Miracle-dz/UniFAR-FTUs huggingface dataset:
Vehicle Sideslip Estimation Design , implementation , and experimental validation

Control systems that help the driver avoid accidents, or limit the damage in case of an accident, have become ubiquitous in modern passenger cars. For example, new cars typically have an anti-lock braking system (ABS), which prevents the wheels from locking during hard braking, and they often have an electronic stability control system (ESC), which stabilizes the lateral motion of the vehicle to prevent skidding. Collision warning and avoidance, rollover prevention, crosswind stabilization, and preparation for an impending accident by adjusting seat positions and seat belts are additional examples of control systems for automotive safety. These systems rely on information about the state of the vehicle and its surroundings. To obtain this information, modern cars are equipped with various sensors. For a typical car with an ESC system, necessary measurements include the steering wheel angle, wheel angular velocities, lateral acceleration, and the rate of rotation around the vertical body-fixed axis, known as the yaw rate. These measurements alone contain a great deal of information about the state of the vehicle. The speed of the car can be estimated using the wheel angular velocities, and a linear reference model taking the speed, steering wheel angle, and additional measurements as inputs can be used to predict the behavior of the car under normal driving conditions. The predicted behavior can be compared to the actual behavior of the car; ESC systems, for example, use the brakes to correct the deviation from a yaw reference model when the vehicle starts to skid [1]. Although some quantities are easily measured, others are difficult to measure because of high cost or impracticality. When some quantity cannot be measured directly, it is often necessary to estimate it using the measurements that are available. Observers combine the available measurements with dynamic models to estimate unknown dynamic states. Often, dynamic models of sufficient accuracy are not available, and must be carefully constructed as part of the observer design. The observer estimates can be used to implement control algorithms, as Figure 1 illustrates.
Organizational Determinants of Corporate IaaS Usage

This paper presents and tests a set of hypotheses explaining corporate IaaS (Infrastructure-as-a-Service) acceptance and adoption. The empirical results are based on an online survey involving 226 IT executives and IT infrastructure experts. We identify (1) perceived usefulness and ease of use as drivers of attitude towards IaaS usage, (2) attitude, perceived usefulness, and subjective norm as strongest drivers of intention to use, and, (3) intention to use as driver of IaaS adoption. The theoretical and practical implications of this model are discussed.
Improving the performance of wireless links via end-to-end standard TCP tuning techniques

The increasing popularity of wireless networks indicates that wireless links will play an important role in future internetworks. Wireless networks are characterized by limited bandwidth and frequent packet losses due to transmission errors and handoffs. Consequently wireless and other lossy links suffer from significant non-congestion-related losses due to the reasons above. In fact, reliable transport protocols such as TCP are tuned to perform well in traditional networks where packet losses occur mostly because of congestion. TCP responds to all losses by invoking congestion control and avoidance algorithms, resulting in degraded end-to-end performance and poor bandwidth usage. In this paper we propose end evaluate a basic framework of end-to-end (IETF) standard tuning techniques to optimize communication in this difficult error environment. We argue and demonstrate that most of these mechanisms, largely available in commercial network equipments, if properly configured and combined can achieve a satisfactory communication efficiency even in a highly variable error environment. Key-Words: Internet, Mobile Communication, Wireless links, TCP end-to-end tuning
Combining Analytical and Holistic Strategies for Handwriting Recognition

In this paper, a study is conducted on combining analytical and holistic strategies for handwriting recognition. Even though the big majority of the recent high recognition rate systems adopts analytical strategies, physiological scientists suggest that the holistic strategy is the key for realizing near-human performance. In what we believe is a fresh perspective on handwriting recognition, combining the two strategies results in improving recognition rate. The concept and the analysis of combining the two strategies is the first contribution of this work. The proposed approach is applied to an Arabic handwriting recognition system. We use a hidden Markov model (HMM)-based analytical recognizer, and four different methods combined as a holistic recognizer. Two of the used holistic recognition methods are novel: a method based on estimating the probability of writing in each pixel, and a method that uses the Hausdorff distance. The third holistic method is a connected components-based approach, and the fourth one uses dynamic time warping (DTW) with two modifications. These four methods and their use as a holistic recognizer are the second contribution of this work. The proposed technique is tested using the IFN/ENIT database. The combination between the holistic and the analytical recognizers has led to an evident recognition rate improvement ranging from 5% to 16% over the use of the analytical recognizer alone.
Doing two-level logic minimization 100 times faster

Two-level logic minimization consists in finding a minimal cost sum-of-products, i.e, disjunctive normal form, of a given Boolean function. This paper presents a new algorithm to solve this problem, and gives experimental evidences showing that it outperforms the leading minimizers of several orders of magnitude. We suspect that it may be possible to explain this improvement in performance theoretically, and hope that our empirical results will stimulate research along
When are Your Customers Active and is Their Buying Regular or Random? An Erlang Mixture State-Switching Model for Customer Scoring

Scoring customers with regard to the expected number of their future transactions is of fundamental interest to direct marketers. For the purpose of customer scoring, a variety of models have been developed, based on the negative binomial distribution. Extensions of such models allow for customer defection (“buy until you die”). We extend customer scoring models in two ways: (1) We assume that customers switch between an active state in which they buy and an inactive state in which they do not purchase. Switching between states can occur in both directions, implying that defection is temporary. (2) We allow for heterogeneity among households with regard to the regularity of purchasing. We achieve this by modeling purchasing in the active state as a mixture of Erlang-distributed interpurchase times. Our model nests the BG-NBD model and other variants of the NBD as special cases. We develop MCMC simulation methods for our model and apply it to five different data sets, comparing the results to several benchmark models. Our results imply that it is often erroneous to assume that customers “buy until they die”. , the results support the assumption of transient switching between an active and inactive state. We show that, when the model does not account for transient switching, customer scoring is biased. We discuss both the theoretical and managerial implications of our results, and potential areas for future research.
Prototyping recommender systems in jcolibri

Our goal is to support system developers in rapid prototyping recommender systems using Case-Based Reasoning (CBR) techniques. In this paper we describe how jCOLIBRI can serve to that goal. jCOLIBRI is an object-oriented framework in Java for building CBR systems that greatly benefits from the reuse of previously developed CBR systems. jCOLIBRI includes a case base of templates for case-based recommender systems that can be easily adapted to prototype a great variety of alternatives. We describe the contents of the case base and show experimental results from our experience using the recommender templates case base with mid-size projects from undergraduate students.
Refining segmental boundaries for TTS database using fine contextual-dependent boundary models

This paper proposed a post-refining method with fine contextual-dependent GMM for the auto-segmentation task. A GMM trained with a super feature vector extracted from multiple evenly spaced frames near the boundary is suggested to describe the waveform evolution across a boundary. CART is used to cluster acoustically similar GMM, so that the GMM for each leaf node is reliably trained by the limited manually labeled boundaries. An accuracy of 90% is thus achieved when only 250 manually labeled sentences are provided to train the refining models.
Sequenced event set pattern matching

Event pattern matching is a query technique where a sequence of input events is matched against a complex pattern that specifies constraints on extent, order, values, and quantification of matching events. The increasing importance of such query techniques is underpinned by a significant amount of research work, the availability of commercial products, and by a recent proposal to extend SQL for event pattern matching. The proposed SQL extension includes an operator PERMUTE, which allows to express patterns that match any permutation of a set of events. No implementation of this operator is known to the authors. In this paper, we study the sequenced event set pattern matching problem, which is the problem of matching a sequence of input events against a complex pattern that specifies a sequence of sets of events rather than a sequence of single events. Similar to the PERMUTE operator, events that match with a set specified in the pattern can occur in any permutation, whereas events that match with different sets have to be strictly consecutive, following the order of the sets in the pattern specification. We formally define the problem of sequenced event set pattern matching, propose an automaton-based evaluation algorithm, and provide a detailed analysis of its runtime complexity. An empirical evaluation with real-world data shows that our algorithm outperforms a brute force approach that uses existing techniques to solve the sequenced event set pattern matching problem, and it validates the results from our complexity analysis.
OCR for Printed Telugu Documents

Optical character recognition(OCR) is a well known process for converting text images to machine editable text format. Applications of OCR include preserving old/historical documents in electronic format, library cataloging, automatic reading for sorting of postal mail, bank cheques and forms as base for many applications in Natural Language Processing(NLP). OCR is a difficult problem to solve on real world data. In specific for Telugu language it is very difficult problem as single character formed by a single vowel or consonant or it can be compound character consists of combination of vowel and consonants. Drishti is the only one significant OCR available for Telugu language which will work on high resolution, good quality, noise free documents and specific input formats which is making use of OCR unrealistic. Our aim is to create a OCR which can eliminate all the constraints on input formats, quality which will work on real world documents. We have created a basic end to end functioning OCR. We have used basic 0/1 features we obtained accuracy of 30% in stage1. We have observed the problems with OCR are font dependence, joint characters and broken characters. We have experimented with different types of features like gabor, wavelets, skeleton and circular features in stage2 for solving font dependence problem. We got improvement in accuracy with wavelet features, we got 48% accuracy. We have given our solution for joint character problem. We have observed distorted characters formed by binarization methods, we have studied some methods for solving binarization problem.
Predicting breast cancer survivability using data mining techniques

In this paper, appropriate and efficient networks for breast cancer knowledge discovery from clinically collected data sets are investigated. Invoking various data mining techniques, it is desired to find out the percentage of disease development, using the developed network. The results, help in choosing a reasonable treatment of the patient. Several neural network structures are evaluated for this investigation. The performance of the statistical neural network structures, self organizing map(SOM), radial basis function network (RBF), general regression neural network (GRNN) and probabilistic neural network (PNN) are tested both on the Wisconsin breast cancer data (WBCD) and on the Shiraz Namazi Hospital breast cancer data (NHBCD). To overcome the problem of high dimension of the data set and realizing the correlated nature of the data, principal component techniques are used to reduce the dimension of data and find appropriate networks. The results are quite satisfactory while presenting a comparison of effectiveness each proposed network for such problems.
Using Doctrines for Human-Robot Collaboration to Guide Ethical Behavior

In this paper, we consider the issue of guiding ethical behavior in human-robot teams from a systemic viewpoint. Considering a team as a sociotechnical complex, we look at how responsibility for actions can arise through the interaction between the different actors in the team while playing specific roles. We define the notions of role, discuss how they establish a social network, and then use logical notions of multi-agent trust to formalize responsibility as accountability against capabilities that are invoked during collaboration.
An Introduction to the Basic Principles of Marxism Class Implementation of the Case Teaching

Case teaching is an ideal consistent with the law students of cognitive and psychological needs of new teaching attempts.The flexible use of this teaching practice,opens up new opportunities for "Two Courses" teaching reform and deepens the "Two Courses" teaching method reform as a new breakthrough.To implement case teaching for An Introduction to the Basic Principles of Marxism should strive to enhance teachers sense of responsibility and sensitivity,we should pay attention to the ideological and political education,focusing on the aims of education and teaching.The students should strive to capture the theoretical thinking of the doubt hot and difficult for case teaching.
Concepts are Not “ Webs of Sensation ” : Evidence from motion words

What are concepts made of? One prominent theory assumes that concepts are comprised of sensory-motor features distributed throughout the sensory-motor cortices. For example, the meaning of the word “kick” is partially represented in the visual motion regions that are activated during the observation of kicking. This theory makes specific predictions about concepts that have motion properties: they are represented in motion perception regions. To test this prediction we used functional magnetic resonance imaging (fMRI) to localize brain regions involved in perception of motion and body movement. We investigated the response of these regions during word comprehension. Participants made relatedness judgments on word-pairs from several categories that varied in the degree to which they brought to mind visual motion (verbs of action, thought, change-of-state, and bodily-function; and nouns referring to animals, tools and inanimate natural kinds). The results show that none of the visual motion areas respond more to high-motion than low-motion words. We also tested the claim that concepts are not represented directly in sensory-motor cortices but in nearby regions. According to this version of this hypothesis, these brain regions store visual motion features that are retrieved during word comprehension. This theory predicts that posteriorlateral temporal cortices (PLTC) respond to action concepts because they represent motion associations. We replicated previous findings showing greater activity in the PLTC for action than animal concepts. These PLTC regions were observed just anterior to biological motion perception regions. Critically, regions that responded to action concepts did not respond more to high-motion than low-motion concepts. Rather, these regions responded more to all verbs than all nouns. We conclude that concepts are abstracted away from their associated sensory experiences.
Integrating anchored instructional strategy and modularity concept into Interactive multimedia PowerPoint presentation

This study is based on the cognitive theory of multimedia learning to propose an alternative presentation integrating anchored strategy and modularity concept into a Microsoft PowerPoint presentation for classroom learning. With the anchored instructional strategy and rich visual interactivity, the presentation helps students to acquire more information and remember more ideas. In addition, incorporated hyperlinks and modular design strategies, teachers can easily select the particular slide they need to help student with their questions, thereby reducing their information load. This method can support students’ cognitive process with the comprehension of the content being taught so as to enhance their learning attitude and performance. In this study, an exploratory test was conducted with 83 students in a university of technology. The results indicated that the students in the experimental group had a better learning effectiveness for lectures with interactive PowerPoint presentation than that of the control group.
Speeding Up Neural Networks for Large Scale Classification using WTA Hashing

In this paper we propose to use the Winner Takes All hashing technique to speed up forward propagation and backward propagation in fully connected layers in convolutional neural networks. The proposed technique reduces significantly the computational complexity, which in turn, allows us to train layers with a large number of kernels with out the associated time penalty. As a consequence we are able to train convolutional neural network on a very large number of output classes with only a small increase in the computational cost. To show the effectiveness of the technique we train a new output layer on a pretrained network using both the regular multiplicative approach and our proposed hashing methodology. Our results showed no drop in performance and demonstrate, with our implementation, a 7 fold speed up during the training.
Identifying risks for male street gang affiliation: a systematic review and narrative synthesis

Abstract Gang violence has increased in recent years. Individuals appear to be joining gangs at younger ages, and many have suffered historic maltreatment. Subsequent exposure to violence can result in profound consequences, including acute psychological harm. This review aims to identify predictive risk factors for male street gang affiliation. A systematic literature search was conducted utilising PsycINFO, PsycARTICLES, Medline, the Cochrane Central Register of Controlled Trials, the Cochrane Database of Systematic Reviews and the Social Policy and Practice databases (from the databases’ inception to 3 April 2015). From this search, n = 244 peer-reviewed papers were included in an initial scoping review, and n = 102 thereafter met criteria for a systematic review; a narrative synthesis follows. Gang members have typically faced numerous historic adversities across multiple domains; individual, family, peers, school and community. Cumulative factors generated an independent risk. The meta-narrative described an overarching failure to safeguard vulnerable individuals, with the motivation for gang affiliation hypothetically arising from an attempt to have their basic needs met. Clinical and research recommendations were made to inform early intervention policy and practice.
Distributed State Estimation in Power System Using Belief Propagation: Algorithms and Performance

We present a detailed study of the applications of factor graphs and the belief propagation (BP) algorithm to the state estimation (SE) problem. Our methodology starts with the BP solution for the linearized DC model, and use insights obtained therein to derive the BP algorithm for the non-linear AC model. Then, we make a key further step, where we present the solution in which the BP is applied sequentially over the AC model, akin to what is done by the Gauss-Newton method. The resulting BP-based Gauss-Newton algorithm has the interpretation of a fully distributed Gauss-Newton method with the same accuracy as the centralized SE, preserving a number of advantages brought in by the BP framework. The BP-based algorithms presented in this paper are fully distributed, however, they can be easily extended to the case of multi-area SE. Finally, the paper provides extensive numerical study of the proposed algorithms and gives a number of useful insights for their implementation.
Nonlinear filtering update phase via the single point truncated unscented Kalman filter

A fast algorithm to approximate the first two moments of the posterior probability density function (pdf) in nonlinear non-Gaussian Bayesian filtering is proposed. If the pdf of the measurement noise has a bounded support and the measurement function is continuous and bljective, we can use a modified prior pdf that meets Bayes' rule exactly. The central idea of this paper is that a Kalman filter applied to a modified prior distribution can improve the estimate given by the conventional Kalman filter. In practice, bounded support is not required and the modification of the prior is accounted for by adding an extra-point to the set of sigma-points used by the unscented Kalman filter.