Miracle-dz/UniFAR-FTUs huggingface dataset:
Parallelization of genetic algorithm with sexual selection

We propose a parallel genetic algorithm with sexual selection. In genetic algorithms with sexual selection with one population, females keep their traits around local optima by using a lower mutation rate than that of the males, while the males change their traits actively. When a runaway process takes place, the transitions of the males' traits are biased toward a certain direction which depends on the bias of the females' preferences. If the population size is large, the search converges quickly. However, the large population size causes a decrease in search performance. In the proposed method with parallelization, the population size of each subpopulation is maintained adequately, and each subpopulation seeks its own direction of evolution independently. As a result, the proposed method makes a search converge quickly because the runaway process which leads to intermittent evolution tends to take place more quickly than one population model. We applied the proposed method to some test problems. In these problems, while the performance of conventional genetic algorithms was decreased by parallelization, the proposed method exhibited better performance with parallelization. Moreover, the performance of the proposed method is better than that of conventional methods. This capability of parallelization is a remarkable characteristic of sexual selection. © 2004 Wiley Periodicals, Inc. Electr Eng Jpn, 150(1): 42–49, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20029
PRIVACY PRACTICES TOWARDS UBIQUITOUS SERVICES: A PROACTIVE APPROACH

While ubiquitous services (u-services) are likely to be recognized as the next trend in e-services, privacy issues are considered as a major obstacle to thrust uservices. This study endeavors to better understand how a general e-services provider’s (ESP) proactive privacy governance influences the delicate balance between customer privacy risk beliefs, and confidence and enticement beliefs, which is in turn to enhance customer disclosure willingness. Whereas most ESPs tend to detail their privacy practices online in response to consumers privacy concerns, ESPs can also take action to proactively interact with customers to mitigate the privacy concerns and ensure against privacy invasion. The proposed model was empirically tested using data collected from a survey of B2C setting. The findings and implications of this study to both researchers and practitioners are
Network calculus, deterministic effective bandwidth and VBR trunks

We use network calculus results to study some properties of lossless multiplexing. We define a general form of the deterministic effective bandwidth and equivalent capacity. We find that call acceptance regions based on deterministic criteria (loss or delay) are convex, in contrast to statistical cases where it is the complement of the region which is convex. We find that, in general, the limit of the call acceptance region based on statistical multiplexing when the loss probability target tends to 0 is strictly larger than the call acceptance region based on lossless multiplexing. We consider the problem of determining the optimal parameters of a variable bit rate (VBR) ATM connection when it is used as a trunk, or tunnel, given that the input traffic is known. We find that there is an optimal peak rate for the VBR trunk, independent of any optimization function. For a linear cost function, we find an explicit algorithm for the optimal remaining parameters of the VBR trunk. We expect these results to form the basis of a measurement based call admission control method over a VBR trunk.
Low vowels and transparency in Kinande vowel harmony

This paper addresses theoretical issues confronting cross-height harmony systems through an experimental study of Kinande, a Bantu language of the Democratic Republic of Congo. Using a combination of acoustic analysis and lingual ultrasound imaging, we evaluate previous proposals concerning the phonetic correlates of the harmonic vowel feature and the transparency of low vowels. Results indicate that (i) although a multivalued scalar acoustic feature in F1/F2 space is not adequate to distinguish all vowel categories in Kinande, the cross-height feature does correlate acoustically with F1, (ii) the cross-height feature of Kinande involves systematic tongue-root articulations and (iii) low vowels in Kinande are not neutral to harmony in the way reported in earlier work, but exhibit significant and systematic tongue-root advancement and retraction according to the dictates of harmony.
Arabic text classification using decision trees

Text mining draw more and more attention recently, it has been applied on different domains including web mining, opinion mining, and sentiment analysis. Text pre-processing is an important stage in text mining. The major obstacle in text mining is the very high dimensionality and the large size of text data. Natural language processing and morphological tools can be employed to reduce dimensionality and size of text data. In addition, there are many term weighting schemes available in the literature that may be used to enhance text representation as feature vector. In this paper, we study the impact of text pre-processing and different term weighting schemes on Arabic text classification. In addition, develop new combinations of term weighting schemes to be applied on Arabic text for classification purposes.
A virtual Russian sense tagged corpus and catching errors in a Russian ↔ semantic pivot dictionary

There are areas in computational linguistics, where a word-sense tagged corpus becomes a necessary prerequisite or gives a significant boost to research. Unfortunately, publicly available corpora of this kind are extremely rare and making them from scratch is a very long and costly process. No corpus of Russian with unambiguous word-sense tags has been published so far. This paper describes an experimental approach of creating a virtual equivalent of a Russian sense tagged corpus and putting it to some real use. The virtual corpus was created using two public resources: the English SemCor corpus and our free multilingual semantic pivot dictionary, called the "Universal Dictionary of Concepts". The dictionary provides information sufficient to find sense-specific translations for nearly all sense-tagged words in SemCor. However, the pivot dictionary itself is under development and we are looking for the ways to improve it. We used the existing Russian volume of the pivot dictionary to calculate lexical context vectors for individual senses of 13832 Russian words, supposedly equivalent to the vectors that could be obtained from a real Russian translation of SemCor. Another set of vectors representing real usage of the same Russian words was extracted from a medium-size corpus of Russian without any semantic markup. The vector similarity score proved to be a useful factor in judging the correctness of links between Russian words and word senses similar to ones registered in the Princeton Wordnet. It helped to rank over 21000 of such links out of 56000 known and significantly reduce the amount of the manual work required to proofread the dictionary.
Switched Reluctance Motor Initial Design for Electric Vehicle using RMxprt

This paper presents a design and development of 8/6 switched reluctance motor for small electric vehicle using analytical method. The absent of permanent magnet, inherent fault tolerance capabilities, simple and robust construction make this motor become more attractive for small electric vehicle application such as electric scooter and go-kart. The switched reluctance motor is modelled using analytical formula in designing process. Later, the designed model is analyzed using ANSYS RMxprt software. In order to achieve 5kW power rating and to match with the design requirement, the switched reluctance motor model has been analyzed using RMxprt tools for the preliminary parameters design process. This tools is able to predict the output performance of motor in term of speed, flux linkage characteristic, output torque and efficiency.
Text Mining for Quality Control of Court Records

Attorneys across the United States use government-provided electronic databases to submit docket entries and associated case les for processing and archival in public judicial records. Data entry errors in these repositories, while rare, can disrupt the court process, confuse the public record, or breach privacy and condentiality. Docket quality assurance is thus a high priority for the courts, but manual review remains resource-intensive. We have developed a prototype application of text mining and human language technologies to partially automate quality assurance review of electronic court documents. This solution uses document classication and named entity recognition to extract metadata directly from documents. Discrepancies between the extracted metadata and the userprovided metadata indicate a possible data entry error. On two independent samples of publicly available court documents, we nd that for a small number of classes with a sucient number of training documents, the document class can be automatically classied with greater than 94% accuracy in one case, but only 81% in the other. Our attempts to extract case numbers and the names of parties from documents via a conditional random eld model met with less success. Future work with more extensive training data is necessary to more accurately evaluate both applications.
A binary division algorithm for clustering remotely sensed multi-spectral images

A new method is proposed for clustering remotely sensed multi-spectral images with both high accuracy and high efficiency. For high speed processing, we project image data onto one dimensional sub-space, and limit the number of boundaries in the sub-space. The optimal sub-space and boundary are selected so that the ratio of the variance of within distance to the variance of between distance takes the minimum value. Image data are repeatedly divided into two groups until all of the groups consist of a single cluster. Performance of the proposed method was better than that of ISODATA in both speed and accuracy. The method was successfully applied to actual remotely sensed multi-spectral images. <<ETX>>
A survey of Learning Based Techniques of Phishing Email Filtering

Phishing email is one of the major issues of the web nowadays ensuing in monetary losses for organizations and individual users. Varied approaches are developed to filter phishing emails. The current paper focuses on machine learning applications used to detect and predict phishing emails. Moreover, a comparative study and analysis of the assorted filtering ways on the market for organizations and individual users is additionally undertaken. The conducted survey is an organized guide to support the current state of the literature, in read of the subject’s wide-ranging scope of papers.
Research and implementation of real-time face detection, tracking and protection

Privacy protection in video images is becoming one of the research focuses in the field of remote collaborative system. In this paper, a method for face detection, tracking and privacy protection is presented. According to skin-color distribution in the color space, we developed a statistical skin-color model through interactive sample training. Using this model we convert the color image to binary image and then segment face candidate region. Then we use a facial feature matching scheme for further detection. The presence or absence of a face in each region is verified by means of mouth detector. Real time detection and tracking can be achieved by using this method in video images. In order to speed up tracking, we improve the traditional method by adding motion prediction, which works better when several disturbing objects appear simultaneously. Finally we make the tracking region blurring and transmit the frames to the remote collaborative sites to obtain the privacy protection. The level of privacy protection can be dynamically adjusted according to collaborators' requests and credibility of remote sites. The experiment results show the proposed method not only has high speed and efficiency, but also is robust to head rotation to some extent.
Improving Scheduling on Symmetric Multiprocessing (SMP) Architecture Based on Process Behavior

This thesis presents a new method of scheduling for systems of symmetric multiprocessing (SMP) architecture based on process behavior. The method takes advantage of process behavior, which includes system calls to create groups of similar processes using machine-learning techniques like clustering or classification, and then makes process distribution decisions based on classification or clustering groups. The new method is divided into three stages: the first phase is collecting data about process and defining which subset of data is to be used in further processing. The second phase is using data collected in classification or clustering to create classification/clustering models by applying common techniques similar to those used in machine learning, such as a decision tree for classification or EM for clustering. System training classification should be done in this phase, and after that, classification or clustering models should be applied on a running system to find out to which group each process belongs. The third phase is using process groups as a parameter of scheduling on SMP systems when doing distribution over multi-processor cores. Another advantage can be achieved by letting the end user train the system to classify a specific type of process and assign it to a specific process core, targeting real-time response or performance gain. The new method increases process performance and decreases response time based on different kinds of distribution. The fair distribution method was used in this research, where group elements (processes) are divided equally on processor cores, and it has a good result in terms of performance and response time, but it has limitations where there is inter-process communication.
Tracking Features With Global Motion Compensation for Drone Camera Servoing

This paper deals with visual servoing for a pan and tilt camera embedded in a drone. Video is transmitted to the ground where images are processed on a PC, and turret controls are sent back to the drone. The objective is to track any fixed object on the ground without knowledge about shape or texture and to keep it centered in the image. In order to achieve this task an algorithm that combines feature-based and global motion estimation is proposed. This algorithm provides a good robustness to very strong video transmission noise and works at a frame rate close to 25 fps. The control of the system is based on a double closed loop, which achieves a fast convergence to the desired position. Experimentation in real conditions shows the effectiveness of the proposed scheme.
Manual annotation of the National Corpus of Polish with Anotatornia

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Visual Transfer Learning in the Absence of the Source Data

Image recognition has become one of the most popular topics in machine learning. With the development of Deep Convolutional Neural Networks (CNN) and the help of the large scale labeled image database such as ImageNet, modern image recognition models can achieve competitive performance compared to human annotation in some general image recognition tasks. Many IT companies have adopted it to improve their visual related tasks. However, training these large scale deep neural networks requires thousands or even millions of labeled images, which is an obstacle when applying it to a specific visual task with limited training data. Visual transfer learning is proposed to solve this problem. Visual transfer learning aims at transferring the knowledge from a source visual task to a target visual task. Typically, the target task is related to the source task, and the training data in the target task is relatively small. In visual transfer learning, the majority of existing methods assume that the source data is freely available and use the source data to measure the discrepancy between the source and target task to help the transfer process. However, in many real applications, source data are often a subject of legal, technical and contractual constraints between data owners and data customers. Beyond privacy and disclosure obligations, customers are often reluctant to share their data. When operating customer care, collected data may include information on recent technical problems which is a highly sensitive topic that companies are not willing to share. This scenario is often called Hypothesis Transfer Learning (HTL) where the source data is absent. Therefore, these previous methods cannot be applied to many real visual transfer learning problems. In this thesis, we investigate the visual transfer learning problem under HTL setting. Instead of using the source data to measure the discrepancy, we use the source model as the proxy to transfer the knowledge from the source task to the target task. Compared to the source data, the well-trained source model is usually freely accessible in many tasks and contains equivalent source knowledge as well. Specifically, in this thesis, we investigate the visual transfer learning in two scenarios: domain adaptation and learning new categories. In contrast to the previous methods in HTL, our methods can both leverage knowledge from more types of source models and achieve better transfer performance. In chapter 3, we investigate the visual domain adaptation problem under the setting of Hypothesis Transfer Learning. We propose Effective Multiclass Transfer Learning (EMTLe) that can effectively transfer the knowledge when the size of the target set is small. Specifically, EMTLe can effectively transfer the knowledge using the outputs of the source models as the auxiliary bias to adjust the prediction in the target task. Experiment results show that EMTLe can outperform other baselines under the setting of HTL.
A Topic Approach to Sentence Ordering for Multi-document Summarization

Sentence ordering is different but important for multi-document summarization. In this paper, we propose a topic approach to sentence ordering for generic multi-document summarization. We use LDA model to calculate sentence weight and similarity between sentence and topic. We put these two criteria into two-dimensional coordinate system in order to integrate them. We illustrate the problem of sentence ordering as the maximum vector problem. The selected points which we called summarization-points are those points which are not dominated by any other point. The experiments showed that the proposed algorithm achieved better performance compared the other state-of-the-art algorithms on DUC2002corpus.
Synthesis of Property-Preserving Platform Mappings

System development often involves decisions about how a high-level design is to be implemented using primitives from a low-level platform. Certain decisions, however, may introduce undesirable behavior into the resulting implementation, possibly leading to a violation of a desired property that has already been established at the design level. In this paper, we introduce the problem of synthesizing a property-preserving platform mapping: A set of implementation decisions ensuring that a desired property is preserved from a high-level design into a low-level platform implementation. We provide a formalization of the synthesis problem, and propose a technique for synthesizing a mapping based on symbolic constraint search. We describe our prototype implementation, and a case study demonstrating the application of our technique to synthesizing secure platform mappings for OAuth, a popular family of authorization protocols on the web.
An experimental comparison of ensemble learning methods on decision boundaries

This paper presents an experimental comparison on different kinds of neural network ensemble learning methods on a patter classification problems. To summarize, there are three ways of designing neural network ensembles in these methods: independent training, sequential training and simultaneous training. The purpose of such comparison is not only to illustrate the learning behavior of different neural network ensemble learning methods, but also to cast light on how to design more effective neural network ensembles. The experimental results show that the decision boundary of the trained neural network ensemble by negative correlation learning is almost as good as the optimum decision boundary.
A concept for a usability focused design method

Many user-centered design methods (UCDM) have been developed within the last decades. However, industry increasingly demands for a new-generation UCDM since only few of the existing UCDM seem to be applicable to the daily design practice. This paper first discusses the main criteria regarding such new-generation UCDM. These criteria are based on literature research. Based on the formulated criteria, the paper discusses the characteristics of a proposed road map based concept for such a new-generation UCDM afterwards. The UCDM's concept enables designers to define a specific design approach which fits best to the specific characteristics of the design problem and the design context in which the design activities take place. Finally this paper discusses an outlook on the further development of the proposed UCDM's concept. The next step will exist out of specifying the roadmap. Afterwards supporting the designer in using the toolbox will be the object of research.
A Buffer Cache Management for Locality with Prefetching

paper proposes buffer cache architecture. The proposed model consists of three units - main buffer cache unit, pre-fetch unit and LRU Evict Unit. An algorithm is proposed to retain the evicted entry from main buffer cache unit in the LRU Evict Unit thereby giving it a second chance of access. On subsequent access in the LRU Evict Unit, the entry is promoted to the pre- fetch unit to accommodate more entries in the main buffer cache unit. On access in the pre-fetch unit, an entry is fetched into the main buffer cache. The LRU replacement policy is used in all the units. The proposed model is compared with buffer cache architecture with LRU replacement algorithm. The performance is comparable for sequential input where as 3% improvement in performance is seen for random input.
Statistical inference from genetic data on pedigrees

This monograph is based on lectures presented at a 1999 CBMS Summer Research Conference. It develops the probability models for genetic data on related individuals, from the meiosis level to data on extended pedigrees. The focus is on simple Mendelian traits, such as DNA markers, but on joint models for data at multiple genetic loci, such as arise in modern genome scan studies. The statistical approach is that of likelihood, maximum likelihood estimation, and methods for the analysis of latent-variable and hidden-Markov models including the EM algorithm, the Baum algorithm, and Monte Carlo imputation methods.