and recognize face from image, should give as a feedback information, is there any face and if answer is positive, where is its location on image. In order to achieve acceptable performances, algorithm should minimize false recognitions. These are the cases when the algorithm ignores and does not recognize the real object from the image, and vice versa, when the wrong object is recognized as real. One of the algorithms that is frequently applied in this area of research is the Viola-Jones algorithm. This algorithm is functional in real time, meaning that besides detection, it is also possible to adjust the ability to monitor faces from video material.In this paper, the problem that will be analyzed is facial image detection. Man can do this task in a very simple way, but to do the same with a computer, it is necessary to have a range of precise and accurate information, formulas, methods and techniques. In order to maximize the precision of recognizing the face of the image using the Viola-Jones algorithm, it is desirable that the objects in the images are completely face-to-face with the image-taking device, which will be shown through experiments.
Comparison of tools suitable for use on the field for the evaluation of feelings in HMI usage

Creating interfaces that trigger emotions requires being able to evaluate and quantify users' feelings. The goal of this study is to compare the existing tools that allow the evaluation of feelings in man machine interactions in an automotive context, while considering two objectives: (i) the balance between intrusiveness of the measuring instruments and the amount of data as to avoid biasing user experience; (ii) how much such tools can be used in the field by ergonomists and designers who are not, themselves, experts of emotion evaluation methods. To do so, we introduce the leading types of tools that are used to assess feelings along with their associated theoretical approaches. We then compare them according to a set of selected criteria that are relevant to the evaluation of human machine interfaces in the field.
TRANSIENT BEHAVIOR IMPROVEMENT OF ITERATIVE LEARNING CONTROL SYSTEM BASED ON PSEUDOSPECTRUM METHOD

Robust monotonic convergence (RMC) should be obtained when designing ILC (Iterative learning control) system. But it may cause convergence rate becoming slower. In order to maintain stability of control system, transient growth is often limited within a certain range, which is one of the most properties of transient behaviors. Pseudospectral of the system transfer matrix is introduced to predict the transient growth of ILC system. At the same time, asymptotic performance can be improved if the transient growth is kept small. Under this case, the convergence rate will also be speeded up. The simulation demonstrates the effective of new method for designing ILC system.
Unsupervised Feature Selection Method for Imbalanced Data

The traditional feature selection methods handle data with balanced distribution,aim for getting optimal classification accuracy,so there exist very limited feature selection methods that perform well on imbalance data.This study proposes a new feature selection method based on the character of data distribution for imbalanced data sets.It modifies data distribution for balance by assigning different weights to the function of feature importance measurements according to the variation of the size of clusters in unsupervised learning.Experimental results on several UCI datasets show that the performance of the proposed method outperforms other classic feature selection algorithms.It not only maintains or enhances the classification performance and dimensionality reduction,but also improves the precision,recall and F-Measure of the minor classes on different classifiers.
THE ANALYSIS OF ARKADAŞ TÜRKÇE SÖZLÜK ( ARKADAŞ TURKISH DICTIONARY ) AND THE SUGGESTED MODIFICATIONS FOR ITS LEARNER ' S VERSION

This study aims to draw attention to two of the problems that Turkish lexicography faces today. One of these problems is that there are not any Turkish to Turkish dictionaries that have been prepared for the people who are learning Turkish as a second or foreign language. Another problem that faces Turkish lexicography is the deficiency of a corpus-based approach in dictionary building process. In this study, the layout of a dictionary that was prepared for the native speakers of Turkish was analyzed, and some modifications were suggested for the learner’s version of this dictionary. The importance of corpus based approach in dictionary building process is the other point that is emphasized throughout the study. The study consists of two parts. In the first part, the modifications that can be made on a Turkish-to-Turkish dictionary were determined through the analyses of two English-to-English dictionaries, one of which was prepared for the native speakers of English and the other for the foreign learners of English. The possible modifications that can be done in the front-back matters and the A-Z entries of the Turkish Dictionary were presented in a list. In the second part, 1 Adıyaman University, Department of English Language and Literature, Adıyaman, Türkiye, scakir@adiyaman.edu. tr Makale gönderim tarihi: 5 Nisan 2016; Kabul tarihi: 29 Temmuz 2016
From hybrid events to the next generation - interactive virtual events : Viewed from three different stakeholders’ point of view

The traditional event management has changed in recent years with the new technologies and currently we are living at the time of hybrid events, which combines the traditional events and the innovative technologies such as live streaming. Webinars and web coaching have also become part of every-day-life the future is moving towards the virtual space. Virtual reality, artificial intelligence and holograms are the technologies that are developed now in many industries. What would happen if virtual reality, interaction, artificial intelligence and holograms would be added to the event industry? The three biggest stakeholders in event business are the attendees, speakers and organizers. How would the evolving technology change their experiences when attending or organizing a virtual event? Aim is to go through the event journey from the three stakeholders’ point of view and think about the opportunities and challenges that the virtual environment, artificial intelligence, interaction and holograms bring to their event journey and what are the points of connection amongst these stakeholders. In this thesis, there is also a short research done on how virtual reality, holograms and AI is used and what are the probable future scenarios of these technologies.
Review and Content Analysis of the International Review of Research in Open and Distance/Distributed Learning (2000–2015)

This paper presents a review of distance education literature published in the International Review of Research in Open and Distance/Distributed Learning (IRRODL) to describe the status thereof and to identify gaps and priority areas in distance education research based on a validated classification of research areas. All articles (N = 580) published between 2000 and 2015 were reviewed for this study. An analysis of abstracts using the text-mining tool Leximancer over three 5-year periods reveals the following broad themes over the three periods: the establishment of online learning and distance education institutions (2000–2005), widening access to education and online learning support (2006–2010), and the emergence of Massive Open Online Courses (MOOCs) and Open Educational Resources (OER) (2011–2015). The analysis auf publication and authorship patterns revealed that IRRODL is a very international journal with a high impact in terms of citations.