Miracle-dz/UniFAR-FTUs huggingface dataset:
A 125-MHz CMOS mixed-signal equalizer for Gigabit Ethernet on copper wire

A discrete-time mixed-signal linear equalizer designed for the analog front end of Gigabit Ethernet receivers performs cable equalization while relaxing the A/D converter complexity. Based on a coefficient-rotating FIR filter architecture, the circuit incorporates 8 taps that are adapted to the cable characteristics by means of an LMS algorithm. A distributed array of interleaved sampling circuits and a linear low-voltage multiplier topology allow both high speed and low power dissipation. Fabricated in a 0.25-/spl mu/m digital CMOS technology, the equalizer operates at 125 MHz while dissipating 75 mW from a 2.5-V power supply.
Improving Performance in Cloud using Multi-Job Scheduling based Group Discovery Algorithm

computing is a large model change of computing system. It provides high scalability and flexibility among an assortment of on-demand services. To imporve the performance of the multi-cloud environment in distributed application might require less energy efficiency and minimal inter-node latency correspondingly. The major problem is that the energy efficiency of the cloud computing data center is less if the number of server is low, else it increases. To overcome the energy efficiency and network latency problem a novel energy-efficient particle swarm optimization representation for multi-job scheduling and Latency representation for the grouping of nodes with respect to network latency is proposed. Design a realistic particle swarm optimization algorithm for the cloud servers and construct an overall energy competence based on the purpose of the servers and calculation of fitness value for each cloud servers. Also, in order to speed up the convergent speed and improve the probing aptitude of our algorithm, a local search operative is introduced. Finally, the experiment demonstrates that the proposed algorithm is effectual and well-organized.
Probabilistic constraints and syntactic ambiguity resolution

Abstract Natural languages contain probabilistic constraints that influence the resolution of ambiguities. Current models of sentence processing agree that probabilistic constraints affect syntactic ambiguity resolution, but there has been little investigation of the constraints themselves-what they are, how they differ in their effects on processing, and how they interact with one another. Three different types of probabilistic constraints were investigated: “pre-ambiguity” plausibility information, information about verb argument structure frequencies, and “post-ambiguity” constraints that arrive after the introduction of the ambiguity but prior to its disambiguation. Reading times for syntactically ambiguous sentences were compared to reading times for unambiguous controls in three self-paced reading experiments. All three kinds of constraints were found to be helpful, and when several constraints converged, ambiguity resolution was facilitated compared to when constraints conflicted. The importance of.
Visualizing and Querying Community Survey Data with Google Maps

This article describes how an online survey was enhanced using Google Maps® to visualize public concerns regarding a community's water/sewer quality while revealing patterns that could indicate a potential source of the identified problems. Online surveys have become part of the standard toolkit for many County Extension Offices; however, these tools are limited when it comes to surveys involving geospatial information. Through a few simple steps when developing the survey, collected data can be converted for use in a GIS or online geospatial viewer.
Prevalence and Characterization of Antibiotic Resistance Food Borne Pathogens Isolated from Locally Produced Chicken Raw Meat and their Handlers

The present study was undertaken to determine the prevalence and the potential public health significance of Salmonella (S.)serovars, Escherichia coli (E.coli), and Staphylococcus aureus (S. aureus) in raw chicken meat which is available in domestic retail shops as well as their handlers in Mansoura City, Dakahlia Governorate, Egypt. Samples of retail raw chicken meat (n = 200) as well as equal sized samples of hand swabs and stool specimens (n = 50) from retail handlers were bacteriologically tested E.coli, Staph. aureus and Salmonellaspp. , wererecovered from the raw chicken meat at the following percentages: (35, 22 and 5%, respectively) using conventional biochemical identification methods. Serotyping of the obtained Salmonella spp. , revealed that Salmonella Kentuckypresented at the highest rate of isolation followedby Salmonella Enteritidis, Salmonella Infantis and Salmonella Typhimurium.High frequency of S. aureus were found to colonize the skin (40%) and the stool specimens (30%) of chicken meat handlers; whereas four out of 50 stool samples (8%) and one out of 50 hand swabs (2%) from handlers were found to be contaminated with Salmonella spp.E.coli was also detected in 40% of the stool samples and in 24% of handlers hand swabs. Serological identification of E. coli isolates revealed the presence of E. coli(O26: H11, O103:H2, O128:H2, O111:H2 andO78) in the examined raw meat, O26: H11, O2:H4 and O128:H2 in stool samples and O26: H11,O103:H2 and O125:H21 in hand swabs.All recovered isolates showed various degree of antibiotic resistance. It becomes apparent that retail chicken raw meat sold at the respective shops at Mansoura city is highly contaminated with food-borne pathogens which is considered a potential vehicle for transmitting food-borne diseases. Hence there is an urgent need for increased implementation of consumer food safety education efforts.
A comparison of techniques for name matching.

Information explosion is a problem for everyone nowadays. It is a great challenge to all kinds of businesses to maintain high quality of data in their information applications, such as data integration, text and web mining, information retrieval, search engine, etc. In such applications, matching names is one of the popular tasks. There are a number of name matching techniques available. Unfortunately, there is no existing name matching technique that performs the best in all situations. Therefore, a problem that every researcher or a practitioner has to face is how to select an appropriate technique for a given dataset. This paper analyses and evaluates a set of popular name matching techniques on several carefully designed different datasets. The experimental comparison confirms the statement that there is no clear best technique. Some suggestions have been presented, which can be used as guidance for researchers and practitioners to select an appropriate name matching technique in a given dataset.
Affricates, Palatals and Iotization in Serbian: Representational Solutions to Longstanding Puzzles

This paper investigates the segment inventory of Serbian from both an articulatory and a phonological perspective, and it proposes a straightforward phonological analysis of both the inventory and the morpho-phonological process of iotization. The analysis is couched in terms of the Parallel Structures Model of feature geometry (Moren 2003a, b, c).
An Integrated Product Design and Selection Approach Using Grey QFD and Grey TOPSIS Methods

Quality function deployment (QFD) is a successful product design tool for analyzing customer demands. Recently, researchers have combined QFD and grey system theory for solving small size, uncertain and incomplete data problems. However, some problems, such as single grey values, competitive analysis and prototype product selections, require further investigation. To resolve these issues, this study integrates interval grey numbers, QFD and TOPSIS techniques to develop a grey product design and selection (GPDS) method. GPDS can help product developers identify important engineering characteristics, perform a competitive analysis and select the best prototype product(s) under a small size and uncertain data environment. A real-world case study (paper facial mask) is offered to explain the research process of the GPDS method. The case results indicate that GPDS helps the case company identify important engineering characteristics, understand the intended market position of a product and select the best prototype product.
Metode Smoothing dalam Naïve Bayes untuk Klasifikasi Email Spam

Berkirim surat merupakan kegiatan yang sering dilakukan. Jika beberapa tahun sebelumnya masih sering digunakan pos sebagai sarana berkirim surat, kini dilakukan melalui email . Keunggulan yang dimiliki email dibandingkan dengan surat konvensional yang dikirimkan melalui pos ternyata banyak disalahgunakan sebagai sarana pengiriman pesan massal ( spam ). Dengan adanya masalah kehadiran spam tersebut maka muncul piranti lunak spam filter untuk mengklasifikasikan email . Naive Bayes banyak digunakan sebagai teknik dalam klasifikasi oleh pengembang spam filter . Pada teknik Naive Bayes tersebut terdapat metode smoothing yang telah umum digunakan yaitu Laplace smoothing . Selain metode smoothing tersebut terdapat 4 metode smoothing lainnya yaitu Jelinek-Mercer (JM) smoothing , Dirichlet (Dir) smoothing , Absolute Discounting (AD) smoothing , dan Two-Stage (TS) smoothing. Keempat metode smoothing diduga mampu meningkatkan akurasi hasil klasifikasi dan belum pernah diimplementasikan sebelumnya pada pengembangan spam filter . Penelitian ini membandingkan keempat metode smoothing dalam pengklasifikasian email.
Message passing in complex irregular systems

MPI is a standard that is well adapted to the needs of the parallel computing community, where performance is a primary concern. However, there is a lack of standards and tools well suited to message passing in complex irregular systems. This paper presents a set of message passing requirements for complex irregular systems. These requirements lead to the implementation of a communication library layered over MPI to provide a higher level of abstraction. The implementation of a training system for the crew of maritime patrol aircraft based on this communication library demonstrates its applicability to solve complex irregular problems.
Biologically-inspired control framework for insect animation

Insects are common in our world, such as ants, spiders, cockroaches etc. Virtual representations of them have wide applications in Virtual Reality (VR), video games and films. Compared with the large volume of works in biped animation, the problem of insect animation was less explored. Their small body parts, complex structures and high-speed movements challenge the standard techniques of motion synthesis. This thesis addressed the aforementioned challenge by presenting a framework to efficiently automate the modelling and authoring of insect locomotion. This framework is inspired by two key observations of real insects: fixed gait pattern and distributed neural system. At the top level, a Triangle Placement Engine (TPE) is modelled based on the double-tripod gait pattern of insects, and determines the location and orientation of insect foot contacts, given various user inputs. At the low level, a Central Pattern Generator (CPG) controller actuates individual joints by mimicking the distributed neural system of insects. A Controller Look-Up Table (CLUT) translates the high-level commands from the TPE into the low-level control parameters of the CPG. In addition, a novel strategy is introduced to determine when legs start to swing. During high-speed movements, the swing mode is triggered when the Centre of Mass (COM) steps outside the Supporting Triangle. However, this simplified mechanism is not sufficient to produce the gait variations when insects are moving at slow speed. The proposed strategy handles the case of slow speed by considering four independent factors, including the relative distance to the extreme poses, the stance period, the relative distance to the neighbouring legs, the load information etc. This strategy is able to avoid the issues of collisions between legs or over stretching of leg joints, which are produced by conventional methods. The framework developed in this thesis allows sufficient control and seamlessly fits into the existing pipeline of animation production. With this framework, animators can model the motion of a single insect in an intuitive way by specifying the walking path, terrains, speed etc. The success of this framework proves that the introduction of biological components could synthesise the insect animation in a naturalness and interactive fashion.
From Text to Facts: Recognizing Ontological Facts for a New Application

Fact extraction methods are used for various aims. Here, a new method is introduced for a new application - computing semantic relatedness of texts. Ontological facts are the facts about real world that are available in a knowledgebase called ontology. There are many systems to extract ontological facts from a text. State-of-the-art of these systems is SOFIE that is not appropriate to compute semantic relatedness of texts. In this paper, this problem will be explained and a new ontological facts extraction system is introduced that is appropriate for computing semantic relatedness of texts. For this aim, YAGO ontology will be used as a background knowledge and facts resource. In final it will be suggested that using YAGO relations can optimize ontological facts extraction system.
Improving Safety in Spine Surgery: Reducing Perioperative Blood Loss and Transfusion Requirements

Significant blood loss is associated with increased complications in spine surgery. Decreasing the rate of blood loss, salvaging lost blood, and decreasing the need for transfusion are three techniques to help minimize the hemodynamic complications associated with major spinal surgery. Herein, we present a concise overview of preoperative, intraoperative, and postoperative methods for decreasing blood loss and need for allogeneic transfusion.
GPU-enhanced power flow analysis

This thesis addresses the utilization of Graphics Processing Units (GPUs) to improve the Power Flow (PF) analysis of modern power systems. GPUs are powerful vector co-processors that have been very useful in the acceleration of several computational intensive applications. PF analysis is the steady-state analysis of AC power networks and is widely used for several tasks involved in system operation and planning. Currently, GPUs are challenged by applications exhibiting an irregular computational pattern, as is the case of most known methods to solve the PF analysis. At the same time, the PF analysis needs to be improved in order to cope with new requirements of efficiency and accuracy coming from the Smart Grid concept. The relevance of GPU-enhanced PF analysis is twofold. On one hand, it expands the application domain of GPUs to a new class of problems. On the other hand, it consistently increases the computational capacity available for power system operation and design. What specific features of GPUs can be used to enhance crucial aspects of PF analysis? What algorithms or models for PF analysis are best suited to the massively parallel architecture of GPUs? The present work attempts to answer such questions in two complementary ways: (i) by developing novel GPU programming strategies for available PF algorithms, and (ii) by proposing novel PF analysis methods that can exploit the numerous features present in GPU architectures. The proposed methods are implemented using state-of-the-art programming frameworks and paradigms, and tested over state-of-the-art GPU and CPU architectures. Results are discussed and compared with existing solutions from the literature of the field, leading to relevant conclusions and guidelines for future research. Specific contributions to GPU computing include: (i) a comparison of two programming paradigms, namely regularity and load-balancing, for implementing the so-called treefix operations; (ii) a study of the impact of the representation format over performance and accuracy, for fuzzy interval algebraic operations; and (iii) the utilization of architecture-specific design, as a novel strategy to improve performance scalability of scientific applications. Contributions to PF analysis include: (i) design and evaluation of a novel method for the uncertainty assessment, based on the fuzzy interval approach; and (ii) the development of an intrinsically parallel method to solve the PF analysis, which is not affected by the Amdahl's law.
Evaluation of Postprandial To Fasting Insulin Ratio as a Biochemical Marker of Insulin Resistance in Obese Indian Children

Background:Insulin resistance in childhood obesity is a well recognised and worrisome complication. Reliable measurements of insulin resistance require invasive or multiple sampling procedures or complex mathematical calculations which are difficult to practice in routine office practice. Objective:This observational study was aimed to evaluate a simple ratio of 120 minute post glucose load (OGTT) insulin to fasting insulin for diagnosis of insulin resistance in obese children having clinical markers of insulin resistance and with a HOMA-IR value suggestive of insulin resistance for age and sex. Methods:Forty Eight obese children and adolescents (35 girls and 34 boys; mean age 10.19 ± 4.23 years and mean BMI 26.17± 3.76) were included in the study. All participants underwent an OGTT. Blood samples were obtained 0 and 120 minutes after oral glucose administration for glucose and insulin measurements, and 2 separate groups were studied in both pubertal and pre-pubertal age group, according to the presence or absence of insulin resistance as per the standard HOMA-IR cut-offs. The ratio of 120 minute and fasting insulin, measurements was termed as insulin resistance Index (Ri) and was calculated for both pre-pubertal and pubertal children with insulin resistance. The accuracy of this measurement in measuring Insulin Resistance and the optimal insulin resistance index (Ri) for diagnosis of insulin resistance was established with a receiver operating characteristic (ROC) curve. Results:The area under the curve for pre-pubertal children was 0.7(close to upper left) and yielded a sensitivity of 76% and specificity of 70% at Ri value of 4.7. The value of positive likelihood ratio comes at 2.53, pretest probability of 0.61, pretest odds of 1.56, post-test odds of 3.95 and post-test probability of 0.78. However ROC plot had area under the curve of only 0.3 in pubertal children (close to bottom right). Conclusion:Ri is a sensitive and specific test for calculation of insulin resistance in pre pubertal children but not accurate in pubertal children.
Design and Simulation of MC-CDMA Transceiver via Slantlet Transform

Wireless communications is a rapidly growing piece of the communications manufacturing, with the potential to provide high-speed high-quality information exchange between the portable devices located anywhere in the world. Multi-Carrier Code Division Multiple Access (MC-CDMA) has emerged recently as a promising candidate for the next generation broad-band mobile networks. Recently, it was found that Slantlet transform (SLT) based Orthogonal Frequency Division Multiplexing (OFDM) is capable to reduce the Inter Symbol Interference (ISI) and the Inter Carrier Interference (ICI), which are caused by the loss of orthogonality between the sub- carriers. SLT-OFDM can support higher spectrum efficiency than Fast Fourier Transform-based OFDM (FFT-OFDM) due to the elimination of the Cyclic Prefix (CP). In this paper, a novel SLT-MC-CDMA transceiver design is presented based on the SLT-OFDM that is used as a basic building block in the design of MC-CDMA transceiver to maintain the orthogonality against the multi- path frequency Selective Fading Channels (SFC). Simulation results are provided to demonstrate the significant gain in the performance of the proposed technique. The Bit Error Rate (BER) of SLT-MC-CDMA scheme is compared with FFT-MC-CDMA and tested in Additive White Gaussian Noise (AWGN), Flat Fading and Selective Fading Channels (SFH). The simulation results confirmed that, the proposed system outperforms the reference one.
A new kind of dictionary for Shakespeare's plays: an immodest proposal

The language of Shakespeare’s plays has received substantial treatment in various ‘dictionaries’, ‘glossaries’, ‘lexica’ and ‘concordances’. However, the classic works are written in the philological tradition that characterised the Oxford English Dictionary. This paper explores how modern principles and techniques developed in Corpus Linguistics can be deployed in the creation of a radically new kind of dictionary. In particular, this involves a focus on usage and frequency. A further innovation is that the proposed dictionary will be comparative, making both internal comparisons (e.g. female characters compared with male) and external comparisons (e.g. Shakespeare’s usage compared with that of contemporary plays and other genres). The bulk of this paper is made up of case studies, involving discussion of the words ‘horrid’, ‘good’, ‘ah’ and ‘and’, multiword units, and linguistic profiles for characters and plays. Through these, the aim is to demonstrate the characteristics of the dictionary and raise pertinent issues, including, for example, how many and what kind of words to include in the dictionary, whether the dictionary should include only words (and how they should be defined), how word-senses should be distinguished, how stylistic and social meanings should be captured, and what approach to grammar should be taken.
Tifinagh Character Recognition by Graphs Model Representation

Most methods of character recognition existing require lot of pre-processing. Also using the method's of artificial intelligent( neural network , SVM, .) request much resources and time (time necessary for the learning phase and recognition ) , and the scarcity of work aimed the Tifinagh language , we present a structural approach to recognition the Tifinagh characters using the graphs representation as a model for the offline recognition of Tifinagh characters. This representation allows extracting the basic form of each character and recognizes it by employing a pre-classification of the characters in order to earn time. The proposed approach shows its effectiveness against problems.
Acceleration of back propagation through initial weight pre-training with delta rule

A training strategy for backpropagation (BP) neural networks, named delta pre-training (DPT), is proposed. The core of the training strategy is based on pre-training the initial weights for BP networks using the delta rule, instead of using random values. After pre-training, the normal BP training procedure is carried out to complete network training. With the DPT, the convergence rate for training BP networks can be significantly improved. Since the DPT deals only with initial weight settings, most variations of the standard BP algorithm can be combined with the DPT so as to further speed up convergence. With regards to on-chip learning in VLSI implementations, only a little additional circuitry is required for the pre-training phase with the DPT. Simulation results using the proposed training method show its superiority over previous methods. <<ETX>>
Semi-global stabilization of linear systems with position and rate limited actuators

It is shown that, if a linear system is asymptotically null controllable with bounded controls, then, when subject to both actuator position and rate saturation, it is semi-globally stabilizable by linear state feedback. If, in addition, the system is also detectable, then it is semi-global stabilizable via linear output feedback.