Miracle-dz/UniFAR-FTUs huggingface dataset:
Representing Memory Graphs Interactive debugging using the Eclipse Framework

The overall aim of the project was to recover, analyse and represent the memory graph from a running program, in such a way that other programs can use a public API to visualise the memory graph. It has achieved the following: • Building an integrated plugin for an IDE that acquires and represents the memory graph of one or more running programs, in such a way that it can be used in conjunction with existing debugging tools. • Exposing the representation to other plugins, through an API, with the intention of having the memory graph displayed visually. • Further developing the idea to produce novel ways of reducing the output memory graph size by removing certain objects, according to a set of application rules. • Investigating ways in which a memory graph might be focused, particularly through identifying subjectively important or relevant objects, providing some promising initial results. Previous tools have been little more than experiments, testing the concept this projects represents some of the first steps to update the methods for use with modern development environments in a generic fashion. In particular, the efforts to transform a memory graph by selectively removing certain nodes and the research into analysing important or relevant objects are areas of software development research that have not previously been investigated.
The Concept of "Communication" in Contemporary Research

The article is a review of the research of the communication phenomenon, including the history of formation of the theory of communication, communication models. The article considers communication models of various authors. The chronological analysis of the theories and models of communication is about 100 years, from the 20s of the 20th century to the present. The article analyzes the modern definition of communication of Russian and international authors, reveals the structure of these definitions and their basic content. Then we consider the theory of communication, ranging from Harry Nyquist to Harold Lasswell. The final section of the article chronologically examines the most important modern communication models of such authors as Shannon and Weaver, Berlo, Schramm, Barnlund, Innis, Craig. Finally, we make conclusions on the current trends in the research of communication practices.
RELATING ERP CONFIGURATION MODELS TO STRATEGY OF ORGANIZATIONS

Two Competing methods of ERP configuration are reported in the literature: OOM (Object Oriented Methodology) and DEM (Dynamic Enterprise modeling). OOM is comparatively cheap, simple and fast, whereas DEM allows for handling uncertainty in businesses. We argue that choice of ERP Configuration varies with the organizational strategy types. For defenders type of organizations OOM method for configuring ERP will be most suitable and for prospectors and analyzers (that have higher business uncertainty) DEM method for ERP configuration will be most suitable.
A Study on Data Analysis Process Management System in MapReduce using BPM

MapReduce is a distribution-system-based programming model to process massive data and has been utilized as an analysis model not only in the academic world but also in the industrial fields. However, developers who implement MapReduce have some deficiency in understanding the data analysis, while data analysts have difficulty in programming MapReduce for various analyses by themselves. Hence, it is difficult for developers to provide a demanded analysis output. In order to solve such difficulty between developers of MapReduce and the data analysts, this study proposes a new MapReduce analysis process management system based on BPM (Business Process Management). This system was designed to provide a mutual complimentary intermediary function for MapReduce developers and analysts, and also makes it possible to respond flexibly to any alteration of analysis procedure.
Learning to Represent Multiple Object Classes on a Continuous Viewsphere

Existing work on multi-class object detection usually does not cover the entire viewsphere of each class in a continuous way: object classes from different viewpoints are either discretized into a few sparse viewpoints [12], or treated as entirely separate object classes [20]. In the present work, we describe an approach to multi-class object detection which allows sharing parts between different viewpoints and several classes while also learning a dense representation for the entire viewsphere of each class. We describe three learning approaches with different part sharing strategies in order to reduce the computational complexity of the learnt representation. Our approach uses synthetic training data to achieve a dense viewsphere coverage which also allows to perform object class and 3D pose estimation on single images.
AC2012-3303: REMOTEEXPERIMENTATIONFORCOMMUNICATION: FROM REMOTE DESKTOPS TO GATEWAYS

With continuous development of information technology and mature communication channels through the Internet, remote labs may potentially overcome many constrains faced in hands-on laboratories (e.g., minimizing expenses, providing flexible lab schedule, and sharing limited resources among multiple students). This paper presents a comparative study among three different remote lab implementation options, which include all the unique solutions used in simple-to-sophisticated system development. The testbed is eComLab, a radio-communication remote laboratory system developed by the authors; it has been used in several courses at the department of Electrical and Computer Engineering, the University of Texas at San Antonio (UTSA). The implementation options are discussed as various evolutional stages of the eComLab, which evolved from a simple single-user remote desktop application to a multi-user system architecture that operates as a gateway connecting four remote sides: (1) PC-controlled experiment, (2) users such as students, (3) instructors and (4) system administrators. In its latest version, users may utilize a regular web-browser to access remote experiments. The system provides various handy functions and enables group work capabilities. The paper also provides intervention data in the classroom through the analysis of surveys collected among students.
A case study on argumentation based teaching

This qualitative single holistic case study is the analysis of students’ views and experiences during the argumentation based teaching process which can potentially provide insights on developing further argumentation based teaching designs. The participants of this study were fourteen students at 11th grade at a state school in the Marmara Region. The data were gathered through semi-structured interview form and teaching journal. In this study, it was determined that the opinions of participants about argumentation based teaching were generally positive. They mainly described argumentation based learning as fun and effective and that it enabled them to learn without having to memorize. On the other hand, the inadequacey of the duration of the lessons and some conflicts among the students have been noted as the negative aspects by the participants.This qualitative single holistic case study is the analysis of students’ views and experiences during the argumentation based teaching process which can potentially provide insights on developing further argumentation based teaching designs. The participants of this study were fourteen students at 11th grade at a state school in the Marmara Region. The data were gathered through semi-structured interview form and teaching journal. In this study, it was determined that the opinions of participants about argumentation based teaching were generally positive. They mainly described argumentation based learning as fun and effective and that it enabled them to learn without having to memorize. On the other hand, the inadequacey of the duration of the lessons and some conflicts among the students have been noted as the negative aspects by the participants.
Encoding Human Motion for Automated Activity Recognition in Surveillance Applications

As computing becomes ubiquitous in our modern society, automated recognition of human activities emerges as a crucial topic where it can be applied to many real-life human-centric scenarios such as smart automated surveillance, human computer interaction and automated refereeing. Although the perception of activities is spontaneous for the human visual system, it has proven to be extraordinarily difficult to duplicate this capability into computer vision systems for automated understanding of human behavior. Motion pictures provide even richer and reliable information for the perception of the different biological, social and psychological characteristics of the person such as emotions, actions and personality traits of the subject. In spite of the fact that there is a considerable body of work devoted to human action recognition, most of the methods are evaluated on datasets recorded in simplified settings. More recent research has shifted focus to natural activity recognition in unconstrained scenes with more complex settings. Encoding Human Motion for Automated Activity Recognition in Surveillance Applications
A Short Review on Structural Equation Modeling:Applications and Future Research Directions

The application of multivariate techniques is mainly to expand the researchers' explanatory ability and statistical efficiency. The first generation analytical techniques share a common limitation i.e. each technique can examine only a single relationship at a time. Structural Equation Modeling, an extension of several multivariate techniques is the technique popularly used today can examine a series of dependence relationships simultaneously. The purpose of this study is to provide a short review on Structural Equation Modeling (SEM) being used in social sciences research. A comprehensive literature review of article appearing in top journals is conducted in order to identify how often SEM theory is used. Also the key SEM steps have been provided offering potential researchers with a theoretical supported systematic approach that simplify the multiple options with performing SEM.
Locally optimal control under unknown dynamics with learnt cost function: application to industrial robot positioning

Recent methods of Reinforcement Learning have enabled to solve difficult, high dimensional, robotic tasks under unknown dynamics using iterative Linear Quadratic Gaussian control theory. These algorithms are based on building a local time-varying linear model of the dynamics from data gathered through interaction with the environment. In such tasks, the cost function is often expressed directly in terms of the state and control variables so that it can be locally quadratized to run the algorithm. If the cost is expressed in terms of other variables, a model is required to compute the cost function from the variables manipulated. We propose a method to learn the cost function directly from the data, in the same way as for the dynamics. This way, the cost function can be defined in terms of any measurable quantity and thus can be chosen more appropriately for the task to be carried out. With our method, any sensor information can be used to design the cost function. We demonstrate the efficiency of this method through simulating, with the V-REP software, the learning of a Cartesian positioning task on several industrial robots with different characteristics. The robots are controlled in joint space and no model is provided a priori. Our results are compared with another model free technique, consisting in writing the cost function as a state variable.
Weighted batch means estimators in Markov chain Monte Carlo.

This paper proposes a family of weighted batch means variance estimators, which are computationally efficient and can be conveniently applied in practice. The focus is on Markov chain Monte Carlo simulations and estimation of the asymptotic covariance matrix in the Markov chain central limit theorem, where conditions ensuring strong consistency are provided. Finite sample performance is evaluated through auto-regressive, Bayesian spatial-temporal, and Bayesian logistic regression examples, where the new estimators show significant computational gains with a minor sacrifice in variance compared with existing methods.
Optimal Linear Income Tax When Highly-Skilled Individuals Vote with Their Feet

This paper examines how allowing individuals to emigrate to pay lower taxes changes the optimal non-linear income tax scheme in a Mirrleesian economy. Type-dependent participation constraints are borrowed from contract theory. An individual emigrates if his domestic utility is less than his utility abroad net of migration costs, utilities and costs both depending on productivity. Three social criteria are distinguished according to the agents whose welfare matters. Mobility significantly alters the closed-economy results qualitatively, but also quantitatively as veri.ed by simulations. A curse of the middle-skilled occurs in the first-best. In the second-best, the middle-skilled can support the highest average tax rates and the marginal tax rates can be negative. Moreover, preventing emigration of the highly-skilled is not necessarily optimal.
REAKTUALISASI PENDIDIKAN KARAKTER PESANTREN DI ERA GLOBALISASI

Tulisan ini mendeskripsikan pendidikan karakter mulai dari definisi dan implementasinya dari berbagai tokoh. Menjelaskan peran pendidikan karakter dan pentingnya pendidikan karakter terutama di era globalisasi ini. Tulisan ini juga mendeskripsikan keadaan pesantren saat ini dan perbandingannya dengan pesantren yang dulu mulai dari tantangan dan penanggulangannya. Menggambarkan pola pendidikan karakter di pesantren dan pentingnya pesantren mengoptimalkan pendidikan karakter untuk mencetak santri yang berkipribadian baik baik ketika masih di dalam pesantren ataupun ketika sudah di luar pesantren.
KMC-2: an X-ray beamline with dedicated diffraction and XAS endstations at BESSY II

The KMC-2 beamline is dedicated to provide X-ray radiation with high energy stability and resolution. The experimental setup is optimized towards offering a wide range of methods and sample environments. Two permanent endstations can be used in alternation. DIFFRACTION is a flexible multi-purpose diffractometer, based on a Huber six circle diffractometer in psi geometry. XANES provides the possibility for EXAFS, XANES and X-ray fluorescence measurements at-air.
A modelling framework for model based risk analysis

Traditionally, risk analysis projects use a document based approach: the description of the system and the result of the analysis are expressed in a textual way or in drawings without an explicit semantics. The consistency and the relationships between documents are difficult to assess and it is rather difficult to extract and manipulate needed information for validation or for another purposes, or to capitalize knowledge. An approach to overcome these limitations is to use a model based approach for risk analysis. This would allow to represent knowledge in a consistent manner, easy to manipulate and to transform In this work, we present an approach for model based risk analysis, which can be used with any classical methods such as PHA, HAZOP and FMEA.
Simultaneous Variable and Feature Group Selection in Heterogeneous Learning: Optimization and Applications

Advances in data collection technologies have made it cost-effective to obtain heterogeneous data from multiple data sources. Very often, the data are of very high dimension and feature selection is preferred in order to reduce noise, save computational cost and learn interpretable models. Due to the multi-modality nature of heterogeneous data, it is interesting to design efficient machine learning models that are capable of performing variable selection and feature group (data source) selection simultaneously (a.k.a bi-level selection). In this thesis, I carry out research along this direction with a particular focus on designing efficient optimization algorithms. I start with a unified bi-level learning model that contains several existing feature selection models as special cases. Then the proposed model is further extended to tackle the block-wise missing data, one of the major challenges in the diagnosis of Alzheimers Disease (AD). Moreover, I propose a novel interpretable sparse group feature selection model that greatly facilitates the procedure of parameter tuning and model selection. Last but not least, I show that by solving the sparse group hard thresholding problem directly, the sparse group feature selection model can be further improved in terms of both algorithmic complexity and efficiency. Promising results are demonstrated in the extensive evaluation on multiple real-world data sets.
TV Program Recommender System Using Viewing Time Patterns

Abstract As a number of TV programs broadcast today, researches about TV program recommender system have been studied and many researchers have been studying recommende r system to produce recommendation with high accuracy. Recommender system recommends TV program to user by using metadata like genre, plot or calculating users’ preferences about TV programs. In th is paper, we propose a new TV program Collaborative Filtering Recommender System that exploits viewin g time pattern like viewing ratio, relation with finish time and recently viewing history to calculate prefe rence for high-quality of recommendation. To verify usefulness of our research, we also compare our method wh ich utilizes viewing time patterns and baseline which simply recommends TV program of user's most freq uently watched channel. Through this ex-periments, we show that our method very effectively works and r ecommendation performance increases.Key Words : TV Program, Recommender System, Viewing time Patterns, Collaborative Filtering Received: Mar. 22, 2015Revised : Apr. 5, 2015Accepted: Sep. 15, 2015
Synchronous dataflow models for Halide programming environment

Image processing algorithms are time-consuming in general. In domains like healthcare, image processing algorithms are required to run in real-time. Efficient image processing is very important to assure the correct functionality of such machines. With the advent of multi-core technology, there have been technologies to perform the processing in parallel in different cores to reduce the computation time and improve efficiency. The crucial question here is scheduling decisions to map the processing stages to cores that optimises the efficiency of the image processing. Halide programming environment is a state-of-the-art technology to perform scheduling of image processing tasks to the processing cores. This thesis focuses on analyzing the performance (i.e., throughput) of the schedules of Halide programs. Dataflow is a well-known formalism for throughput analysis of imaging pipelines. Performance analysis of obtained models is much faster than doing the same analysis on the real platform. Utilizing such model-based analysis speeds up the exploration of the optimal schedules. In this work, we analytically obtain dataflow models from Halide programs and generate dataflow models automatically when input image size or schedule changes. We analyse the performance of Halide programs with different schedules by using dataflow models and obtain dataflow models for two-stage pipelined algorithms. We show that the performance analysed by dataflow models is close to reality. We measure the performance of different schedules on the real platform and obtain the same results from dataflow analysis when comparing the relative performance of schedules. Synchronous dataflow models for Halide programming environment iii
Automatic barcode recognition method based on adaptive edge detection and a mapping model

Abstract. An adaptive edge detection and mapping (AEDM) algorithm to address the challenging one-dimensional barcode recognition task with the existence of both image degradation and barcode shape deformation is presented. AEDM is an edge detection-based method that has three consecutive phases. The first phase extracts the scan lines from a cropped image. The second phase involves detecting the edge points in a scan line. The edge positions are assumed to be the intersecting points between a scan line and a corresponding well-designed reference line. The third phase involves adjusting the preliminary edge positions to more reasonable positions by employing prior information of the coding rules. Thus, a universal edge mapping model is established to obtain the coding positions of each edge in this phase, followed by a decoding procedure. The Levenberg–Marquardt method is utilized to solve this nonlinear model. The computational complexity and convergence analysis of AEDM are also provided. Several experiments were implemented to evaluate the performance of AEDM algorithm. The results indicate that the efficient AEDM algorithm outperforms state-of-the-art methods and adequately addresses multiple issues, such as out-of-focus blur, nonlinear distortion, noise, nonlinear optical illumination, and situations that involve the combinations of these issues.
A Critical Review of Models and Theories in Field of Individual Acceptance of Technology

Information systems and technology have become essential tools for organizations to achieve their goals. Therefore, many technology acceptance theories and models have been developed in the field. Research showed a number of models that introduce the factors which make information systems successful. Based on the theoretical lens rooted in the literature, the researchers critically reviewed ten models and theories on individual acceptance of technology. The review identifies the major models to make explicit assumptions. The present study tries to provide a summary of strengths and weaknesses that have been discussed about these models and theories.
A Review of ‘Brain Computation as Hierarchical Abstraction’

Abstract This article is a review of the book ‘Brain Computation As Hierarchical Abstraction’ by Dana H. Ballard published by MIT press in 2015. The book series computational neuroscience familiarizes the reader with the computational aspects of brain functions based on neuroscientific evidence. It provides an excellent introduction of the functioning, i.e. the structure, the network and the routines of the brain in our daily life. The final chapters even discuss behavioral elements such as decision-making, emotions and consciousness. These topics are of high relevance in other sciences such as economics and philosophy. Overall, Ballard’s book stimulates a scientifically well-founded debate and, more importantly, reveals the need of an interdisciplinary dialogue towards social sciences.
Improved algorithm for no-reference quality assessment of blurred image

A fast and effective quality assessment algorithm of no-reference blurred image based on improving the classic Repeat blur( Reblur) processing algorithm was proposed for the high computational cost in traditional methods. The proposed algorithm took into account the human visual system, selected the image blocks that human was interested in instead of the entire image using the local variance, constructed blurred image blocks through low-pass filter, calculated the difference of the adjacent pixels between the original and the blurred image blocks to obtain the original image objective quality evaluation parameters. The simulation results show that compared to the traditional method, the proposed algorithm is more consistent with the subjective evaluation results with the Pearson correlation coefficient increasing 0. 01 and less complex with half running time.