Miracle-dz/UniFAR-FTUs huggingface dataset:
Scheduling flexible job shop problem subject to machine breakdown with game theory

Flexible job shop scheduling problem subject to machine breakdown is one of the challenging problems in manufacturing field. In this problem, robustness and stability are the two important measures to evaluate the quality of rescheduling. Considering these two objectives cannot be optimised simultaneously and the equilibrium of two objectives has game procedure, non-cooperative game theory with complete information is applied to build new scheduling model. In this game model, the two objectives can be seen as the two sides which take part in the game. Firstly, pre-scheduling without considering machine failure is generated, and then the operations affected by machine failure are obtained according to time at which machine breakdown happens. The decision made by each side should obtain better robust and stable performance. To achieve the optimal objects, Nash Equilibrium (NE) solution for each side is achieved. Considering NE solution may not exist or there are more than one NE solutions, concepts of ideal NE solution and near NE solution are proposed. To avoid huge decision searching space when the number of operations is more than two, an NE searching algorithm under multi-stage game based on the two concepts is also proposed to find NE or near NE solutions. In order to prove the feasibility and effectiveness of the method, classical problems are employed with random breakdown. The computational results show the feasibility and effectiveness of the new model and the new approaches.
Pobreza extrema e seus tríplices fundamentos: profundidade, persistência e multiplicidade

O objetivo deste artigo e contribuir com o debate conceitual sobre pobreza extrema e propor a incorporacao de novos elementos para a compreensao desse complexo fenomeno. A inexistencia de clareza sobre o que e e como se mede pobreza extrema e um fato que continua a desafiar os pesquisadores. Alem disso, e apesar dos avancos, as contribuicoes disponiveis, na sua maioria, nao contemplam o processo conhecido como razao publica. Este artigo apresenta um diferencial neste aspecto, combinando elementos do debate corrente na literatura com resultados de uma pesquisa de campo que, dentre outras inovacoes, definiu as dimensoes da pobreza a serem investigas ouvindo os pobres. Os resultados mostram que pobreza extrema esta muito longe de ser apenas uma questao de intensidade na privacao do acesso a recursos para sobrevivencia. A pobreza extrema congrega caracteristicas de intensidade, persistencia e multiplicidade. Os resultados econometricos evidenciaram que o capital social, renda e escolaridade sao as dimensoes mais importantes para caracterizar os pobres extremos. Extreme poverty and its triple foundation: depth, persistence and multiplicity Abstract: The paper aims to contribute to the discussion on extreme poverty conceptualization and measurement of its dimensions and manifestations. It is widely known that there is no agreement about the conceptualization of extreme poverty. The literature goes from the traditional measures which are based on lack of income to the more sophisticated definitions using deprivation of basic capabilities and fundamental human rights. However, the majority of the concepts available in the literature are based on ideas and knowledge of the researchers and experts without any participation from the poor and extremely poor people. Our contribution in this paper is to put together the evolving debate from the literature and the results from a survey carried out in Porto Alegre (Brazil) during 2007. The results from the two stage questionnaire shows that that extreme poverty is far more complex than the lack of basic resources to survive, and the majority of poor people in Porto Alegre faced some of the extreme poverty characteristics during their life. The econometric results point out social capital, income and schooling as important variables to explain extreme poverty. Key-words: extreme poverty, capability, multidimensionality. JEL: I32; I39.
Research on dynamic feedback and elastic scheduling model and algorithm for flexible workload

Soft real-time applications, such as multimedia systems, are becoming increasingly unpredictable due to the variation of the tasks' attributes. Under these circumstances, the scheduling algorithms depending on the tasks' static attributes can't provide useful and efficient scheduling support to those soft real-time systems. In this paper, we present a dynamic feedback and elastic scheduling model and period adjustment algorithm for soft real-time application with flexible workload. Based on logging the number of task instance that miss deadline periodically, this model adjusts the tasks' executing period to change the resource requirement in the next sampling period. This model makes the system state converge to the steady state when the system is overloaded, and makes tasks to use system resource sufficiently when system resource isn't fully utilized. We analyse the model and evaluate its performance.
Objective Video Quality Method Based on Mutual Information and Human Visual System

In this paper we present the objective video quality metric based on mutual information and Human Visual System. The calculation of proposed metric consists of two stages. In the first stage of quality evaluation whole original and test sequence are pre-processed by the Human Visual System. In the second stage we calculate mutual information which has been utilized as the quality evaluation criteria. The mutual information was calculated between the frame from original sequence and the corresponding frame from test sequence. For this testing purpose we choose Foreman video at CIF resolution. To prove reliability of our metric were compared it with some commonly used objective methods for measuring the video quality. The results show that presented objective video quality metric based on mutual information and Human Visual System provides relevant results in comparison with results of other objective methods so it is suitable candidate for measuring the video quality.
Geometric Approaches to Statistical DefectPrediction and Learning

Software quality is directly correlated with the number of defects in software systems. As the complexity of software increases, manual inspection of software becomes prohibitively expensive. Thus, defect prediction is of paramount importance to project managers in allocating the limited resources effectively as well as providing many advantages such as the accurate estimation of project costs and schedules. This thesis addresses the issues of defect prediction and learning in the geometric framework using statistical quality control and genetic algorithms. A software defect prediction model using the geometric concept of operating characteristic curves is proposed. The main idea behind this predictor is to use geometric insight in helping construct an efficient prediction method to reliably predict the cumulative number of defects during the software development process. The performance of the proposed approach is validated on real data from actual software projects, and the experimental results demonstrate a much improved performance of the proposed statistical method in predicting defects. In the same vein, two defect learning predictors based on evolutionary algorithms are also proposed. These predictors use genetic programming as feature constructor method. The first predictor constructs new features based primarily on the geometrical characteristics of the original data. Then, an independent classifier is applied and the performance of feature selection method is measured. The second predictor uses a built-in classifier which automatically gets tuned for the constructed features. Experimental results on a NASA static metric dataset demonstrate the feasibility of the proposed genetic programming based approaches.
Modification of TCP SYN flood (DoS) attack detection algorithm

This work focuses onto proposal and implementation of modification of SYN flood (DoS) attack detection algorithm. Based on Counting Bloom filter, the attack detection algorithm is proposed and implemented into KaTaLyzer network traffic monitoring tool. TCP attacks can be detected and network administrator can be notified in real-time about ongoing attack by using different notification methods.
Automated Motivic Analysis via Melodic Clustering

In this paper a computational model will be presented that attempts to organise melodic segments into ‘significant’ musical categories (e.g., motives). Given a segmentation of a melodic surface, the proposed algorithm constructs an appropriate representation for each segment in terms of a number of attributes (these reflect melodic and rhythmic aspects of the segment at the surface and at various abstract levels) and then a clustering algorithm (the Unscramble algorithm) is applied for the organisation of these segments into ‘meaningful’ categories. The proposed clustering algorithm automatically determines an appropriate number of clusters and also the characteristic attributes of each category. As a test case this computational model has been used for obtaining a motivic analysis of Schumann's Träumerei and Debussy's Syrinx.
Interactive-activation as a framework for understanding morphological processing

Abstract A description is given of the main experiments that have been taken as support for the view that, in reading, a prefixed word is stripped of its prefix and lexically accessed on the basis of its stem. Since one of the most important of those experiments had been poorly executed, a new version of the same study is presented with results that are entirely consistent with the previous one. However, logical problems exist with the view that says that stems act as access codes used to gain access to the lexicon, the main ones having to do with the fact that a prefix store is required. As a result, an alternative model is favoured, namely, an interactive-activation model. Prefixed words are represented in decomposed form in this model, but no prelexical prefix-stripping is required. A detailed examination is made of the manner in which this framework is able to incorporate the previous empirical results, as well as other aspects of morphological processing.
Work Analysis of Compliant Leg Mechanisms for Bipedal Walking Robots

In this study, we analyse the work of bipedal walking robots with compliant feet. For this purpose, a walking model of bipedal robots with compliant feet is considered. The ligamentous structure of the human foot is used for the compliant feet mechanisms of the bipedal robot considered herein. A work principle is used for determining the corresponding work at the joint space of the compliant legs according to the reaction force which is propagated from the foot space. The usefulness of the work analysis is demonstrated through exemplary simulations. Consequently, it is shown that the work analysis can be used for evaluating the fatigue of the bipedal robot accumulated by the physical walking impact between the supporting foot and the contact surface. Furthermore, comfortable walking by means of footgear with a compliance is addressed.
Static and perfused phantom media for thermal property measurement techniques

The development of techniques for the measurement of thermal transport properties (thermal conductivity, thermal diffusivity, and, in the case of perfused biological tissue, the local blood flow) requires phantom media which are stable, well-characterized, and simulate the behavior of the systems to be measured over the expected range of property values. The authors describe two phantom media which have been developed and are used for calibration and evaluation of thermal property measurement techniques. The first is a static gel phantom of high thermal conductivity. The second is a low-flow filter bed used to simulate the convective transport processes occurring in the tissue microvasculature. <<ETX>>
NeuralParse a neural model for parsing natural language

Neural models for dealing with symbolic processing are in their infancy. Success thus far can be defined by the parsing of very simple phrases and a small set of words into small, fixed size frames. Many of these systems do not scale well as one increases the number of words or the phrase length. These models are limited with respect to the large number of epochs required to train and the error rates. We address the issue of training and present an analysis which provides a lower bound on the error rate. The approach investigates simple extensions to the basic learning algorithm and makes use of a closest neighbor algorithm for correctness.
Japanese Query Alteration Based on Lexical Semantic Similarity

We propose a unified approach to web search query alterations in Japanese that is not limited to particular character types or orthographic similarity between a query and its alteration candidate. Our model is based on previous work on English query correction, but makes some crucial improvements: (1) we augment the query-candidate list to include orthographically dissimilar but semantically similar pairs; and (2) we use kernel-based lexical semantic similarity to avoid the problem of data sparseness in computing query-candidate similarity. We also propose an efficient method for generating query-candidate pairs for model training and testing. We show that the proposed method achieves about 80% accuracy on the query alteration task, improving over previously proposed methods that use semantic similarity.
A Model Predictive Control Algorithm Applied To Non‐Linear Processes

Model predictive control (MPC) is an optimization‐based approach that has been successfully applied to a wide variety of control problems. In most of nonlinear strategies, the controllers are based on linear models with fixed parameters so that the vast body of linear control theory can be applied. Other solutions include the use of a nonlinear analytical model, combinations of linear empirical models, etc. This paper presents an MPC algorithm which uses on‐line simulation and rule‐based control. (© 2006 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Automatic analysis of attack data from distributed honeypot network

There are many ways of getting real data about malicious activity in a network. One of them relies on masquerading monitoring servers as a production one. These servers are called honeypots and data about attacks on them brings us valuable information about actual attacks and techniques used by hackers. The article describes distributed topology of honeypots, which was developed with a strong orientation on monitoring of IP telephony traffic. IP telephony servers can be easily exposed to various types of attacks, and without protection, this situation can lead to loss of money and other unpleasant consequences. Using a distributed topology with honeypots placed in different geological locations and networks provides more valuable and independent results. With automatic system of gathering information from all honeypots, it is possible to work with all information on one centralized point. Communication between honeypots and centralized data store use secure SSH tunnels and server communicates only with authorized honeypots. The centralized server also automatically analyses data from each honeypot. Results of this analysis and also other statistical data about malicious activity are simply accessible through a built-in web server. All statistical and analysis reports serve as information basis for an algorithm which classifies different types of used VoIP attacks. The web interface then brings a tool for quick comparison and evaluation of actual attacks in all monitored networks. The article describes both, the honeypots nodes in distributed architecture, which monitor suspicious activity, and also methods and algorithms used on the server side for analysis of gathered data.
A Protection Method using Destination Address Packet Sampling for SYN Flooding Attack in SDN Environments

SDN(Software Defined Networking) has been considered as a new future computer network architecture and DDoS(Distributed Denial of Service) is the biggest threat in the network security. In SDN architecture, we present the technique to defend the DDoS SYN Flooding attack that is one of the DDoS attack method. First, we monitor the Backlog queue in order to reduce the unnecessary monitoring resources. If the Backlog queue of the certain server is occupied over 70%, the sFlow performs packet sampling with the server address as the destination address. To distinguish between the attacker and the normal user, we use the source address. We decide the SYN packet threshold using the remaining Backlog queue that possible to allow the number of connections. If certain sources address send the SYN packet over the threshold, we judge that this address is attacker. The controller will modify the flow table entry to block attack traffics. By using this method, we reduce the resource consumption about the unnecessary monitoring and the protection range is expanded to all switches. The result achieved from our experiment show that we can prevent the SYN Flooding attack before the Backlog queue is fully occupied.
An Activity-Based Cellular Automaton Model to Simulate Land-Use Dynamics

In recent decades several methods have been proposed to simulate land-use changes in a spatially explicit way. In these models land is generally represented on a lattice with cell states indicating the predominant land use. Since a cell can have only one state, mixed land uses and different densities of one land use can only be introduced superficially, as separate cell states. In this paper we describe a cellular automata model that simulates dynamics in both land uses and activities, where activities represent quantitative information, such as the number of inhabitants at a location. Therefore each cell has associated with it (1) a value representing one of a finite set of land-use classes, and (2) a vector of numerical values representing the quantity of each modelled activity that is present at that location. This allows simulation of incremental changes as well as mixed land uses. The proposed model is tested with a synthetic application that uses two activities: population and jobs. It simulates the emergence of human settlements over time from local interactions between activities and land uses. Assessment of results indicates that the model generates realistic urbanization patterns.
Unified walking control for a biped robot using neural networks

A control scheme for handling variable speed gaits was implemented on an experimental biped. The control scheme used adaptation of pre-planned motion sequences in combination with closed loop reactive control. CMAC neural networks were responsible for the adaptive control of motion sequences. The biped was able to walk with variable speed gaits, and to change gait speeds on the fly. The slower gait speeds required statically balanced walking, while the faster speeds required dynamically balanced walking. It was not necessary to distinguish between the two balance modes within the controller. Following training, the biped was able to walk on flat, nonslippery surfaces at forward velocities in the range of 21 cm/min to 72 cm/min, with an average stride length of 6.5 cm.
A New Software Estimating Framework: 25 Years and Thousands of Projects Later

1,2 Abstract—It’s about time we in the software development community revisit the assumptions, relationships, and flexibility contained in our currently-available software estimating models. Most of the current models still implement fundamental relationships that are based on 25 year old data and assumptions. In the meantime, data from many thousands of projects have since been collected and offer an opportunity to revisit old assumptions and relationships. This paper documents the basis, assumptions, and derivations behind a set of general software effort, duration, and defects estimating relationships that are based on the notion that software construction is the application of effort (labor) over some duration (period of elapsed calendar time) that produces a desired software product (size) and unwanted byproducts (defects). This set of relationships is derived from three evidently-good correlations: 1) effort generally increases with increasing size, 2) duration generally increases with increasing size; 3) defects generally increase with increasing team size, and 4) productivity decreases with increasing team size. This derivation yields two limited tradeoff relationships: one between effort and duration and one between defects and duration.
Evaluating the quality of interaction in asynchronous discussion forums in fully online courses

Fully online courses are becoming progressively more popular because of their “anytime anywhere” learning flexibility. One of the ways students interact with each other and with the instructors within fully online learning environments is via asynchronous discussion forums. However, student engagement in online discussion forums does not always take place automatically and there is a lack of clarity about the ideal role of the instructors in them. In this article, we report on our research on the quality of discussion in fully online courses through analysis of discussion forum activities. We have conducted our research on two large fully online subjects for computing students over two consecutive semesters and used a grounded theoretic approach for data analysis. Our results reveal what students and instructors consider as quality interaction in fully online courses. We also propose two frameworks based on our findings that can be used to ensure effective online interaction.
Optimal control of noncollaborative servers in two‐stage tandem queueing systems

We consider two‐stage tandem queueing systems with dedicated servers in each station and a flexible server that is trained to serve both stations. We assume no arrivals, exponential service times, and linear holding costs for jobs present in the system. We study the optimal dynamic assignment of servers to jobs assuming a noncollaborative work discipline with idling and preemptions allowed. For larger holding costs in the first station, we show that (i) nonidling policies are optimal and (ii) if the flexible server is not faster than the dedicated servers, the optimal server allocation strategy has a threshold‐type structure. For all other cases, we provide numerical results that support the optimality of threshold‐type policies. Our numerical experiments also indicate that when the flexible server is faster than the dedicated server of the second station, the optimal policy may have counterintuitive properties, which is not the case when a collaborative service discipline is assumed. © 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 435–446, 2014
A new approach for evaluating the virtual education of students using association rule-mining in cloud computing environments

Nowadays, most universities and organizations use various computer systems, operating systems, mobile devices and databases with different infrastructure, hardware and software architectures. Many internet-based systems and advanced distributed environments have currently been developed in adapting to heterogonous systems. Cloud and grid computing are the new developed environments that present required services of users. In virtual education system, learners use different systems and facilities in various geographical locations. Therefore, evaluating the quality of education of learners is sophisticate. To this issue, we have developed a new approach for analyzing and extracting useful rules based on distributed heterogonous environments. For this research, we have implemented our proposed approach based on simulated grid computing environment. The obtained results confirm that the extracted rules based on distributed information can be useful to personalize education system for learners based on their characteristics and locations. This new architecture is powerful and rapid in comparison with centralized architectures. Keywords: cloud computing, association rule mining, virtual education;