Miracle-dz/UniFAR-FTUs huggingface dataset:
Low-Power Repeater Insertion for Global Interconnects

Repeater insertion is one of the most widely used techniques to reduce the signal propagation delay on global interconnects. The number of repeaters inserted into interconnects is expected to be enormous due to the ever-increasing chip dimension. The huge number of repeaters can take up significant silicon area and consume a lot of power. Consequently, minimization of power consumption of repeaters with timing closure constraints is a very important problem in future low-power VLSI design. In this dissertation, we investigate efficient schemes for low-power repeater insertion on global interconnects. We first analyze key issues on repeater library design by introducing an analytical low-power repeater insertion algorithm for uniform two-pin interconnects. Our study leads to the answer of how to design a compact repeater library for low-power in early design stages. We then discuss several low-power repeater insertion schemes under given timing constraints. These schemes achieve a better trade-off between solution quality and runtime than previously proposed approaches. To handle the signal integrity problem while performing repeater insertion, we next present a novel low-power repeater insertion scheme under both timing and signal slew rate constraints. The proposed scheme is able to capture both delay and slew rate information, resulting in high quality interconnect designs. Besides the repeater insertion algorithms for given interconnects, we also describe a simple yet effective power macromodel for global interconnects with the consideration of low-power repeater insertion. By incorporating the macromodel into a macrocell placement tool, we have achieved simultaneous minimization of timing violations and power dissipation.
A Heuristic Approach to Cell to Switch Assignment

In this paper, we consider a problem of network design of personal communication services (PCS). The problem is to assign cells to the switches of a PCS network in an efficient manner. We consider two types of costs. First is the cost of handoffs between cells. Second is the cost of cabling between a cell site and its associated switch. There is constraint on the problem by the call volume handling capacity of switch. This paper describes Firefly Algorithm with Heuristic Method and proposes a possible way it to solve the problem of assignment of cells of a geographical area to the available number of switches based on the minimization of total cost for the assignment.
Definition of a data model that includes the instructional design to describe the elements involved in the design of virtual courses

Las metodologias convencionales de ingenieria de software tienen estrategias para el analisis y diseno de aplicaciones de software educativo, pero poco han evolucionado en lo relacionado con la fase del diseno instruccional. En este articulo, a partir de la revision de algunos modelos de diseno instruccional y la identificacion de vacios en dos modelos propuestos para el diseno de cursos virtuales, se propone un modelo de datos para describir los elementos que intervienen en el diseno de cursos virtuales e ilustrar las relaciones entre tales elementos. Para la elaboracion este modelo se tienen en cuenta las actividades de algunos modelos de diseno instruccional. La construccion de este modelo de datos es un aporte a la formalizacion del diseno de cursos virtuales.
Cryptanalysis of electrical PUFs via machine learning algorithms Kryptoanalyse von elektrischen PUFs durch maschinelle Lernalgorithmen

Physical Unclonable Functions are a recent cryptographic primitive. Exploiting the uniqueness and complexity of a physical system they ensure that only the system itself, or someone in possession of it, can be authenticated. This is in contrast to a digital ID which, once exposed, can be copied arbitrarily. But in this thesis it is depicted that current and proposed silicon implementations (ArbiterPUF, XOR-ArbiterPUF, FeedForward-ArbiterPUF) are susceptible to building virtual counterfeits via machine learning techniques. Acknowledgment I thank all the people who supported me writing this thesis with remarks and suggestions. Especially I thank Ulrich Ruhrmaier for guiding me throughout my research, Christian Osendorfer and Frank Sehnke for providing practical advice and Vera Stoyanova for the inspirations based on her work. Further on I thank Qingqing Chen for generating the analog circuit simulation test data.
A Path Tracking Control Method for Mobile Robot

In this paper, the hardware of the mobile robot was first described. Based on the concepts of predictive control, a novel method for generating the reference path was developed. It can finish some control actions in advance then approach the reference path during 2-3 control periods.The oscillation in other feedback controller will not occur.The test results show that this mobile robot controlled by the method can follow various reference paths and it possesses having ideal robustly. This control method can also be applied to autonomous practical land vehicle guidance.
Quadratic Sieve integer factorization using Hadoop

Integer factorization problem is one of the most important parts in the world of cryptography. The security of the widely-used public-key cryptographic algorithm, RSA [1], and the Blum Blum Shub cryptographic pseudorandom number generator [2] heavily depend on the presumed difficulty of factoring a number to its prime constituents. As the size of the number to be factored gets larger, the difficulty of the problem increases enormously. This fact has led to the development of many different algorithms to attack bigger number within polynomial time. However, there is no known efficient algorithm for very large numbers, to shake up the security of real-world cryptographic applications. The fastest factoring algorithms to factor general numers are General Number Field Sieve (GNFS) and Quadratic sieve (QS). While QS is regarded as the fastest one for integers less than 110 digits, GNFS is by far the fastest for very big numbers. As of December 2009, the biggest number factored is 768-bit, 232-digit number taken from RSA challange [6] by using GNFS algorithm over a period of two years. The success came after a long hard work and complex programming development. This shows the complexity and difficulty of the Integer factorization problem. As the size of the number to be factored increases, the data required to factor that number equally increases. Hadoop-MapReduce is a powerful tool for processing for such magnitude data in a reasonable time. Because of this advantage, the thesis investigates the possiblity of attacking the problem in a distributed fashion using MapReduce programming paradigm on Hadoop. A single quadratic sieve algorithm written in Java was used to test different composite numbers. The results were carefully analyzed, then the strength and limitations of the approach are thoroughly discussed.
A robust visual tracking method with restricted Boltzmann machine based classifier

In general, visual trackers employ hand-crafted feature descriptors to track the object, which limits their performance. In this paper, a novel Restricted Boltzmann Machine based Tracker (RBMT) is proposed to enhance the robustness. RBMs are introduced to learn multiple feature descriptors for the different image cues which are transformed from the given images. A data augment method is introduced to online train the RBMs so as to make the learnt feature descriptors specific for different tracked objects. To make the proposed tracker adapted to drastic varying scenes, a feature selection method is also developed to fuse the multiple cues in feature level for the design of appearance-based classifiers. Our experimental results have shown that the proposed tracker can obtain promising performances compared with the other state-of-the-art approaches.
Conceptual and representational choices in defining an ISO standard for semantic role annotation

This paper presents two elements of the ISO standard for semantic role annotation which is under development (ISO CD 24617-4:2013), namely (a) the metamodel, which describes the types of concepts that may occur in semantic role annotation and their conceptual relations, and (b) an annotation language for expressing semantic role annotations, with its abstract syntax, XML-based dconcrete syntax, and semantics.
LESSONS LEARNED FROM IDEA: ARE TEACHERS UTILIZING SPECIAL EDUCATION PRACTICES AND TECHNIQUES AS A WAY TO IMPROVE DIFFERENTIATED INSTRUCTION IN ALL CLASSROOMS?

This paper explores the extent which general education teachers are utilizing special education techniques as a part of their classroom instructional practices. It also aims to ascertain if there is a difference in use of such practices among teachers in Title I and non-Title I schools. Utilizing qualitative interviews, our findings suggest teachers are currently not employing many special education practices, but, they believe they would be beneficial in their classrooms due to the differing abilities found in the general education populations. Training and perceived time to implement practices were listed as potential inhibitors of incorporation.
Validation studies of 2D turbulent flow over a backward facing step using a MRT k-epsilon LBM scheme

This paper presents the numerical simulation of 2D turbulent flow over a backward facing step by Lattice-Boltzmann Method (LBM). There are several numerical methods to solve CFD problems. Among them LBM is quite a new approach to solve CFD problems. It deals with microscopic parameters and macroscopic characteristics can be captured by multiscale analysis. In this paper a detailed analysis is presented to demonstrate the capabilities of this method. Moreover this method is extended to allow incorporation of traditional turbulent model. Implementation of a two equation k−e model without wall function is presented. Validation of Lattice-Boltzmann method is extended to backward facing step for turbulent case. The results are validated with the results done in other works. In turbulent case different total viscosity at different locations leads to different relaxation time in LBM. Therefore a multi relaxation time (MRT) model is used in this work.
Speech codec for the European mobile radio system

The speech coding scheme which will be used as the standard for the European mobile radio system has been selected by the CEPT Groupe Special-Mobile (GSM) as a result of formal subjective listening tests. It is based on the regular-pulse excitation linear predictive coding technique (RPE-LPC) combined with long-term prediction (LTP). The solution is called the RPE-LTP codec. The codec algorithm and the error protection scheme are presented. The net bit rate is 13.0 kb/s, and the gross bit rate, including error protection, is 22.8 kb/s. The experimental implementation based on VLSI signal processors is described. The speech quality obtained with the technique considered is far superior to that obtainable with present-day analog mobile radio systems. A duplex speech codec including error protection can be implemented with two VLSI sign processors with external data memories of about 1 K*16 b. <<ETX>>
Analysis of the impact of cyber attacks on energy management system in smart grid environment

Smart grids are an integrated solution to recently raised issues in the power industry. To achieve the main objectives of a smart grid, Energy Management System (EMS) must organically connect numerous SCADA systems included in existing power control systems. As such, it is necessary to establish vulnerability classification criteria for cyber security in the large-scale, multi-functional complex architecture of EMS. This paper presents a methodology for classifying cyber threats that can be applied to EMS models in a smart grid environment, and a qualitative analysis of the interactions between each level.
Extraction of Opinion Targets Based on Shallow Parsing Features

With the rapid development of the world wide web,more and more common users express their opinions on the web and many researchers pay more attentions to sentiment analysis.Fine-grained sentiment analysis on sentence level is very important.The extraction of opinion targets from opinion sentence is the key issue to sentence level of sentiment analysis.To improve the performance of opinion targets extraction,this paper proposes to integrate shallow parsing features and heuristic position information for modeling of the training process without introducing domain lexicon.The experiment results show that after adding the proposed features,nearly all specifications of both conditional random fields and contrast model are improved,and the results of conditional random fields are more efficient than that of the contrast model.Meanwhile,compared with the best results of the 2008 Chinese opinion analysis evaluation,the F measures of conditional random fields are 5% higher than the maximum value.
A New Hybrid Filtering Technique Based on Neighboring Pixels to Remove Impulse Noise from Digital Images

This paper proposes a hybrid technique to remove impulse noise from digital images. In this approach the filtering operation is based on 3  3 neighborhood of pixel under consideration. During filtering, the properties of neighborhood are considered to check whether it is highly corrupted with noise, medium or only itself act as impulse. Based upon these properties a new hybrid technique has been proposed to process the pixel which further uses different schemes. The experiments have been performed at various noise levels on standard images as well as on real images. The results have been evaluated on the basis of metrics like Signal to noise ratio (SNR), Edge preservation index ( EPI), Structure similarity index measure (SSIM), Multi scale structure similarity index measure (MS-SSIM) and Peak signal to noise ratio ( PSNR). From the results, it has been observed that proposed technique has worked efficiently by preserving the edges and fine lines. To demonstrate the effectiveness of proposed technique, the results have also been compared with other well accepted denoising techniques
Latent Fingerprint Matching using Alignment Algorithm Based Hough Transform

Identifying suspects based on latents which are lifted from crime scenes is extremely important to forensics and law enforcement agencies. Latent’s are the partial fingerprints usually smudgy and blurred with small area, and contain large distortion. Due to these characteristics, they have a considerably smaller number of minutiae points compared to full fingerprints and therefore it can be extremely difficult to automatically match latent’s to plain or rolled fingerprints that are stored in law enforcement databases. The goal is to develop a latent matching algorithm that uses only minutiae information. The proposed latent matching algorithm consists of three modules: (i) Align two sets of minutiae with a descriptor based Hough Transform; (ii) Establish the correspondence between minutiae; and (iii) Compute a match score.
Improved Sort-based KNN Text Categorization Method

The problem of large feature dimension and the expansibility reduces the KNN function.This paper brougt forward an ameliorative KNN method to solve the problem that big swatch sort with more texts is easy to become the k-nearest neighbors under the feature reduction condition.Firstly,it used an improved odds radio method to select feature.Secondly,it estimated the possible sorts for the text by using the sort vector.Lastly,it used an improved KNN method in the reduced texts to realize text categorization.The experiment shows that this method has improved the precision.
First versus subsequent foreign language development in situated interaction from a Vygotskyan SCT perspective: microgenetic analysis of Persian-speaking learners

ABSTRACT The present study compared L2 and L3 learners of the same additional language in two parallel case studies framed within a Vygotskyan SCT perspective to examine how foreign language learners’ background knowledge impacts on the path of their development. Interactive data were collected on the development of interrogative formation by four Persian learners of English (N = 2) and French (N = 2) in sessions tutored by a Persian L1, English L2, French L3 instructor with experience in teaching both languages. Data were collected, analysed, and interpreted employing Vygotsky’s microgenetic method and his constructs of ZPD and mediation. Four aspects of language development were compared across the cases and studies: (a) responsiveness to instruction, (b) responsiveness to graded mediation, (c) exploiting current affordances for further development and (d) approach to task accomplishment. The findings showed that differing linguistic profiles made the subjects fundamentally different from the outset. The L3ers were more sensitive to the structures, could extract more from the instructions/interactions, better able to exploit their resources to make meaningful connections between past and present, and experienced a faster-paced, more efficient transition of the structures from inter to intrapsychological functioning. Additionally, heightened metalinguistic and metacognitive awareness equipped them to more systematically approach challenging tasks.
Effective Engagement with Latino Youths and Communities

California's 4-H Youth Development Program adopted an asset-based and systematic community development approach to studying successful Latino-serving youth development programs. This effort included a scan of Latino engagement resources and Latino-serving youth development settings as well as key informant interviews. Beyond generating important data, the research process itself facilitated impactful Latino community outreach, relationship building, and engagement, suggesting multiple potential benefits to using a community development approach in research.
Mental task classification for brain computer interface application

Electroencephalogram(EEG) signal is an important information source of underlying brain processes.The communication based on EEG between human brain and computer is a new modality of human-computer interaction.In this paper,EEG signal of different mental tasks is preprocessed by Independent Component Analysis(ICA),AR model coefficient is extracted as feature vector,and classify the mental tasks based on BP neural network.According to the analysis and experiment results,the method can get high correct rate of classification
Physical Activity and Body-Mass-Index Relation in Secondary-School Students of the Visegrad Region

Background: One of the greatest problems of the contemporary society is obesity that manifests itself across all age categories. It is, however, in a direct contradiction to the increase of scientifically proven effects of physical activity (hereinafter referred to as ‘PA’) on the reduction and elimination of obesity. The question to be asked is: What is the impact of PA on BMI of the monitored study participants? Objective: The objective of our research was to analyse the relationship between physical activity (PA) and the body mass index (BMI) of 15 – 17-yearsold secondary-school students in four countries of the Visegrad group (V4). Material and method: The research was conducted in 2015 and the research sample concerned 2,499 students. Due to incomplete data, or, errors in answers, the statistic processing of the data included only 2,140 students, out of which 880 boys (41 %) and 1,260 girls (58.9 %). The information was gathered by means of an extended version of standardised International Physical Activity Questionnaire (IPAQ). The questionnaires were filled online while at school using the INDARES internet-based system. For the statistical processing of data, we used the methods of ANOVA, Pearson’s correlation coefficient, Kruskal-Wallis Test, and Mann Whitney U Test. Results: The achieved results confirmed significant differences in engaging in PA on the part of boys (p=0.01). There are also differences between grades, and in boys also in different forms of strain; in girls only in the overall volume of PA (0.01). In the context of individual countries, significant differences exist in both boys and girls equally. Pupils from the Slovak Republic report the highest levels of PA, while those form Hungary report the lowest PA (p=0.01). When evaluating overall BMI as well as BMI in individual countries, the difference between genders has been confirmed to the boys’ disadvantage (p=0.01). This statement also applies to comparison of individual countries, where the Czech students have the highest BMI and the Slovak students the lowest. Conclusion: Overall, as well as in terms of specific grades, boys are more physically active than girls. In all cases, they also have higher BMI. No significant relation has been proven between the volume and intensity of PA and BMI in both genders.
Digital didactical designs: Re-imagining designs for teaching and learning using media tablets

Isa Jahnke, Andreas Olsson, Anders Norberg, Lars Norqvist 1 Umea University, Dep. of Applied Educational Sciences, IML, Sweden, isa.jahnke@umu.se 2 Umea University, Dep. of Applied Educational Sciences, IML, Sweden, andreas.olsson@umu.se 3 Umea University, Dep. of Applied Educational Sciences, IML; Sweden, anders.norberg@skelleftea.se 4 Umea University, Dep. of Applied Educational Sciences, IML; Sweden, lars.norqvist@umu.se
Hemispheric Asymmetry and Multisensory Learning Impact Visual Perceptual Selection

Author(s): Piazza, Elise Ann | Advisor(s): Silver, Michael A; Banks, Martin S | Abstract: When visual input is consistent with multiple perceptual interpretations (e.g., the Necker cube), these interpretations compete for conscious awareness. The process of determining which interpretation will be dominant at a given time is known as perceptual selection. We study this process using binocular rivalry, a bistable phenomenon in which incompatible images presented separately to the two eyes result in perceptual alternation between the two images over time. In one study, we showed that a well-established asymmetry in spatial frequency processing between the brain’s two hemispheres applies to perceptual selection. Specifically, a lower spatial frequency grating was more likely to be selected when it was presented in the left visual field (right hemisphere) than in the right visual field (left hemisphere), while a higher spatial frequency grating showed the opposite pattern of results. Surprisingly, this asymmetry persisted for the entire duration of a continuously presented pair of static rivaling stimuli (30 seconds), which is the first demonstration that hemispheric differences in spatial frequency processing continue long after stimulus onset.In another study, we found that very recently formed audio-visual associations influence perceptual selection. Here, we used a brief (8-minute) crossmodal statistical learning paradigm to expose subjects to arbitrary, consistent pairings of images and sounds. In a subsequent binocular rivalry test, we found that a given image was more likely to be perceived when it was presented with a sound that had been consistently paired with it during exposure than when presented with previously unpaired sounds. Our results indicate that the audio-visual associations formed during the brief exposure period influenced subsequent visual competition and that this effect of learning was largely implicit, or unconscious.Together, these results demonstrate an unconscious influence of complex perceptual mechanisms on perceptual selection, the process by which we form conscious representations of ambiguous sensory input.