Miracle-dz/UniFAR-FTUs huggingface dataset:
Computing data cubes over GPU clusters.

The data cube is a fundamental relational operator for decision support systems, thus very important for analytics. Unfortunately, a full data cube with all of its tuples has exponential complexity in terms of runtime and memory consumption as the dimensions increase linearly, so algorithms to reduce query response times continue under development. The problem stated in this work is: how can we reduce complex multidimensional queries response times from high dimensional data cubes? The problem is aggravated if recurrent updates occur and if there is a huge volume of high dimensional data to be managed. The hypothesis of this work is that clusters of CPU-GPU devices can speedup queries from high dimensional holistic data cubes that are updated constantly. The alternative solution presented in this work, named JCL-GPU-Cubing, partitions the base relation into multiple independent sub- cubes. These multiple sub-cubes represent a partial data cube to reduce the exponentiality and they are used to perform queries in CPU or in CPU-GPU computer architectures efficiently. The experimental evaluations using complex multidimensional queries demonstrated that the CPU cluster version scaled well when the base relation increased and the CPU-GPU version outperformed the CPU only version in certain scenarios.
CS:APP2e Web Aside ASM:OPT: Machine Code Generated with Higher Levels of Optimization

value Program value a1 ls a2 sofar Cond(a1, a2) ls == NULL Result(a1, a2) sofar Nval1(a1, a2) ls->next Nval2(a1, a2) sofar + ls->val We can transform the general, tail-recursive function into a version that uses iteration as follows: itail(a1, a2) { while (! Cond(a1, a2)) { na1 = Nval1(a1, a2); na2 = Nval2(a1, a2); a1 = na1; a2 = na2; } return Result(a1, a2); } In this code, we keep updating the variables a1 and a2 in the same manner as we would via repeated calls to the recursive function. (We show this as first computing values na1 and na2 and then assigning these to a1 and a2 to avoid any inconsistency of having the computation of Nval2 use the updated version of a1.) Once we reach the terminating condition, we can simply return the value computed by the base case. If we apply this transformation to the tail-recursive list summation example, we get the following code: 1 int sum_list_itail(list_ptr ls, int sofar) { 2 while (! (ls == NULL)) { 3 list_ptr nls = ls->next; 4 int nsofar = sofar + ls->val; 5 ls = nls; 6 sofar = nsofar; 7 } 8 return sofar;
Chaos quantum-behaved particle swarm optimization algorithm

Using quantum-behaved particle swarm optimization(QPSO) to handle complex functions with high-dimension has the prob-lems of low convergence speed and sensitivity to local convergence.The chaos quantum-behaved particle swarm optimization algorithm(CQPSO) is proposed.The method of judging the local convergence by the variance of the population's fitness is proposed,which enhances searching efficiency.Numerical simulation results show that CQPSO is of high efficiency,and of excellent optimum perfor-mance.Especially it's of strong ability to avoid running into local optima.It is of much better performance to PSO and QPSO.
Time-varying homotopy and the animation of facial expressions for 3D virtual space teleconferencing

A homotopy describes the transformation of one arbitrary curve into another that shares the same endpoints. In this paper, we propose a deformable cylinder model, based on homotopy, in which an arbitrary surface interpolated between two contours via a blending function is transformed into another surface over time. We then show how this homotopic deformation can be applied to the realistic animation of human faces in a virtual space teleconferencing system. Specifically, we show that facial expressions such as wrinkling of the forehead and opening and closing of the mouth can be synthesized and animated in real time through 3D homotopic deformations.
What Functional Programming Can Do for FreeBSD and Vice Versa

In the last decades, scene of functional programming has changed dramatically as many new compilation and programming techniques were introduced, and then became available. Many research eorts put into development of better languages and compilers is focused in Haskell, a standardized, generalpurpose, lazy, pure functional language. It supports several features to make today’s software engineering easier with a minimal loss in performance. This paper investigates the possibilities on how to employ these features in supporting the development of an “unknown giant among free operating systems”, namely FreeBSD. On the other hand, FreeBSD, as a descendant of AT&T UNIX via the Berkeley Software Distribution, represents reliable and stable UNIX-compliant internals and system APIs, which might help to keep development of the de facto standard Glasgow Haskell Compiler in a reasonably good shape. We believe that these open source projects would be able to help each other in a promising way.
Design and implementation of an adaptive compensator for delay of tracker in tracking system

Delay of tracker in a tracking system includes sensor integration time, sensor readout time, any A/D, D/A conversion time and transportation time from tracker to the servo control system. These delays produce a harmful effect on stability and tracking accuracy of the system. For this reason, the effect of tracker delay on the tracking system is quantitatively analyzed in theory. A method for delay prediction and compensation, which is based on normalized LMS algorithm, is proposed. Results of experiments demonstrate the effectiveness of the method.
Metonymy and cognitive operations

This chapter begins with a distinction made in Ruiz de Mendoza and Pena (2005) between two broad kinds of cognitive operations: formal and content operations. The former are higher-level processes whose activity is necessary for lower-level processes to take place. The latter are used to make inferences on the basis of cues provided by the linguistic expression and its context. The chapter proposes that metonymy can be broken down into two content operations, domain expansion and domain reduction, the latter of which can be further broken down into other two processes: facetization and zone activation. It then explores how these operations relate to or contrast with other content operations and the way in which they are supported by formal operations.
Intuitionistic fuzzy theory for soft-computing: more appropriate tool than fuzzy theory

The entire work reported here is a philosophy based theoretical presentation. In this work it has been justified that intuitionistic fuzzy theory is more appropriate tool than fuzzy theory for soft-computing. Any association of computing methodologies centered on fuzzy set theory is well regarded as one kind of Softcomputing. Soft computing with fuzzy theory involves fluent use of both μ(x) and μ(x) i.e. ν(x) of the elements of all the universes of the concerned decision problem. The role of both μ(x) and ν(x) are very fluent in solving decision problems in almost all application areas of fuzzy theory. Recent literatures reveal that fuzzy set theory may not be an appropriate model to deal with ill-defined large size decision problems. In this work the author very precisely unearths the weakness of fuzzy theory in a further dimension, which is not caused due to μ(x) but due to the other part ν(x) of the coin. Doing a rigorous exercise with few real life examples it is observed that the fuzzy set theory is inappropriate not only for large size decision problems but also for many decision problems, irrespective of its size, large or small, in real life environment. Consequently, a set of necessary eligibility conditions is proposed at least one of which is to be mandatorily satisfied by any fuzzy decision maker before using ‘Fuzzy Set Theory’ in SoftComputing. The initial content of this paper deals with a basic question: Is ‘Fuzzy Theory’ really always good for soft-computing? In the Theory of CIFS it is justified that while solving any decision making problem, the selection of a suitable soft-computing set theory or crisp theory is made by the concerned decision maker by his own choice and own knowledge, which functions at the outer sphere of the cognition system of the decision maker. It is analogous to the case of a computer programmer who solves a problem by writing codes in a
Synchrophasor Calculation Algorithm Based on Robust State Estimation

This paper proposes a novel robust state estimation based method for synchrophasor calculation. With the Tukey's Bi-Square technique, the traditional Weighted Least Square (WLS) based state estimation method is upgraded to the proposed Iteratively Reweighted Least Square (IRLS) based robust state estimation method, to better filter out measurement noise and reject bad data. Extensive numerical experiments demonstrate that the proposed method has higher synchrophasor calculation accuracy compared to existing IEEE e37.118 synchrophasor standard, independent of measurement noise level, bad data level and system frequency.
Peer Rejection In Childhood

Thank you for downloading peer rejection in childhood. Maybe you have knowledge that, people have look hundreds times for their chosen readings like this peer rejection in childhood, but end up in infectious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they juggled with some malicious virus inside their desktop computer. peer rejection in childhood is available in our book collection an online access to it is set as public so you can download it instantly. Our book servers hosts in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the peer rejection in childhood is universally compatible with any devices to read.
Development of Dual Response Approach using Artificial Intelligence for Robust Parameter Design

Prediction process of parameters in robust design is very important. If the prediction results is fairly precise then the quality improvement process will economize time and reduce cost. Dual response approach based on response surface methodology has widely investigated. Separately estimating mean and variance responses, dual response approach may take advantages of optimization modeling for finding optimum setting of input factors. A sufficient number of experimentations are required to improve the precision of estimations. This research recommended an alternative dual response approach without performing experiments. A hybrid neural network-genetic algorithm has been applied to model relationships between responses and input factors. Mean and variance responses conform to output nodes while input factors are used for input nodes. Using empirical process data, process parameter can be predicted without performing real experimentations. A genetic algorithm has been applied to obtain the input factors optimum setting. An example has been studied to demonstrate the procedures and applicability of the proposed approach.
Low Cost, High Performance Automatic Motorcycle Helmet Violation Detection

Road fatality rates are very high, especially in developing and middle-income countries. One of the main causes of road fatalities is not using motorcycle helmets. Active law enforcement may help increase compliance, but ubiquitous enforcement requires many police officers and may cause traffic jams and safety issues. In this paper, we demonstrate the effectiveness of computer vision and machine learning methods to increase helmet compliance through automated helmet violation detection. The system detects riders and passengers not wearing helmets and consists of motorcyclist detection, helmet violation classification, and tracking. The architecture of the system comprises a single GPU server and multiple computational clients that cooperate to complete the task, with communication over HTTP. In a real-world test, the system is able to detect 97% of helmet violations with a 15% false alarm rate. The client-server architecture reduces cost by 20-30% compared to a baseline architecture.
Investigation of plasmid DNA and antibiotic resistance in some pathogenic organisms

Twenty-eight strains of Salmonella, Pseudomonas, and Escherichia coli isolated from cultures of stool, urine and wound were tested for their susceptibility to various antimicrobial agents. All the strains were resistant to erythromycin and tetracycline. Nineteen Salmonella isolates were susceptible to chloramphenicol and gentamycin. All the Pseudomonas and Escherichia coli isolates were resistant to chloramphenicol and cloxacillin while one Pseudomonas species and three Escherichia coli isolates were sensitive to gentamycin. The antibiotic resistance determinants in each strain were encoded by plasmid. These isolates were typed by plasmid profile analysis according to their different molecular weights. Several similar and distinct profiles were identified for most resistant and sensitive isolates. It appeared that a single strain containing a plasmid conferring multiple drug resistance emerged within the bacterial population and was able to adapt and to survive the challenges of antibiotics as they were introduced into clinical medicine. Therefore, the acquisition of plasmid has greatly contributed to the rapid spread of antibiotic resistance genes in the bacterial population.
Development of the Hippocampal Learning Algorithm Using Associate Memory and Modulator of Neural Weight

In this paper, we propose the development of MHLA(Modulatory Hippocampus Learning Algorithm) which remodel a principle of brain of hippocampus. Hippocampus takes charge auto-associative memory and controlling functions of long-term or short-term memory strengthening. We organize auto-associative memory based 3 steps system(DG, CA3, CAl) and improve speed of learning by addition of modulator to long-term memory learning. In hippocampal system, according to the 3 steps order, information applies statistical deviation on Dentate Gyrus region and is labelled to responsive pattern by adjustment of a good impression. In CA3 region, pattern is reorganized by auto-associative memory. In CAI region, convergence of connection weight which is used long-term memory is learned fast by neural networks which is applied modulator. To measure performance of MHLA, PCA(Principal Component Analysis) is applied to face images which are classified by pose, expression and picture quality. Next, we calculate feature vectors and learn by MHLA. Finally, we confirm cognitive rate. The results of experiments, we can compare a proposed method of other methods, and we can confirm that the proposed method is superior to the existing method.
Rancang Bangun Sistem Temu Kembali Informasi (Information Retrieval System) Dokumen Berbahasa Jawa menggunakan Metode DICE Similarity

Implementasi dokumen teks bahasa jawa masih banyak dijumpai baik secara onlinemaupun offline. Pencarian dokumen teks bahasa jawa dengan menggunakan mesin pencari atauSistem Temu Kembali Informasi (STKI) yang tersedia menghasilkan suatu hasil pencariandengan dokumen terambil yang banyak (recall tinggi) sehingga hasil menjadi kurang akurat(precision rendah). Tujuan pembuatan rancang bangun STKI dengan metode DICE Similarityagar user mudah dalam melakukan pencarian dokumen teks berbahasa Jawa. Software STKIdirancang untuk memberikan hasil pencarian dokumen dalam jumlah recall rendah danprecision tinggi menggunakan metode pemeringkatan DICE Similarity, sehingga user akanmendapatkan hasil pencarian cepat dan akurat. Metode DICE Similarity akan melakukanpembobotan tiap dokumen yang ada pada database sehingga antar dokumen memiliki bobotyang berbeda untuk menentukan dokumen mana yang paling mirip (similar) dengan query.Rangking teratas akan ditempati oleh dokumen dengan bobot tertinggi hasil pencarian Evaluasihasil pencarian IRS dilakukan dengan uji recall dan precision dengan model persepsi. STKImampu melakukan pencarian dokumen dan menampilkan hasil pencarian dokumen memilikirata-rata recall 0,04 dan rata-rata precision 0,83. Hasil STKI dilengkapi dengan bobot tiapdokumen dan letakknya yang akan memudahkan user dalam pencarian dokumen teks bahasaJawa.
Cyber Security Challenges and Big Data Analytics

More than half of the global population is involved in the cyber world. With this massive population there is a generation of large amount of data. These data are of high-volume variety and velocity, which is collectively termed as Big data. The security of these data is of primary importance. Cybercrimes, threats are also taking the form of big data. We cannot deny the fact that we have conventional cyber security application that filters the anomalous traffic that are seamlessly working fine unto this point. But due to rapid expansion on data and threats these applications need to be updated. Big data analytics tool when incorporated with these traditional approaches may help to find the efficient measure to defeat security attacks. Here in this paper we have gone through some of the major cyber security threats, some of the conventional Cyber security application and try to find how big data analytics can be incorporated in those application to obtain the reliable outcome.
Web Browser Network Based on a BA Model for a Web-Based Virtual World

Real-time web applications such as a virtual world require considerable computing resources. However, as the number of servers increases, so does the maintenance and financial cost. To share tasks among web browsers, the browsers must share data. Therefore, a network must be constructed among the web browsers. In this paper, we propose the construction of a web browser network based on the Barabasi–Albert model (BA model). We focus on a web-based multiplayer online game that requires higher frequent communication and significant computing resources. We attempt to optimize computing resource utilization for web browsers. We improve upon the method in our previous study, which constructed a network for a web-based virtual world, using only location information. When a new user logged into a world, the web browser connected to two other browsers whose users had a location close to that of the user. The experimental results of that method showed 50% data coverage, which was insufficient to display the game screen because the web browser displays the characters on the virtual world. In this study, we attempt to use the BA model to construct more efficient networks than those in the previous study to increase data coverage. Our new method uses the number of connections of the web browser and location information to calculate the probability of web browser selection. The experimental results show that the data coverage exceeds 90%, indicating significant improvement over the previous method.
Reference advice for word sense disambiguation

Much of the world's knowledge is encoded in natural language. Accessing this information would be invaluable for applications such as agent systems, question answering, the semantic web, expert systems, and many more. However, language is very ambiguous – each word in a natural language utterance can have a variety of meanings. Word sense disambiguation is the task of determining the dictionary (or lexical) sense of each word in a context. Knowing which sense a word represents allows us to ascertain its meaning. In this work, we introduce a new resource for WSD processing: reference advice. Reference advice uses reference resolution methods to infer potential meanings of referring expressions, such as 'he', 'she,' 'this,' 'it' and many more. We present a method for creating reference advice, and incorporating that advice into WSD processing. We also develop several system configurations which act as points of comparison for evaluation. Lastly, we discuss the impact of the success of reference advice in supporting WSD processing. We explore a shift away from pipeline text processing architectures, wherein each processing component feeds its results downstream until a final processing component presents a solution. We discuss how our system represents a step toward text processing architectures where each processing component is able to provide input to many other processing components, not just those components that are rigidly positioned downstream.
Endoscopic urethral realignment of traumatic urethral disruption: A monocentric experience

Introduction: The optimal management strategy for urethral injuries remains controversial. Suprapubic cystostomy followed by delayed urethral reconstruction or immediate primary urethral injury repair is associated with high complication rate. Early endoscopic realignment for urethral injuries provides early definitive procedure with low complications and avoids the need for further urethral reconstruction. Materials and Methods: Between April 2007 and March 2016, 27 patients with traumatic posterior urethral disruptions were reviewed. All patients were managed by endoscopic urethral realignment. Patients were followed up every 3 months for 18–36 months regarding incontinence, potency, and urine flow. Results: Endoscopic urethral realignment was performed immediately in 21 patients and was delayed in 6 patients. It was successful in 24/27 (88.8%) patients. During the procedure, the proximal urethral end was identified directly in four patients, and by the guidance of methylene blue injection, guidewire or endoscopic light in three, nine, and eight patients, respectively. The Foley catheter was removed after 9 weeks in 17/24 (70.8%) patients and after 12 weeks in 7/24 (29.2%) patients. Six months after realignment, minimal urethral stricture was developed in 4/24 (16.7%) patients. All of them were treated successfully with single session of internal urethrotomy without recurrence after 1-year follow-up. After 18–36-month follow-up period, all patients were potent, continent, and with satisfactory voiding pattern. Conclusions: Endoscopic primary realignment of posterior urethral rupture is less invasive and a safer procedure, without pelvic hemorrhage or additional injuries. It has low incidence of severe urethral stricture and avoids the need for delayed open urethral reconstruction.
Natural Language Parsing : Progress and Challenges

Natural language parsing is the task of automatically obtaining the syntactic structure of sentences written in a human language. Parsing is a crucial step for language processing systems that need to extract meaning from text or speech, and thus a key technology of artificial intelligence. This article presents an outline of the current state of the art in this field, as well as reflections on the main challenges that, in the author’s opinion, it is currently facing: limitations in accuracy on especially difficult languages and domains, psycholinguistic adequacy, and speed.