Miracle-dz/UniFAR-FTUs huggingface dataset:
Liver Classification Using Modified Rotation Forest

Eesembling Classification techniques have been widely used in the medical field for accurate classification than an individual classifier. Modified Rotation Forest algorithm was proposed for accurate liver classification by analyzing the combination of selected classification algorithm and feature selection technique. Selected classification algorithms were considered from each category of classification algorithms. The category of classification algorithms are Tree based, Statistical based, Neural Networks based, Rule based and Lazy learners. Modified Rotation Forest algorithm for UCI liver data set has multi layer perception classification algorithm and Random Subset feature selection technique and for INDIA liver data set has nearest neighbour with generalized distance function and correlation based feature selection technique.
Identifying the Safety Impact of Signal Coordination Projects along Urban Arterials Using a Meta-analysis Method

The safety impact of changes to roadway operations have been of interests in recent years with the publication of the Highway Safety Manual. One area that is in need of further study is the safety impact of traffic signal coordination projects in urban areas. Specifically, this study seeks to identify the safety benefit from traffic signal coordination projects on major arterial roadways through urban areas using a before and after study with a comparison groups approach and a meta-analysis method. The findings suggest that traffic signal coordination could decrease total crashes by 21 percent, injury crashes by 52 percent and property-damage-only crashes by 21 percent. The results can be utilized by engineering practitioners to estimate the safety benefits for projects that seek to coordinate traffic signals along an urban corridor. Because these projects can both improve the safety of roadways while improving traffic flow, the application of these findings could be broad.
ROAD MARKINGS TO IMPROVE PEDESTRIAN SAFETY AT CROSSINGS

This article describes research to investigate aspects of pedestrian safety and amenity at traffic signals, commissioned by the DITM (now TMT) Division of the Department of the Environment, Transport and the Regions (DETR). The aim of the project was to investigate how pedestrians behave at traffic signals, and to relate geometric and control aspects of current layouts to accident records and to attempt to identify features leading to safe/unsafe operation. Following 'before' and 'after' studies at three sites using video surveys, it is clear that there is scope for using low-cost engineering measures to improve pedestrian safety at signal-controlled junctions. (A)
Lucid Gumption Maturation with Query Constellation and Data Retrieval

This research work will be strengthening my ongoing study of Information Retrieval performance Refinement in Deep Web Mining. ” The information explosion on the Internet has placed high demands on search engines. Yet people are far from being satisfied with the performance of the existing search engines, which often return thousands of documents in response to a user query. Many of the returned documents are irrelevant to the user's need. The precision of current search engines is well under people's expectations. The problem of gumption indistinctness is also one of the reasons of poor performance of the search engines. Deep Web clustering engines have been proposed as one of the solution to the lexical indistinctness issue in Deep Web Information Retrieval. These systems group search results, by providing a cluster for each specific aspect (i.e., meaning) of the input query. Users can then select the cluster(s) and the pages therein that best answer their information needs. The paper tries to discover the success level of query clustering approach for the gumption indistinctness problem in web information retrieval. The extension with annotated framed interface can be exercised for better retrieval of information from deep web too. Furtherance to it, the introduction of logical deep web data retrieval services carries great potential for information retrieval.
An Analysis of the Oxford Placement Test and the Michigan English Placement Test as L 2 Proficiency Tests

The purpose of this study was to analyze two second language (L2) English placement tests, the Oxford Placement Test and the Michigan English Placement Test, to evaluate the degree to which the tests function as L2 proficiency tests. Descriptive statistics, normality tests, and correlation coefficients were calculated for the two tests. Factor analytic methods and Rasch scaling were also applied to the data set. The results suggest that both tests can function as proficiency tests for Japanese learners of English. However, the distribution of the Michigan English Placement Test scores deviated from normality, and the reliability estimates of the scores on some of the subsections of that test were low. The implications of the results are that researchers need to choose a testing instrument that measures the aspect of proficiency that is related to a particular study, and that subsection scores may not be reliable enough to use as a unidimensional scale. ウィスナー. qx 09.3. 10 18:46 ページ33
Separate visual representations for perception and for visually guided behavior

Converging evidence from several sources indicates that two distinct representations of visual space mediate perception and visually guided behavior, respectively. The two maps of visual space follow different rules; spatial values in either one can be biased without affecting the other. Ordinarily the two maps give equivalent responses because both are veridically in register with the world; special techniques are required to pull them apart. One such technique is saccadic suppression: small target displacements during saccadic eye movements are not preceived, though the displacements can change eye movements or pointing to the target. A second way to separate cognitive and motor-oriented maps is with induced motion: a slowly moving frame will make a fixed target appear to drift in the opposite direction, while motor behavior toward the target is unchanged. The same result occurs with stroboscopic induced motion, where the frame jump abruptly and the target seems to jump in the opposite direction. A third method of separating cognitive and motor maps, requiring no motion of target, background or eye, is the Roelofs effect: a target surrounded by an off-center rectangular frame will appear to be off-center in the direction opposite the frame. Again the effect influences perception, but in half of the subjects it does not influence pointing to the target. This experience also reveals more characteristics of the maps and their interactions with one another, the motor map apparently has little or no memory, and must be fed from the biased cognitive map if an enforced delay occurs between stimulus presentation and motor response. In designing spatial displays, the results mean that what you see isn't necessarily what you get. Displays must be designed with either perception or visually guided behavior in mind.
THE RELATIONSHIP BETWEEN CAPITAL STRUCTURE AND DIVIDED POLICY IN LISTED COMPANIES OF TEHRAN STOCK EXCHANGE

This paper studied the relationship between capital structure and profit divided policy in Iran’s stock market. This study investigated the relationship between capital structure and profit divided policy in listed companies in Tehran stock exchange during 1387-1391 with the use of consolidated data. Thus, 109 companies in Tehran stock exchange were selected by screening as a sample of study. On the other hand, F test and Hausman test were used to select the best model for common consolidated data, suitable effects, and random effect. The result of this study indicated that there wasn’t any significant relationship between leverage ratio (capital structure) and profit divided cost (profit divided policy).
1 THE EMPOWERMENT EFFECT : INVESTIGATING THE INTERACTION BETWEEN PSYCHOLOGICAL EMPOWERMENT , QUALITY OF WORK-LIFE AND ORGANIZATIONAL COMMITMENT

This study aimed at studying the main and interaction effects of psychological empowerment on organizational commitment and quality of work-life. Data were collected from several MBA classes to achieve the objective of diversity. Result provided support to the framework originally developed by Thomas and Velthouse in 1990 that psychological empowerment is a multi-dimensional construct consisting of four aspects (meaning, competence, self-determination and impact). Egyptian employees exhibited above average levels of psychological empowerment, quality of work-life and organizational commitment. Positive associations between quality of work-life, commitment and psychological empowerment were found. Psychological empowerment did not have a mediating effect between quality of work-life and organizational commitment.
Optimization of Injection Characteristics in a Large Marine Diesel Engine Using Evolutionary Algorithms

The reduction of pollutant emissions of marine diesel engines can be achieved by employing multiple injection strategies, similar to the ones used in automotive engines. In the present work, the options for advanced injection strategies, in terms of pilot injections, are explored for a large two-stroke marine diesel engine operating at full load, by utilizing Computational Fluid Dynamics simulations coupled with an Evolutionary Algorithm. The goals of the present Multi-Objective constrained optimization are the minimization of nitrogen oxides (NOX) emissions and engine specific fuel consumption. The constraint, imposed by structural limitations of the engine, is the maximum cylinder pressure. The design variables include parameters controlling the pilot and main injections, and the injected total mass. The problem is handled as a Multi-Objective Optimization Problem, and the final set of elite solutions is identified based on the Pareto dominance. The methodology yields two families of solutions, corresponding to early and late pilot injection, providing significant improvement, both in terms of NOX emissions and engine efficiency. The elite solutions are analyzed and interpreted by detailed processing of the simulation results.
1 Processing Traffic Data collected by Remote Sensing

Video data is used more and more often to study traffic operations. However, extracting vehicle trajectories from video by using current methods is a difficult process, typically resulting in many errors. Additionally, this requires much labor to manually correct the trajectories. This paper proposes a new method to process video data from traffic operations. We do not detect a vehicle in each picture of the video separately, but instead transform the video data such that the trajectories of the vehicles (their position over time) become visible in a single image. In this single image, the trajectories can be found by detecting lines. The difference with other methods is trajectories are detected rather than vehicles. Trajectory (line) detection is more robust than vehicle (rectangle) detection, seen from the fact that applying this method around 95% of the trajectories are detected correctly and, more importantly, the length of the segments of each of the trajectories is much longer, compared to results from other methods reported in literature. Also, the detection is a very quick process since only a single image is required to be analyzed. For a dataset of 5 minutes in length, transforming costs several minutes, and automatically detecting and tracking costs 40 to 50 minutes per lane. Further manually correcting is then necessary which costs about 10 minutes per lane. In contrast, using a different method the total processing time for analyzing the traffic operations costs approximately one week for all lanes together. Knoop, Hoogendoorn and Van Zuylen 3
Malware Analysis using Profile Hidden Markov Models and Intrusion Detection in a Stream Learning Setting

In the last decade, a lot of machine learning and data mining based approaches have been used in the areas of intrusion detection, malware detection and classification and also traffic analysis. In the area of malware analysis, static binary analysis techniques have become increasingly difficult with the code obfuscation methods and code packing employed when writing the malware. The behavior-based analysis techniques are being used in large malware analysis systems because of this reason. In prior art, a number of clustering and classification techniques have been used to classify the malwares into families and to also identify new malware families, from the behavior reports. In this thesis, we have analysed in detail about the use of Profile Hidden Markov models for the problem of malware classification and clustering. The advantage of building accurate models with limited examples is very helpful in early detection and modeling of malware families. The thesis also revisits the learning setting of an Intrusion Detection System that employs machine learning for identifying attacks and normal traffic. It substantiates the suitability of incremental learning setting(or stream based learning setting) for the problem of learning attack patterns in IDS, when large volume of data arrive in a stream. Related to the above problem, an elaborate survey of the IDS that use data mining and machine learning was done. Experimental evaluation and comparison show that in terms of speed and accuracy, the stream based algorithms perform very well as large
Writing System Variation and Its Consequences for Reading and Dyslexia

ABSTRACT Most current theories of reading and dyslexia derive from a relatively narrow empirical base: research on English and a handful of other European alphabets. Furthermore, the two dominant theoretical frameworks for describing cross-script diversity—orthographic depth and psycholinguistic grain size theory—are also deeply entrenched in Anglophone and Eurocentric/alphabetocentric perspectives, giving little consideration to non-European writing systems and promoting a one-dimensional view of script variation, namely, spelling–sound consistency. Most dyslexics struggle to read in languages that are not European and orthographies that are not alphabetic such as abjads, abugidas, or morphosyllabaries; hence the full spectrum of the world’s writing systems needs to be considered. The global picture reveals multiple dimensions of complexity. We enumerate 10 such dimensions: linguistic distance, nonlinearity, visual complexity, historical change, spelling constancy despite morphophonemic alternation, omission of phonological elements, allography, dual purpose letters, ligaturing, and inventory size. We then consider how these 10 dimensions might affect variation in reading ability and dyslexia.
SCRUTINIZATION OF THE EFFECT OF THE GROWTH FACTOR ON THE DURATION OF TEHRAN STOCK MARKET LISTED FIRMS

Due to the considerable economic and social costs that bankruptcy of companies imposed on shareholders and finance and credit institutes, prediction of the bankruptcy of companies is one of the key financial issues. Survival and bankruptcy are two sides of the same coin. Almost in all of the studies which are being done in this area the emphasis is just laid on the aspect of bankruptcy. In the present research by means of Cox Regression Model the influence of growth on the duration span of Tehran stock market listed firms until reaching a condition of bankruptcy (based on article 141 of the trade law) will be scrutinized. The time span of this research is limited to the years of 2004 to 2010. This research in terms of purpose is an applied research and in terms of method is a descriptive-correlational research. Findings of the research illustrates that parallel to staff development the probability of duration of the company augments at first and then decreases. On the contrary, parallel to an increase in the growth of the assets, the probability of the duration of the company at first decreases and then increases. In other words, there is a second-rate relationship between the growth and survival of Tehran stock market listed firms.
An Empirical Analysis of Action Map in Learning Classifier Systems

: An action map is one of the most fundamental options in designing a learning classiﬁer system (LCS), which deﬁnes how LCSs cover a state action space in a problem. It still remains unclear which action map can be adequate to solve which type of problem e ﬀ ectively, resulting in a lack of basic design methodology of LCS in terms of the action map. This paper attempts to empirically conclude this issue with an intensive analysis comparing di ﬀ erent action maps on LCSs. From the analysis on a benchmark classiﬁcation problem, we identify a fact that an adequate action map can be determined depending on a type of problem di ﬃ culty such as class imbalance, more generally, a complexity of classiﬁcation or decision boundary of problem. We also conduct an experiment on a human activity recognition task as a real world classiﬁcation problem, and then conﬁrm that a suggested adequate action map from the analysis enables an LCS to improve on the performance. Those results claim that the action map should be selected adequately in designing LCSs in order to improve their potential performance.
A closer look at ATM traffic measurements and their statistical analysis

This paper presents an analysis of cell-level ATM traffic measurements collected from Sprint's network through a Telcordia Technologies (formerly Bellcore) custom-built ATM traffic recording device during a week in the summer of 1998. Analysis of the total traffic during one busy hour on one OC-3c link strongly indicated that the total aggregate traffic was self-similar with long-range dependent characteristics, and could be adequately modeled by the fractional Brownian motion (FBM) traffic model for time scales of current performance and engineering interest. Analysis on the finer time scale (100 microseconds) characteristics of the aggregate ATM traffic does not support a multifractal hypothesis. Simple (mono-fractal) scaling holds from 100 microseconds to a few hundred seconds. However the marginal distribution at time scales of 100 microseconds is no longer Gaussian, which can invalidate traffic performance predictions from the FBM model at that time scale. Beyond the aggregate traffic level, additional traffic characteristics were investigated. The three classes of service offered were CBR, VBR, and UBR; most of the subscriptions were for VBR service. The traffic was primarily type AAL 5. The class-of-service was not directly indicative of AAL type. The traffic patterns generated by customers had more to do with the AAL type and applications than with service subscription. The following observations drawn regarding the traffic characteristics by AAL type are presented.
Learning representations for visual recognition

In this dissertation, we propose methods and data driven machine learning solutions which address and benefit from the recent overwhelming growth of digital media content.First, we consider the problem of improving the efficiency of image retrieval. We propose a coordinated local metric learning (CLML) approach which learns local Mahalanobis metrics, and integrates them in a global representation where the l2 distance can be used. This allows for data visualization in a single view, and use of efficient ` 2 -based retrieval methods. Our approach can be interpreted as learning a linear projection on top of an explicit high-dimensional embedding of a kernel. This interpretation allows for the use of existing frameworks for Mahalanobis metric learning for learning local metrics in a coordinated manner. Our experiments show that CLML improves over previous global and local metric learning approaches for the task of face retrieval.Second, we present an approach to leverage the success of CNN models forvisible spectrum face recognition to improve heterogeneous face recognition, e.g., recognition of near-infrared images from visible spectrum training images. We explore different metric learning strategies over features from the intermediate layers of the networks, to reduce the discrepancies between the different modalities. In our experiments we found that the depth of the optimal features for a given modality, is positively correlated with the domain shift between the source domain (CNN training data) and the target domain. Experimental results show the that we can use CNNs trained on visible spectrum images to obtain results that improve over the state-of-the art for heterogeneous face recognition with near-infrared images and sketches.Third, we present convolutional neural fabrics for exploring the discrete andexponentially large CNN architecture space in an efficient and systematic manner. Instead of aiming to select a single optimal architecture, we propose a “fabric” that embeds an exponentially large number of architectures. The fabric consists of a 3D trellis that connects response maps at different layers, scales, and channels with a sparse homogeneous local connectivity pattern. The only hyperparameters of the fabric (the number of channels and layers) are not critical for performance. The acyclic nature of the fabric allows us to use backpropagation for learning. Learning can thus efficiently configure the fabric to implement each one of exponentially many architectures and, more generally, ensembles of all of them. While scaling linearly in terms of computation and memory requirements, the fabric leverages exponentially many chain-structured architectures in parallel by massively sharing weights between them. We present benchmark results competitive with the state of the art for image classification on MNIST and CIFAR10, and for semantic segmentation on the Part Labels dataset
Performance modelling of an idle-signal casting multiple access with failure detection (ICMA/FD) mobile PRLAN

An idle-signal casting multiple access with failure detection (ICMA/FD) protocol suitable for use in a mobile packet radio LAN (PRLAN) is proposed and analyzed. The analysis takes into account the effects of multipath signal fading on channel access and packet transmission. Numerical results, which are validated by computer simulations, are presented to enable a quantitative study of the system performance. It is found that the performance of the ICMA/FD protocol is affected significantly by fading on the base-to-mobile channel. In addition, performance improves with longer packets even though such packets are more vulnerable to failure due to fading during transmission. <<ETX>>
Mnemonic Reader: Machine Comprehension with Iterative Aligning and Multi-hop Answer Pointing

Recently, several end-to-end neural models have been proposed for machine comprehension (MC) tasks. Most of these models only capture interactions between the context and the query, and utilize ”one-shot prediction” to point the boundary of answer, failing to fully understand the context and the query. In this paper, we introduce Mnemonic Reader for MC tasks, an end-to-end neural network which aims to tackle the above problem in two aspects. Firstly, we propose an iterative aligning mechanism which not only captures interactions between the context and the query but also models interactions among the context itself to obtain a fully-aware context representation. Second, we use a multi-hop answer pointer which allows the network to continue refining the predicted answer span. Extensive experiments on TriviaQA and SQuAD datasets show that our model obtains state-of-the-art results.