Miracle-dz/UniFAR-FTUs huggingface dataset:
An Attempt to Improve the Processor Performance by Proper Memory Management for Branch Handling

The performanceof the processor is highly dependent on the regular supply of correct instruction at the right time. Whenever a data miss is occurring in the cache memory the processor has to spend more cycles for the fetching operation. One of the methodsused to reduce instruction cache miss is the instruction prefetching,which in turn will increase instructions supply to the processor.The technology developments in these fields indicates that in future the gap between processing speeds of processor and data transfer speed of memory is likely to be increased. Branch Predictor play a critical role in achievingeffective performance in many modernpipelined microprocessor architecture. Prefetching canbe done either with software or hardware. In software prefetching the compiler will insert a prefetch code in the program. In this case as actual memory capacity is not known to the compiler and it will lead to some harmful prefetches. In hardware prefetching instead of inserting prefetch code it will make use of extra hardware and which is utilized during the execution. The most significant source of lost performance when the process waiting for the availability of the next instruction. Thetime that is wasted in case of branch misprediction is equal to the number of stages in the pipeline, starting from fetch stage to execute stage. All the prefetching methods are givenstress only to the fetching of the instruction for the execution, not to the overall performance of the processor. The most significant source of lost performanceis,when the process is waiting for the availability of the next instruction. The time that is wasted in case of branch misprediction is equal to the number of stages in the pipeline, starting from fetch stage to execution stage.This paper we made an attemptto study the branch handling in a uniprocessing environment, whenever branching is identified instead of invoking the branch prediction the proper cache memory management is enabled inside the memory management unit.
The Impact of Game Avatar Customization in Improving User Experience and Gamer Loyalty: Experiment in Role Playing Game (RPG) Based Video Game

The increasing number of video game titles raises competition between video game developers getting tighter and more competitive, this encourages the game developers to be more creative in designing their games so that players become loyal to play and not quickly switch to playing other games. While we know that game development is very difficult, time consuming and requires a lot of costs for the RPG genre. So that the loyalty of game players in playing games becomes a crucial problem.This study aims to explain how the customization of game avatars can increase the loyalty of video game players in playing role-playing games (RPGs). This study uses the within-subject experimental method with participants who are RPG video game players who have played for at least 1 year, 24 participants participated in the 8 experiment session throughout the study. The results of this study indicate that avatar customization is very important because it can improve the player's self-identification in the game, improve the playing experience, improve the perceived quality of the video game played, increase the flow and immersion of players, and increase gamer loyalty. Another result that was raised was that the use of avatars that were the same gender with players (male players using male avatars) did not have higher flow and immersion than when using different genders (male players use female avatars) so that this raises new issues of gander swapping in the role playing video game that can be continued for further research.
Asymptotic design and analysis of multistage detectors and multistage channel estimators for multipath fading channels

The impact of channel estimation errors on the performance of the LMMSE multistage detec- tor for large multiuser systems with random spreading sequences is analyzed. The receiver consists of a mul- tistage filter for channel estimation and an LMMSE multistage detector that takes into account the im- perfect knowledge of the channels. The asymptotic performance of the whole receiver is provided. I. ANALYSIS AND RESULTS Let us consider a synchronous CDMA system with spread- ing factor N and K users. We assume multipath fading chan- nel with known channel length L and additive noise. The equivalent baseband signal at the chip matched filter output is given by y(m) = S(m)A(m)b(m) + n(m)
ONTOLOGY BASED CLUSTERING IN RESEARCH PROJECT SELECTION & ASSIGN PROPOSALS TO EXPERTS BY ONTOLOGY MATCHING

Research Project Selection is a remarkable bustle in all Research funding and Government agencies. Selection and Assignment processes are unproblematic when number of proposal submission is less. But with mounting in numbers, both processes will become complicated. Through traditional methodologies, Proposal Grouping, Experts grouping and Proposal assignment to experts are done manually or via keywords which yield inefficient results. In recent methodologies, Ontology based Text Mining Method (OTMM) has been used to cluster Proposals based on their likeness in research area by means of Research Ontology. Whereas, Reviewers grouping and proposal assignment to experts is still not precise since it is done manually. This paper proposes Ontology based Text Mining Method to cluster reviewers based on their specialism and overcome the Ontology Mapping Problem to allocate proposals to appropriate reviewers analytically. This methodology affords an effective way for research project selection with escalating figure of proposals and reviewers.
A Genetic Algorithm Approach for Automatic Patch Prediction

Software system should be reliable and available failing which huge losses may incur. To achieve these objectives a thorough testing is required. In recent years, there has been a dramatic rise in the number of languages used in mainstream projects. In particular, languages which run on the JVM or Common Language Runtime (CLR) have become quite popular in creating a global development framework. Naturally, such languages prefer to interoperate with other languages built on these core platforms, particularly Java and C#. The problem is that such efforts are crippled by one fundamental limitation: circular dependencies-a relation between two or more modules which either directly or indirectly depend on each other to function properly. This paper presents the study of patch prediction using a common testing software to minimize cost of testing and optimization of software testing techniques by using Genetic Algorithms (GAs) and Sequential Quadratic Programming Algorithm(SQPA) to provide features like memory management, security, garbage collection, thread management and exception handling. Keywords—Optimization techniques, Genetic algorithm, SQP algorithm, Patch prediction.
Learning and recognition of 3D objects from appearance

The authors address the problem of automatically learning object models for recognition and pose estimation. In contrast to the traditional approach, they formulate the recognition problem as one of matching visual appearance rather than shape. The appearance of an object in a two-dimensional image depends on its shape, reflectance properties, pose in the scene, and the illumination conditions. While shape and reflectance are intrinsic properties of an object and are constant, pose and illumination vary from scene to scene. They present a new compact representation of object appearance that is parameterized by pose and illumination. They have conducted experiments using several objects with complex appearance characteristics. <<ETX>>
Inducing Decision Trees based on a Cluster Quality Index

Decision trees are popular classifiers in data mining, artificial intelligence, and pattern recognition, because they are accurate and easy to comprehend. In this paper, we introduce a new procedure for inducing decision trees, to obtain trees that are more accurate, more compact, and more balanced. Each candidate split is evaluated using Rand Statistics, a quality index based on external measures, because it is considered by many authors as the best existing index. Our method was compared with other state-of-the-art methods and the results over 30 databases from the UCI Repository prove our claims. We also introduce a new equation to measure the balance of a binary tree.
Statistical mechanics for wireless systems: Application of exclusion processes to the modeling and analysis of multihop networks

by Sunil Srinivasa This thesis focuses on the modeling and analysis of wireless multihop networks, employing a combination of ideas from a well-known tool in stochastic geometry, namely the Poisson shot noise theory and an unfamiliar concept in statistical mechanics, namely the totally asymmetric simple exclusion process (TASEP). We begin our study by considering the simplest wireless multihop network topology the line network, where the source, destination and all the relays are located in a collinear fashion. First, we propose a simple buffering and transmission scheme for wireless line networks which not only guarantees packet delivery but also helps keep packet delays small whilst regulating the flow of packets in a completely decentralized fashion. Second, we characterize the end-to-end delay distribution and achievable throughput of the wireless multihop line network for two different channel access schemes, randomized-TDMA and ALOHA. Additionally, we use our results to provide some useful design insights in long line networks. Next, we consider a more intricate network topology comprising an infinite number of source-destination flows and analyze design-level issues such as determining the optimum density of transmitters or the optimal number of hops along Sunil Srinivasa a flow that maximizes the throughput performance of the network. We also consider several other complex topologies comprising intersecting flows and propose the partial mean-field approximation (PMFA), an elegant technique that helps tightly approximate the throughput (and end-to-end delay) of such systems. We then demonstrate via a simple toy example that the PMFA procedure is quite general in that it may be used to accurately evaluate the performance of multihop networks with arbitrary topologies. Finally, we identify that when reliable delivery of packets is not very critical, a viable solution towards balancing end-to-end delay and reliability in multihop networks is to have the nodes forcibly drop a small fraction of packets. Based on this principle, we present an analytical framework that helps quantify the throughput-delay-reliability performances of the ALOHA multihop network. We find that while in the noise-limited regime, dropping a small fraction of packets in the network leads to a smaller end-to-end delay at the cost of reduced throughput, in the interference-limited scenario, dropping a few packets in the network can sometimes help mitigate the interference in the network leading to an increased throughput. We intend to promote TASEPs as a powerful tool to analyze the performance of multihop networks and hope that this introductory work instigates interest in solving other relevant wireless networking problems employing ideas from statistical mechanics. To the last four years of hard work and frolic
An Improved Hybrid Feature Selection Method for Huge Dimensional Datasets

Variable Selection is the most essential function in predictive analytics, that reduces the dimensionality, without losing an appropriate information by selecting a few significant features of machine learning problems. The major techniques involved in this process are filter and wrapper methodologies. While filters measure the weight of features based on the attribute weighting criterion, the wrapper approach computes the competence of the variable selection algorithms. The wrapper approach is achieved by the selection of feature subgroups by pruning the feature space in its search space. The objective of this paper is to choose the most favourable attribute subset from the novel set of features, by using the combination method that unites the merits of filters and wrappers. To achieve this objective, an Improved Hybrid Feature Selection(IMFS) method is performed to create well-organized learners. The results of this study shows that the IMFS algorithm can build competent business applications, which have got a better precision than that of the constructed which is stated by the previous hybrid variable selection algorithms. Experimentation with UCI (University of California, Irvine) repository datasets affirms that this method have got better prediction performance, more robust to input noise and outliers, balances well with the available features, when performed comparison with the present algorithms in the literature review.
Effects of Color Characterization on Computational Efficiency of Feature Detection with Live-Object Handling Applications

This paper presents a machine vision algorithm that utilizes the principal component analysis technique to characterize target features in color space from a set of training data so that the color classification can be done accurately and efficiently. The method, referred to here as the statistically based fast bounded box (SFBB), has significant potential in agriculture and food processing applications where color variability often renders grayscale-based algorithms difficult or impossible to work. We evaluate the algorithm in the context of live-bird handling applications and examine the effects of the color characterization on computational efficiency by comparing the proposed solution against two commonly used color classification algorithms; the RCE neural network classifier and the support vector machine. Comparison among the three methods demonstrates that SFBB is relatively easy to train, efficient and effective since with sufficient training data it requires no additional optimization steps; these advantages make SFBB an ideal candidate for high-speed automation involving live and/or natural objects.
A performance analysis and design of equalization with pilot aided channel estimation

This article presents some analysis and design rules for equalization in Rayleigh frequency-selective fading channels using training or pilot sequences. The union bounds of error probability performance are derived and are shown to be reasonably tight (especially for small constellations). These bounds can be used to predict the existence of an error floor. In addition, desirable properties for training sequences are established which may be used for training sequence design.
Hearer-effects on speaker performances: the influence of the hearer on speakers' effectiveness in oral communication tasks

Experiments were conducted to investigate the oral communication skills of 14 to 16-year-olds and to discover conditions which elicit the best spoken performances. Several conditions were tested, using instructional tasks of different levels of difficulty with speakers of various academic abilities. The performances were scored objectively, using as a criterion the amount of relevant information included by speakers in their instructions. The presence of a hearer and ex periencing of the tasks in ascending order of difficulty both resulted in better spoken performances. Prior experience in the hearer's role proved particularly beneficial in sensitizing speakers to their hearer's information requirements. From these results simple training tech niques are outlined for eliciting competent spoken performances in schools.
Comparing LSTM Recurrent Networks with Spiking Recurrent Networks on the Recognition of Spoken Digits.

One advantage of spiking recurrent neural networks (SNNs) is an ability to categorise data using a synchrony-based latching mechnanism. This is particularly useful in problems where timewarping is encountered, such as speech recognition. Differentiable recurrent neural networks (RNNs) by contrast fail at tasks involving difficult timewarping, despite having sequence learning capabilities superior to SNNs. In this paper we demonstrate that Long Short-Term Memory (LSTM) is an RNN capable of robustly categorizing timewarped speech data, thus combining the most useful features of both paradigms. We compare its performance to SNNs on two variants of a spoken digit identification task, using data from an international competition. The first task (described in Nature [15]) required the categorisation of spoken digits with only a single training exemplar, and was specifically designed to test robustness to timewarping. Here LSTM performed better than all the SNNs in the competition. The second task was to predict spoken digits using a larger training set. Here LSTM greatly outperformed an SNN-like model found in the literature. These results suggest that LSTM has a place in domains that require the learning of large timewarped datasets, such as automatic speech recognition.
Hardware Construction in Chisel

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A Modular Dc-dc Converter for High Outpot Voltage Applications

In this paper the author intends to present and analyze a modular Dc-Dc converter for high output voltage applications. The proposed converter is consisted of DC-Nx: modules whose inputs are connected in parallel and their outputs in series with a control scheme that ensures input current sharing among the modules and requlated output voltage . Moreover, it utilizes single feedback voltage sensing that achieves almost equal output voltage sharing among the modules. List of symbols C = output filter capacitance of one DC-DC module d = the AC part of the duty cycle D a duty cycle Gi = gain of the current sensing amplifier = N / 3 x R s G = gain of the modulator = d/v = Ad/Av = inpul current of one module I n CT i = AC part of the output filter inductor current I = input current of the DC-DC converter system k = the number of DC-DC modules L = output filter inductance of one DC-DC module N = number of turns of the power transformer primary winding N = number of turns of the power transformer secondary winding Paper APT 138-11-28 accepted for presentation at the IEEE/NTUA Athens Power Tech Conference: "Planning, Operation arid Control of Today's Electric Power Systems", Athens, Greece, Sept. 5-8,1993. N = turns ratio of the current sampling transformer P = output power of the DC-DC converter system T. = current loop gain T V = voltage loop gain T = transfer function of the voltage error amplifier R = load resistance of the converter system R = output filter capacitor ESR of one DC-DC module R = output filter inductor ESR of one DC-DC module R = resistance of current sensing network ,s v = AC part of control voltage V ,= output voltage of the DC-DC converter system ,o v = AC part of the converter system output voltage Vo,&= output voltage of one module V = input voltage of the D€-DC converter system ,s v = AC part of input voltage a = power transformer turns ratio = N / N 2 1 q = overall efficiency of the DC-DC converter qd= efficiency of one module w = frequency of the modulator w = frequency of the voltage error amplifier pole w = frequency of the voltage error amplifier zero w ' = frequency of the voltage error amplifier zero when CT
On Fair Window Control for TCP with ECN Using Congestion Level

SUMMARY The current congestion control mechanism for TCP with ECN suffers from the oscillation of the window size and the unfair sharing of network resources. These problems are caused by the insufficient information about the congestion level of a network and the different round trip time (RTT) among the connections respectively. In this paper, we propose a new window control algorithm for TCP with ECN to avoid the oscillation of the window size and to achieve the fairness among the connections.
Adaptive control of electrically‐driven robot without computation of regressor matrix

Abstract By using the function approximation technique (FAT), the adaptive control of rigid‐link electrically‐driven(RLED) robots is addressed in the presence of time‐varying uncertain parameters. Unlike the conventional adaptive control of robot manipulators, the regressor matrix does not need to be calculated in the proposed controller. Rigorous proof of the closed‐loop stability and the convergence of the output tracking error are derived by using Lyapunov‐like theory. Simulation studies are given to illustrate the tracking performance with the proposed control schemes.
Towards evidence-based writing decisions: The Knowledge Base Comprehensible Text

Purpose: This paper introduces the Knowledge Base Comprehensible Text, a digital resource containing 702 studies on comprehension and usability of text and discourse, published between 1980 and 2010. The paper explains which publications were included in the knowledge base, how they were collected, how they were annotated and what the interface of the knowledge base looks like. Method: Literature search Results: The paper presents a brief survey of the studies contained in this resource, and discusses the answers of the knowledge base to an exemplary question about the comprehensibility of passives. Conclusion: The knowledge base makes research on comprehension accessible that is relevant for both practitioners and researchers in the field of technical communication. It is a useful tool in a field which, by its very nature, has a strong interdisciplinary orientation and is therefore hard to oversee.
Color image segmentation based on SLIC and watershed algorithm

In order to overcome the problem of over-segmentation caused by traditional watershed algorithm, a color image segmentation algorithm based on simple linear iterative clustering (SLIC) and watershed algorithm is proposed to achieve an ideal segmentation effect. Firstly, the algorithm calculates the number of super-pixels pre-segmented by image complexity, and uses SLIC to super-pixel segmentation preprocessing of the original image to reduce the redundant information in subsequent processing. Then, an adaptive threshold calculation method is proposed to process the gradient image of the preprocessed image in order to effectively remove noise and obtain more complete contour information. Finally, the watershed segmentation algorithm is used to segment the image extracted by the minimum value marker. Experiments on a large number of images show that the proposed algorithm can effectively suppress the over-segmentation problem caused by the traditional watershed algorithm, and is superior to the traditional algorithm in the comparison of LCE and GCE, and the segmentation quality is improved.
Algoritmo de Calibración en MATLAB para Cámaras Digitales

For several modern subjects, such as computer vision, digital image processing, stereo vision, augmented reality and others, it is necessary to know in advance the parameters of a camera: the focal length, the characteristics of the pixels and the camera’s orientation. This work includes the implementation of an algorithm in MATLAB to extract the internal and external parameters of an off-the-shelf digital camera. The project follows the calibration technique proposed by Zhang (Zhang, A Flexible New Technique for Camera Calibration, 1998) in order to program it as an algorithm using MATLAB. Ultimately, the algorithm is put under test with real and synthetic data to validate its efficiency and reliability.