Miracle-dz/UniFAR-FTUs huggingface dataset:
To Develop Racing Application for Real Time Results Calculation using GPS and GPRS/GSM Device

This paper presents that, integration of GPS and GPRS/GSM device for any kind of vehicle gives an suitable output for real-time racing of vehicles (Bikes, Jet Skies). To develop real time racing application, use of the two technologies is made, i.e. Global System for Mobiles/General Packet Radio Service (GSM/GPRS) which provide 2G and 3G technologies and satellite-based navigation system consisting of several satellites revolving around the earth i.e. Global Positioning System. The GPS and GSM/GPRS device will provide the solution for tracing of path and for real time racing of vehicles (Bikes, Jet skies). A real-time vehicle tracking system proposes the use of a GPS technology module to receive the location of the vehicle, to forward data into microcontroller and connect to internet by a GPRS technology for displaying a real time results on the website map developed by Google map. It allows inspection of vehicles with the Google map on a real-time website. Also vehicles can be monitored and located very effectively and results can be declared faster. GPS has various applications such as in military projects, also used as a tool in commerce, scientific uses, tracking and surveillance etc.
University of Repository

Representation of temporal and spatial information for the Semantic Web often involves qualitative deﬁned information (i.e., information described using natural language terms such as “before” or “overlaps”) since precise dates or coordinates are not always available. This work proposes several temporal representations for time points and intervals and spatial topological representations in ontologies by means of OWL properties and reasoning rules in SWRL. All representations are fully compliant with existing Semantic Web standards and W3C recommendations. Although qualitative representations for temporal interval and point relations and spatial topological relations exist, this is the ﬁrst work proposing representations combining qualitative and quantitative information for the Semantic Web. In addition to this, several existing and proposed approaches are compared using diﬀerent reasoners and experimental results are presented in detail. The proposed approach is applied to topological relations (RCC5 and RCC8) supporting both qualitative and quantitative (i.e., using coordinates) spatial relations. Experimental results illustrate that reasoning performance diﬀers greatly between diﬀerent representations and reasoners. To the best of our knowledge, this is the ﬁrst such experimental evaluation of both qualitative and quantitative Semantic Web temporal and spatial representations. In addition to the above, querying performance using SPARQL is evaluated. Evaluation results demonstrate that extracting qualitative relations from quantitative representations using reasoning rules and querying qualitative relations instead of directly querying quantitative representations increases performance at query time.
Wikipedia vandalism detection

Wikipedia is an online encyclopedia that anyone can access and edit. It has become one of the most important sources of knowledge online and many third party projects rely on it for a wide-range of purposes. The open model of Wikipedia allows pranksters, lobbyists and spammers to attack the integrity of the encyclopedia and this endangers it as a public resource. This is known in the community as vandalism. A plethora of methods have been developed within the Wikipedia and the scientific community to tackle this problem. We have participated in this effort and developed one of the leading approaches. Our research aims to create a fully-working antivandalism system and get it working in the real world.
AN IMPROVED FULL BRIDGE DC-DC CONVERTER FOR ELECTRIC VEHICLES

The battery charger of electric vehicles plays a very important role in their development. This project focuses on the DC-DC converter for the electric vehicle battery charger which is the second stage of a two stage on‐board charger. The first stage is a power factor correction rectifier used to transform the 50Hz electrical quantities into DC quantities with a good input power factor. The second stage is a zero voltage switching full bridge dc-dc converter which adjusts the levels to the values required by the battery and moreover provides a galvanic isolation. The main objective of this project is to process and deliver power efficiently, to minimize the charger size, to reduce the switching losses, to guarantee fast operation and to reduce the cost of electricity drawn from utility. Keywords-DC-DC converter, full bridge, electric vehicle, zero voltage switching (ZVS), battery charger
Direct Optimization of Ranking Measures for Learning to Rank Models

Guo, Li Li. M.S., Department of Computer Science and Engineering, Wright Sta e University, 2012.Direct Optimization of Ranking Measures for Learning to Rank Models . The main challenge in learning-to-rank for information ret rieval is the difficulty to directly optimize ranking measures to automatically constru ct a ranking model from training data. It is mainly due to the fact that the ranking measures ar determined by the order of ranked documents rather than the specific values of rankin g model scores, thus they are non-convex, nondifferentiable and discontinuous. To addr ess this issue, listwise approaches have been proposed where loss functions are defined either by xploiting a probabilistic model or by optimizing upper bounds or smoothed approximati ons of ranking measures. Even though very promising results have been achieved, ther e is still a mismatch between target cost and optimization cost. In this work, we present a novel learning algorithm that directly optimizes the ranking measures without resorting o any upper bounds or approximations. Our approach is essentially an iterative greedy c oordinate descent method in optimization. For each iteration, we only update one parame ter along one coordinate with all others fixed. Since the ranking measure is a stepwise func tion of a single parameter, we exploit an exhaustive line search algorithm to locate the in t rval with the smallest ranking measure along each coordinate. We pick the coordinate that l eads to the largest reduction of ranking measure. In order to determine the optimal value of t he parameter for the selected coordinate, we construct a probabilistic framework for the permutation, and maximize the likelihood of top-m ranked documents. This iterative procedure is continued un til convergence. We conduct experiments of five datasets selected from Microsoft LETOR datasets, our experimental results show that the proposed direct rank algorithm outperforms several well-known state-of-the-art ranking algorithms.
Imitating Targets from all sides: An Unsupervised Transfer Learning method for Person Re-identification

Person re-identification (Re-ID) models usually show a limited performance when they are trained on one dataset and tested on another dataset due to the inter-dataset bias (e.g. completely different identities and backgrounds) and the intra-dataset difference (e.g. camera invariance). In terms of this issue, given a labelled source training set and an unlabelled target training set, we propose an unsupervised transfer learning method characterized by 1) bridging inter-dataset bias and intra-dataset difference via a proposed ImitateModel simultaneously; 2) regarding the unsupervised person Re-ID problem as a semi-supervised learning problem formulated by a dual classification loss to learn a discriminative representation across domains; 3) exploiting the underlying commonality across different domains from the class-style space to improve the generalization ability of re-ID models. Extensive experiments are conducted on two widely employed benchmarks, including Market-1501 and DukeMTMC-reID, and experimental results demonstrate that the proposed method can achieve a competitive performance against other state-of-the-art unsupervised Re-ID approaches.
Improvement of Low Voltage Ride through Capability of DFIG based Wind Energy System

Among the different renewable, wind energy system is the most exploited due to its free availability and technological developments. With the increasing penetration of wind energy into power system, the grid codes are become effective which mean wind energy system must insure the uninterrupted operation with voltage control and reactive power compensation improving low voltage ride through capability(LVRT). The doubly fed induction generator (DFIG) based wind energy system is very sensitive to voltage variation and less ability to resist drop in grid voltage. To enhance the LVRT capability of DFIG, one of the series connected Flexible A.C. Transmission Systems (FACTS) device Static Synchronous Compensator (STATCOM) is proposed here. The STATCOM connected between the wind energy system and point of common coupling compensate accurately. When there is voltage difference between normal voltage and fault condition, difference will be generated by inverter modulation thus improving its LVRT. The simulation for 2MW wind energy system is carried out on Matlab/Simulink environment and results are verified.
NeuroImage: Clinical

Neuromelanin sensitive magnetic resonance imaging (NMS-MRI) has been crucial in identifying abnormalities in the substantia nigra pars compacta (SNc) in Parkinson's disease (PD) as PD is characterized by loss of dopaminergic neurons in the SNc. Current techniques employ estimation of contrast ratios of the SNc, visualized on NMS-MRI, to discern PD patients from the healthy controls. However, the extraction of these features is time-consuming and laborious and moreover provides lower prediction accuracies. Furthermore, these do not account for patterns of subtle changes in PD in the SNc. To mitigate this, our work establishes a computer-based analysis technique that uses convolutional neural networks (CNNs) to create prognostic and diagnostic biomarkers of PD from NMS-MRI. Our technique not only performs with a superior testing accuracy (80%) as compared to contrast ratio-based classification (56.5% testing accuracy) and radiomics classifier (60.3% testing accuracy), but also supports discriminating PD from atypical parkinsonian syndromes (85.7% test accuracy). Moreover, it has the capability to locate the most discriminative regions on the neuromelanin contrast images. These discriminative activations demonstrate that the left SNc plays a key role in the classification in comparison to the right SNc, and are in agreement with the concept of asymmetry in PD. Overall, the proposed technique has the potential to support radiological diagnosis of PD while facilitating deeper understanding into the abnormalities in SNc.
Interactive Text Generation ∗

Users interact with text, image, code, or other editors on a daily basis. However, machine learning models are rarely trained in the settings that reﬂect the interactivity between users and their editor. This is understandable as training AI models with real users is not only slow and costly, but what these models learn may be speciﬁc to user interface design choices. Unfortunately, this means most of the research on text, code, and image generation has focused on non-interactive settings, whereby the model is expected to get every-thing right without accounting for any input from a user who may be willing to help. We introduce a new Interactive Text Generation task that allows training generation models interactively without the costs of involving real users, by using user simulators that provide edits that guide the model towards a given target text. We train our interactive models us-ing Imitation Learning, and our experiments against competitive non-interactive generation models show that models trained interactively are superior to their non-interactive counter-parts, even when all models are given the same budget of user inputs or edits.
The ART of Adaptive Pattern Recognition by a Self-Organizing Neural Network

The adaptive resonance theory (ART) suggests a solution to the stability-plasticity dilemma facing designers of learning systems, namely how to design a learning system that will remain plastic, or adaptive, in response to significant events and yet remain stable in response to irrelevant events. ART architectures are discussed that are neural networks that self-organize stable recognition codes in real time in response to arbitrary sequences of input patterns. Within such an ART architecture, the process of adaptive pattern recognition is a special case of the more general cognitive process of hypothesis discovery, testing, search, classification, and learning. This property opens up the possibility of applying ART systems to more general problems of adaptively processing large abstract information sources and databases. The main computational properties of these ART architectures are outlined and contrasted with those of alternative learning and recognition systems. >
INDEPENDENT COMPONENT ANALYSIS IN MULTIMEDIA MODELING

Modeling of multimedia and multimodal data becomes increasingly important with the digitalization of the world. The objective of this paper is to demonstrate the potential of independent component analysis and blind sources separation methods for modeling and understanding of multimedia data, which largely refers to text, images/video, audio and combinations of such data. We review a number of applications within single and combined media with the hope that this might provide inspiration for further research in this area. Finally, we provide a detailed presentation of our own recent work on modeling combined text/image data for the purpose of cross-media retrieval.
Cerebral vasospasm following subarachnoid hemorrhage: time for a new world of thought

Abstract Objective: Delayed cerebral vasospasm has long been recognized as an important cause of poor outcome after an otherwise successful treatment of a ruptured intracranial aneurysm, but it remains a pathophysiological enigma despite intensive research for more than half a century. Method: Summarized in this review are highlights of research from North America, Europe and Asia reflecting recent advances in the understanding of delayed ischemic deficit. Result: It will focus on current accepted mechanisms and on new frontiers in vasospasm research. Conclusion: A key issue is the recognition of events other than arterial narrowing such as early brain injury and cortical spreading depression and of their contribution to overall mortality and morbidity.
Distinctive and compact featuresAyelet

We consider the problem of extracting features for multi-class recognition problems. The features are required to make fine distinctions between similar classes, combined with tolerance for distortions and missing information. We define and compare two general approaches, both based on maximizing the delivered information for recognition: one divides the problem into multiple binary classification tasks, while the other uses a single multi-class scheme. The two strategies result in markedly different sets of features, which we apply to face identification and detection. We show that the first produces a sparse set of distinctive features that are specific to an individual face, and are highly tolerant to distortions and missing input. The second produces compact features, each shared by about half of the faces, which perform better in general face detection. The results show the advantage of distinctive features for making fine distinctions in a robust manner. They also show that different features are optimal for recognition tasks at different levels of specificity. 2008 Published by Elsevier B.V. E 32 T 60 61 62 63 64 65 66 67 68
Artificial Intelligence and Psychiatry

This paper provides a brief historical introduction to the new field of artificial intelligence and describes some applications to psychiatry. It focuses on two successful programs: a model of paranoid processes and an expert system for the pharmacological management of depressive disorders. Finally, it reviews evidence in favor of computerized psychotherapy and offers speculations on the future development of research in this area.
Integrated Innovation Management for Sustainable Development When knowledge meets creativity

Innovation emerges when we cross boundaries between academic disciplines, issues, organisational units, genders, generations, and different cultures successfully (for an excellent discussion of innovation drivers, see Taleb 2007). The idea is simple – bring people who work in different contexts together and let them exchange ideas: new perspectives emerge and help to create new solutions to old problems. Innovations, thus, seek to create an environment that stimulates changes in perspective. We cannot plan of manage innovation’, but we can manage the organisational context in which innovations may emerge (von Krogh et al. 2000). Fresh ideas do not bring about change unless they are transformed into innovations. Once new ideas, products, or approaches have emerged it is crucial to transfer them into one’s specific context. In this article we share two fundamental ingredients to spark innovation: (i) the creation of a formal network or community and (ii) enabling a culture of creativity.
O ct 2 01 2 YITP-1264 M 5-branes in ABJM theory and Nahm equation

We explicitly construct two classes of the BPS solutions in the ABJM action: the funnel type solutions and the ’t Hooft-Polyakov type solutions, and study their physical properties as the M2-M5 bound state. Furthermore, we give a one to one correspondence between the solutions of the BPS equation and the ones of an extended Nahm equation which includes the Nahm equation. This enables us to construct infinitely many conserved quantities from the Lax form of the Nahm equation. nosaka(at)yukawa.kyoto-u.ac. jp terasima(at)yukawa.kyoto-u.ac. jp
Ligand-specific scoring functions : improved ranking of docking solutions 1

Ligand-specific scoring functions: improved ranking of docking solutions T.V. Pyrkov a b , J.P. Priestle c , E. Jacoby c & R.G. Efremov a a M.M. Shemyakin & Yu.A. Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences , Moscow, Russia b Moscow Institute of Physics and Technology (State University) , Dolgoprudny, Moscow region, Russia c Novartis Institutes for Biomedical Research , Postfach, Basel, Switzerland Published online: 04 Dec 2010.
A compact UWB bandpass filter using a center‐tapped composite right/left‐handed transmission‐line zeroth‐order resonator

The design of a new UWB bandpass filter is proposed, which has a center tapped microstrip composite right/left‐handed transmission‐line zeroth‐order resonator (CRLH‐TL ZOR). In an attempt to reduce the size, instead of the conventional periodic CRLH‐TL, only one cell ZOR geometry is adopted and optimized to have the target UWB bandpass filtering. Along with this subwavelength filter, the matching parts working as a stepped impedance low‐pass filter are suggested for good matching and stopband enhancement. The proposed filter is shown to have the overall size of “0. 69λg × 0. 73λg,” the fractional bandwidth over 100%, the insertion loss much less than 1 dB, and the flat group delay with an acceptable return loss performance in the predicted and measured results. © 2011 Wiley Periodicals, Inc. Microwave Opt Technol Lett, 2011; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.26170
TDPrefixGrowth: an algorithm for mining multiple sequential periodic regularities

The need for the study of dynamic and evolutionary settings made time a major dimension when it comes to data analytics. From business to health applications, being able to understand temporal trends and their evolution throughout time, is key for adapting and optimizing processes according to future requirements. In this context, different research streams, related with Temporal Pattern Mining, have been playing an important role. Examples are the fields of Sequential Pattern Mining, Change Mining or Emerging Patterns. Still, existing solutions are limited with regards to the discovery of expressive temporal relations able to represent temporal evolutionary behaviors. Additionally, the use of background knowledge to guide the discovery of these particular patterns has also been poorly addressed. In this work, we propose a new constraint-based mining approach, named TDPrefixGrowth, in order to discover different temporal regularities over sequential data. We also propose three main types of regularities: Converging, Diverging and Cyclic regularities, which model different evolutionary behaviors, depending on the temporal gaps of the multiple occurrences of the events. The experimental results, from different domain datasets, show the efficiency and flexibility of our algorithm when compared to other similar approaches, as well as the quality of the discovered patterns regarding temporal dynamics. Palavras Chave
A Study of Development of Transmission Systems for Next-generation Terrestrial 4K UHD & HD Convergence Broadcasting

The worldwide transition from analog to digital broadcasting has now been completed and the need to study next generation standards for Ultra High Definition TV (UHDTV) broadcasting, and broadcasting & communication convergence systems is rapidly growing. In particular, high resolution mobile broadcasting services are needed to satisfy recent consumers. Therefore, the development of highly efficient convergence broadcasting systems that provide fixed/mobile broadcasting through a single channel is needed. In this paper, a service scenario and requirements for providing 4K UHD & HD convergence broadcasting services through a terrestrial single channel are analyzed by employing the latest transmission and A/V codec technologies. Optimized transmission parameters for 6 MHz & 8 MHz terrestrial bandwidths are drawn, and receiving performances are measured under Additive White Gaussian Noise (AWGN) and time-varying multipath channels. From the results, in a 6 MHz bandwidth, the reliable receiving of HD layer data can be achieved when the receiver velocity is maximum 140 Km/h and is not achieved when the velocity is over 140 Km/h due to the limit of bandwidth. When the bandwidth is extended to 8 MHz, the reliable receiving of both 4K UHD and HD layer data is achieved under a very fast fading multipath channel.