Miracle-dz/UniFAR-FTUs huggingface dataset:
Training Bayesian networks for realistic man-machine spoken dialogue simulation

Data collection and annotation are generally required to design or assess spoken dialogue systems. Yet, this is a very time consuming and expensive process. For these reasons, user simulation has become an important trend of research in the field of spoken dialogue systems. The general problem of user simulation is thus to produce as many as necessary natural, various and consistent interactions from as few data as possible. In this paper, we propose a user simulation method based on Bayesian networks (BN) that is able to produce consistent interactions in terms of user goal and dialogue history. The model as been introduced in previous work but parameters were hand-tuned and it was assessed in the framework of automatic learning of optimal dialogue strategies. In this paper, the BN is trained on a database of 1234 human-machine dialogues in the TownInfo domain (a tourist information application). Experiments with a state-of-the-art dialogue system (REALL-DUDE/DIPPER/OAA) have been realized and results in terms of dialog statistics are presented.
Advanced Methodology for Requirements Engineering Technique Solution (AMRETS)

One of the big issues of software development project is overspent and delays. Thus added to the equation is unsuficient quality of expected deliverables due to bad selection of requirements engineering techniques. There has been a method introduced for selecting requiremnets engineering techniques based on the given project attributes. But it produced a list of techniques, which left the engineers to figure out which set of techniques that should be selected to ensure that the proper deliverables can be produced. This paper proposed a solution of the existing method by adding deliverable parameter to the project attribute and implement single solution, that reduce the task of selecting the proper set of requirements engineering techniques. The new method, Advance MRETS (AMRETS) shows a promising results. It reduces the number of recommended techniques list from the previous method into a single alternative set of techniques. It ensures that the single alternative produces all required deliverables and minimizes the number of unnecessary deliverables. This method helps the project manager to do a better planning, especially for the requirements phase.
Research and Design of Online System Based on Subjective Questions Automatic Scoring

The examinations of college public courses are great challenges for teachers who are responsible for large numbers of college students. With the traditional examination model, teachers need to set questions, set types, invigilate, correct, record scores and add up the score. The complex and various work costs them a lot of energy and spending. Online examination system is a system based on web technology that expands the flexibility of the examination and ensures the safety of the answers. However, at present, the questions in the online examination system are too single and they also need to be graded by teachers manually. Hence, this paper aims to develop an automatic scoring system for online subjective questions in the basic courses tests. Based on the research of the automatic grading technology at home and abroad, the online examination system realize automatically grading function of subjective questions, combined with Chinese word segmentation technology, keyword extraction algorithm, the statement in the matching processing algorithm, degree theory in fuzzy mathematics thought and algorithm analysis. This research simulates the artificial evaluation steps, and detailing the evaluation process step by step. According to the particularity of natural language, it establishes a synonym thesaurus for keyword matching in order to improve the accuracy of matching. Finally combing the close of the sentence, it calculates score by putting keywords as a scoring point, and the system is feasible through testing.
The Building of Sports E-Education Platform using Multimedia Technology

In this paper, we developed an E-learning system for Sport Education which is based on the concept of ADDIE model and fabricated the teaching materials with the multimedia factors video, voice and animation. Users can view the sports action clip with different angle views for the same action. He/she can only control the one seek bar of any Flash media players of the user interface, and any one of Flash media players will display the synchronic actions. For students, the learning contents that our system supplies is not only used to improve the sports skill but also help them to establish the cognition of sports morality.
PEER EDITING IN THE PROCESS OF LEARNING WRITING DONE BY THE FOURTH SEMESTER STUDENTS AT THE ENGLISH DEPARTMENT OF MUHAMMADIYAH UNIVERSITY

The general opinion of teaching writing is that it is much more difficult than teaching speaking. One of the problems faced by both EFL learners and teachers is that students do not always understand or pay attention to the content of the teacher’s response to their written work. One of the techniques applied by the writing teachers is peer editing, in which the students are involved in the process of correcting their works. This study is aimed at describing the role of peer editing in the process of learning writing at college level. This study belongs to descriptive quantitative study. The instruments used are observations and the students’ questionnaires about the value of peer editing in the process of learning writing. This study was conducted at the English Department of Muhammadiyah University of Malang from June 11th-27th, 2003. The samples, which consist of 18 students, were drawn from the all population of class IV B-2 at the English Department, in which peer editing was held. This study shows that the students respond positively to the peer editing in the writing class and they also regard it very valuable. 89% of them agree to the implementation of the peer editing in their writing class (43% out of 18 students or 6 students find peer editing ‘very valuable’ and 46% of them or 7 students find it ‘valuable’. So, it can be concluded that peer editing can be used as an alternative way to teach writing, when the teacher uses the proper techniques and the students respond it very well. Based on the result of the study, it is suggested that the future researcher needs to conduct an experimental study focused on the effect of peer editing on the students achievement in writing. It is also suggested that the future researcher enlarges the scope of the study by adding some more aspects of writing, such as the grammar use and vocabulary, and involve a greater sample of students.
COMPUTATION IMPROVEMENT OF STOCKMARKET DECISION MAKING MODEL THROUGHTHE APPLICATION OF GRID

The paper is focused on the presentation of decision - making model for stock markets. As well paper introduces grid computation idea and discusses the possibilities of grid technology application for the optimization of computational power related to the realization of the proposed model. The introduced decision - making model is based on the application of artificial intelligence tools: artificial neural networks (ANN) and particle swarmoptimization algorithm(PSO). While making computations with the proposed decision - making model the decision computation process takes quite a lot of time. Working with stock markets it is very important to make decisions fast. One of the possibilities to do that is to apply grid technologies for the distributed computing that involves coordinating and sharing computing, application, data, storage, or network resources across dynamic and geographically dispersed organizations.
Cluster polygonization and qualitative cluster reasoning: overview

Clustering groups similar objects and separates dissimilar objects. It has been widely used in many geospatial data mining applications. It reports aggregations and concentrations in large databases. There has been a lot of research in clustering, but relatively less attention has been paid to what contributed to those clusters. This article overviews qualitative reasoning techniques for clusters, and reviews qualitative cluster reasoning framework.
Ensemble Learning Method for Class Overlapping Problem

Class overlapping and ensemble learning method have long been regarded as one of the toughest yet pervasive problems in data mining. The effect of ensemble learning method and class overlapping are with few researches. To meet this critical challenge, in this paper, we pay a systematic study on the different classifier performance in class overlapping problem and propose a new Ensemble Learning Method based on Naive Bayes for class overlapping problem (ELM-NB). ELM-NB uses NB to find class overlapping region and use this region and non-overlapping region in ensemble learning model. Experimental results on selected data sets prove that ELM-NB can have good classification performance for class overlapping problem.
Effective content vocabulary instruction in the middle: Matching students, purposes, words, and strategies Effective content vocabulary instruction in the middle: Matching students, purposes, words, and strategies

Despite what we have learned about effective vocabulary instruction, and despite the fact that many teachers know vocabulary is important, teachers do not always incorporate best practices with their own instruction. These authors argue that part of the reason may be that the general instructional recommendations of vocabulary research don't always help teachers, particularly content teachers, bridge theory and practice. The issues in teaching vocabulary are often specific to the content, the students, and the instructional purposes. To help teachers address questions of which words to teach, which strategies to use, and how long to spend on teaching them, the authors propose a “four-level” instructional framework tailored to content area vocabulary. Using this framework, the authors show content teachers how to choose words and methods that match their students and their purposes.
Optimal Planar Array Architecture for Full‐Dimensional Multi‐user Multiple‐Input Multiple‐Output with Elevation Modeling

Research interest in three‐dimensional multiple‐input multiple‐output (3D‐MIMO) beamforming has rapidly increased on account of its potential to support high data rates through an array of strategies, including sector or user‐specific elevation beamforming and cell‐splitting. To evaluate the full performance benefits of 3D and full‐dimensional (FD) MIMO beamforming, the 3D character of the real MIMO channel must be modeled with consideration of both the azimuth and elevation domain. Most existing works on the 2D spatial channel model (2D‐SCM) assume a wide range for the distribution of elevation angles of departure (eAoDs), which is not practical according to field measurements. In this paper, an optimal FD‐MIMO planar array configuration is presented for different practical channel conditions by restricting the eAoDs to a finite range. Using a dynamic network level simulator that employs a complete 3D SCM, we analyze the relationship between the angular spread and sum throughput. In addition, we present an analysis on the optimal antenna configurations for the channels under consideration.
Multi-Channel Auditory Search: Toward Understanding Control Processes in Polychotic Auditory Listening

Two experiments are presented which develop a framework for exploring auditory information processing. The framework is referred to as polychotic listening or auditory search, and it requires a listener to aurally scan multiple simultaneous auditory streams for the appearance of a target. The first experiment examined the participant's ability to aurally scan between two and six simultaneous auditory streams of letter and digit names for the name of a target letter. The main independent variable was auditory load, or the number of active audio streams. The primary dependent variable was accuracy. Accuracy and sensitivity measures showed that as load increased, performance decreased. The second study required participants to practice the same task for ten sessions (1,800 trials). Results indicated that even with extensive practice, performance was still affected by auditory load. The relation between the present results and similar results in the visual search literature are discussed.
TALASH : A SEMANTIC AND CONTEXT BASED OPTIMIZED HINDI SEARCH ENGINE

The traditional search engine have shortcoming that they retrieve irrelevant information. Query expansion with relevant words increases the performance of search engines, but finding and using the relevant words is an open problem. This paper presents a Hindi search engine in which we describe three models for query enhancement. They are based on lexical variance, user context and combination of both techniques.
Ecology of staphylococci in a general medical service.

Investigation of the ecology of Staphylococcus aureus on the medical service of the Cincinnati General Hospital was carried out from 1964 to 1970. S. aureus was cultured from 1,442 patients. Overall, there was a progressive increase in the susceptibility of S. aureus to commonly used antibiotics, but not to penicillin. Hospital-acquired S. aureus remained highly resistant to all antibiotics except penicillinase-resistant penicillins. There was a progressive decline in the percentage of hospital-acquired infections from January 1964 to September 1969, followed by a rise during September 1969 to September 1970. No single bacteriophage group predominated among nosocomially acquired S. aureus. Major changes in the hospital environment did not appear to influence the prevalence of nasal carriage or hospital acquisition of S. aureus. This study identified the continuing problem of acquisition of S. aureus in the hospital, but no specific "epidemic strain. "
Fourier series learning of biped walking motion

A learning controller is presented for repetitive walking motion of biped robot. The learning control scheme learns the approximate inverse dynamics input of biped walking robot and uses the learned input pattern to generate an input profile of different walking motion. In the learning controller, the PID feedback controller takes part in stabilizing the transient response of robot dynamics while the feedforward learning controller plays a role in computing the desired actuator torques for feedforward nonlinear dynamics compensation in steady state. It is shown that all the error signals in the learning control system are bounded and the robot motion trajectory converges to the desired one asymptotically. The proposed learning control scheme is shown to be effective through a computer simulation with a 12-DOF biped robot.
PERFORMANCE LIMITS IN MULTI-CHANNEL NETWORKED CONTROL SYSTEM ARCHITECTURES

Abstract In networked control systems the communication links between controller and plant affect closed loop properties. To optimize performance, the communication medium should be used carefully. For that purpose, several configurations have been investigated. In the present work, we study a bit-rate limited multi-channel networked control system architecture, where communication is through parallel links. By adopting an additive quantization noise model, we characterize optimal coder-decoder pairs and show how to allocate individual bit-rates. The results obtained establish performance bounds for the multi-channel networked control system architecture.
Complexitor: An Educational Tool for Learning Algorithm Time Complexity in Practical Manner

Based on the informal survey, learning algorithm time complexity in a theoretical manner can be rather difficult to understand. Therefore, this research proposed Complexitor, an educational tool for learning algorithm time complexity in a practical manner. Students could learn how to determine algorithm time complexity through the actual execution of algorithm implementation. They were only required to provide algorithm implementation (i.e. source code written on a particularprogramming language) and test cases to learn time complexity. After input was given, Complexitor generated execution sequence based on test cases and determine its time complexity through Pearson correlation. An algorithm time complexity with the highest correlation value toward execution sequence was assigned as its result. Based on the evaluation, it can be concluded this mechanism is quite effective for determining time complexity as long as the distribution of given input set is balanced.
IMPLEMENTASI DETEKSI OUTLIER PADA ALGORITMA HIERARCHICAL CLUSTERING

Clustering memiliki permasalahan terhadap hasil yang dicapai apakah sudah sesuai dengan kebutuhan. Salah satu pilihan adalah agglomertive hierarchical clustering(AHC). Pada AHC ada dua hal penting yaitu pemilihan jenis pengukuran similarity dan teknik linkage. Pada beberapa kasus, penggunaan AHC yang berbasis single linkage menunjukkan hasil yang baik pada berbagai himpunan dataset dengan variasi jumlah dan bentuk. Namun, algoritma ini masih dapat menimbulkan masalah kepekaan pada adanya outlier dan fluktuasi kepadatan data. Juga tidak memungkinkan untuk menentukan jumlah klaster secara otomatis. Untuk mengatasi ini, diperlukan pendekatan untuk mendeteksi outlier dan secara otomatis menentukan jumlah klaster yang dibutuhkan secara tepat. Pendekatan ini merupakan pengembangan dari AHC yang dapat mendeteksi adanya outlier. Hasil uji coba menunjukkan bahwa modifikasi HCA lebih unggul dalam penanganan outlier.
Universidade Federal do Rio Grande do Norte Centro de Tecnologia Programa de Pós-Graduação em Engenharia Elétrica e de Computação

The predictive control technique has gotten, on the last years, greater number of adepts in reason of the easiness of adjustment of its parameters, of the exceeding of its concepts for multi-input/multi-output (MIMO) systems, of nonlinear models of processes could be linearised around a operating point, so can clearly be used in the controller, and mainly, as being the only methodology that can take into consideration, during the project of the controller, the limitations of the control signals and output of the process. The time varying weighting generalized predictive control (TGPC), studied in this work, is one more an alternative to the several existing predictive controls, characterizing itself as an modification of the generalized predictive control (GPC), where it is used a reference model, calculated in accordance with parameters of project previously established by the designer, and the application of a new function criterion, that when minimized offers the best parameters to the controller. It is used technique of the genetic algorithms to minimize of the function criterion proposed and searches to demonstrate the robustness of the TGPC through the application of performance, stability and robustness criterions. To compare achieves results of the TGPC controller, the GCP and proportional, integral and derivative (PID) controllers are used, where whole the techniques applied to stable, unstable and of non-minimum phase plants. The simulated examples become fulfilled with the use of MATLAB tool. It is verified that, the alterations implemented in TGPC, allow the evidence of the efficiency of this algorithm.
Stochastic Coverage and Connectivity in Heterogeneous Wireless Sensor Networks

A wireless sensor network (WSN) consists of several tiny battery-powered sensors that can communicate with each other to monitor a Field of Interest (FoI). Coverage is acknowledged as an important metric to measure the quality of service of WSNs. It specifies how well a FoI is monitored by the WSN. Network connectivity is complementary to coverage and it indicates how well the sensory data can be communicated by the sensors to the sink. This thesis studies coverage and connectivity problems in WSNs with stochastic deployment of sensors and demonstrates their applications. The main challenge in deployment of sensors is to make sure that a minimum number of sensors are used to provide the desired level of coverage and connectivity in the FoI. The first contribution of this thesis is to estimate the critical sensor density required for the desired coverage ratio under border effects in WSNs. This work can be used to accurately determine the sensors required in any convex polygon-shaped FoI. In some applications of WSNs, the sensors cannot be deployed directly inside the FoI to be monitored. For example, in canal water monitoring project, existing coverage solutions cannot be used because, the sensors cannot be deployed on the water surface. In the second contribution, we consider the sensors to be deployed uniformly at random outside the FoI near the boundary and estimate the minimum number of sensors required for desired level of coverage and connectivity. We also illustrate an application of this work to develop a traffic information acquisition system. The third contribution of this thesis is to consider a case when the sensors have only directional
A survey of android mobile platform security

Mobile devices have become high-value attacked targets since they contain numerous private or business information. Android, the most popular mobile computing platform, also known for its openness, is facing various threats from system vulnerabilities to malicious applications. After analyzing Android's security model and threat, we summarize the technic possibility for remote or physical attacks, privacy stealing, communication hijacking and malware C&C. According to these threatens, research progress in malware detection technologies and systems strengthening methods is also reviewed.
Fuzzifying Telecommunication Network Features Basing PSO-TE Algorithm

Planning Telecommunication Access Network (TAN) infrastructure is a real time problem that suffers from uncertainty and ambiguity. Fuzzy system is a discipline that proved its capability to deal with vague problems. Converting traditional data to suit fuzzy system calculations is called fuzzification process which is a crucial step affecting the whole system accuracy? Generating appropriate membership function for fuzzy variables is one of the most challenging issues in fuzzy systems design. This paper proposes a solution to generate membership function automatically. An integrated hybrid model (PSO-TE) is introduced which benefit the Particle Swarm Optimization (PSO) and the information theory measures (entropy and mutual information) as the fitness function to adjust particles (membership function parameters). The proposed algorithm is tested using realistic planning information to show its effectiveness and efficiency. During the comparison between the PSO-TE and the fuzzy C-mean algorithm, the proposed model proved its ability to produce stable membership function for the telecommunication data barriers.
A Novel Category Vector-Based Cross Language Text Categorization Method

Due to the globalization on the Web, monolingual Text Categorization can be reformulated as a cross language TC task. To establish a practical English-Chinese CLTC system, a feature translation method and a fast text categorization algorithm based on a novel Category Vector Space Model are proposed in this paper. Provided a Chinese-English bilingual dictionary in scientific and technological fields, parallel corpora was employed to append translation probability value to bilingual dictionary so as to disambiguate translation results. The experiment results show that the CLTC system which was established by method in the paper is practical and valuable. The performance of Cross-Language text categorization system exceeds that of Mono-Lingual text categorization system and the result is exciting.