Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of the traffic behavior of emergency vehicles in a microscopic traffic simulation

This paper presents how emergency vehicles can be modeled and simulated in the microscopic traffic simulation SUMO (Simulation of Urban MObility). The special rights of emergency vehicles are implemented in the SUMO framework and can be switched off and on in the simulation with a blue light device. The surrounding traffic reacts accordingly to the emergency vehicle and form an emergency lane. In addition real world data from emergency vehicles are used to evaluate the driving behavior of emergency vehicles and compare it to real world and simulated vehicle characteristics. The evaluation results show that the simulated vehicles pass an intersection generally faster than in real world. For emergency vehicle a time saving of in average one second at a single intersection could be measured for right turning vehicles.
Effectiveness Comparison of Feature Words for Word Sense Disambiguation

The context of ambiguous words is the most important clue for word sense disambiguation (WSD). The selection and weight assignment of feature words affect the performance of WSD directly. The existing method usually selects the words in a certain length of window as feature words, which is easy to induce noise words in a short distance and neglect feature words in a long distance. Syntax parsing can analyze syntactic relations among words and output phrase structure tree or dependency tree of the sentence, which could be utilized to select feature words. The paper proposes the methods to select feature words based on phrase structure tree and dependency tree, and compares their effectiveness in detail. The results of experiments show that the method to select feature words and assign their weights with dependency tree is a preferred strategy for WSD.
Hachage de photographie d’identité : une approche globale

This thesis addresses the question of the authenticity of identity photographs, part of the documents required in controlled access. Since sophisticated means of reproduction are publicly available, new methods / techniques should prevent tampering and unauthorized reproduction of the photograph. This thesis proposes a hashing method for the authentication of the identity photographs, robust to print-and-scan. This study focuses also on the effects of digitization at hash level. The developed algorithm performs a dimension reduction, based on independent component analysis (ICA). In the learning stage, the subspace projection is obtained by applying ICA and then reduced according to an original entropic selection strategy. In the extraction stage, the coefficients obtained after projecting the identity image on the subspace are quantified and binarized to obtain the hash value. The study reveals the effects of the scanning noise on the hash values of the identity photographs and shows that the proposed method is robust to the print-and-scan attack. The approach focusing on robust hashing of a restricted class of images (identity) differs from classical approaches that address any image
Super-polynomial and exponential improvements for quantum-enhanced reinforcement learning

Recent work on quantum machine learning has demonstrated that quantum computers can offer dramatic improvements over classical devices for data mining, prediction and classification. However, less is known about the advantages using quantum computers may bring in the more general setting of reinforcement learning, where learning is achieved via interaction with a task environment that provides occasional rewards. Reinforcement learning can incorporate data-analysis-oriented learning settings as special cases, but also includes more complex situations where, e.g., reinforcing feedback is delayed. In a few recent works, Grover-type amplification has been utilized to construct quantum agents that achieve up-to-quadratic improvements in learning efficiency. These encouraging results have left open the key question of whether super-polynomial improvements in learning times are possible for genuine reinforcement learning problems, that is problems that go beyond the other more restricted learning paradigms. In this work, we provide a family of such genuine reinforcement learning tasks. We construct quantum-enhanced learners which learn super-polynomially, and even exponentially faster than any classical reinforcement learning model, and we discuss the potential impact our results may have on future technologies.
Sparse Clustered Neural Networks for the Assignment Problem

The linear assignment problem is a fundamental com-binatorial problem and a classical linear programming problem. It consists of assigning agents to tasks on a one-to-one basis, while minimizing thetotal assignment cost. The assignmentproblem appears recurrently in major applications involvingoptimal decision-making. However, the use of classical solvingmethods for large size problems is increasingly prohibitive, as itrequires high computation and processing cost. In this paper, abiologically inspired algorithm using an artificial neural network(ANN) is proposed. The artificial neural network model involvedin this contribution is a sparse clustered neural network (SCN), which is a generalization of the Palm-Wilshaw neural network. The presented algorithm provides a lower complexity compared to the classically used Hungarian algorithm and allows parallelcomputation at the cost of a fair approximation of the optimalassignment. Illustrative applications through practical examplesare given for analysis and evaluation purpose.
Classification Rules Using Decision Tree for Dengue Disease

Spatial data mining becomes more interesting and important as more spatial data have been accumulated in spatial databases. Spatial patterns are of great importance in many GIS applications that yield equal to association rules of a business i.e. On Line Transaction Processing (OLTP). Mining the spatial co-location patterns is an important spatial data mining task with broad applications. Certain operations that incorporate methods of analyses and summarization are very much crucial for organizations having large data sets of spatial data. Decision tree is one of the learning algorithms which possess certain advantages which make it suitable for discovering rules for data mining application. The decision tree has been applied to classify the population in an area based on the chances of hitting a disease. This paper intended to discover the rules for the disease hit using decision tree algorithm. The paper also explores what rule can act in this area for the future prediction. The objective is to creating a prediction model, using decision tree for predicting the chances of occurrences of dengue diseases in a tribal area.
Measurement Reliability, Validity, and Quality of Slider Versus Radio Button Scales in an Online Probability-Based Panel in Norway

Little is known about the reliability and validity in web surveys, although this is crucial information to evaluate how accurate the results might be and/or to correct for measurement errors. In particular, there are few studies based on probability-based samples for web surveys, looking at web-specific response scales and considering the impact of having smartphone respondents. In this article, we start filling these gaps by estimating the measurement quality of sliders compared to radio button scales controlling for the device respondents used. We conducted therefore two multitrait–multimethod (MTMM) experiments in the Norwegian Citizen Panel (NCP), a probability-based online panel. Overall, we find that if smartphone respondents represent a nonnegligible part of the whole sample, offering the response options in form of a slider or a radio button scale leads to a quite similar measurement quality. This means that sliders could be used more often without harming the data quality. Besides, if there are no smartphone respondents, we find that sliders can also be used, but that the marker should be placed initially in the middle rather than on the left side. However, in practice, there is no need to shift from radio buttons to sliders since the quality is not highly improved by providing sliders.
Atomic distributions in crystal structures solved by Boolean satisfiability techniques

Abstract The atomic distribution in crystal structures becomes very complex if atoms are disordered and randomly distributed over positions not being fully occupied. Interatomic distances between neighboring atoms might be too close for simultaneous occupancies and thus are mutually exclusive. The distribution of atoms over crystallographic positions avoiding close contacts with neighboring atoms represents an NP-complete problem that is believed to have no efficient solution. Here, we use Boolean satisfiability (SAT) techniques to find a valid atomic distribution pattern in the crystal structure. Distance constraints are encoded as conjunctions of logical disjunctions over Boolean variables and handed to a SAT solver. If a solution exists, the solver supplies a satisfying assignment to the Boolean variables yielding a valid distribution after decoding. That way the hitherto unsolved problem of distributing k atoms over n positions has an elegant solution related to one of the most central problems in computer science.
Two layers LSTM with attention for multi-choice question answering in exams

Question Answering in Exams is typical question answering task that aims to test how accurately the model could answer the questions in exams. In this paper, we use general deep learning model to solve the multi-choice question answering task. Our approach is to build distributed word embedding of question and answers instead of manually extracting features or linguistic tools, meanwhile, for improving the accuracy, the external corpus is introduced. The framework uses a two layers LSTM with attention which get a significant result. By contrast, we introduce the simple long short-term memory (QA-LSTM) model and QA-LSTM-CNN model and QA-LSTM with attention model as the reference. Experiment demonstrate superior performance of two layers LSTM with attention compared to other models in question answering task.
An Efficient Improved Model for Tor Anonymous Network

In order to improve the transmission efficiency, security and anonymity of Tor anonymous communication system, this paper analyzes the efficiency of Tor anonymous system through research and analysis of the security and vulnerability of transmission mechanism, encryption rules and node distribution of Tor anonymous network system, and then proposes an efficient improved model EFTAN for Tor anonymous network, and finally obtains a network system that can improve the efficiency, security and anonymity of the anonymous communication.
Development of a Software-Defined Radar

Abstract : In wartime environments, radio frequency interference (RFI) is created by communication devices, jammers, and other electronic sources. The RFI spectrum is assumed to be nonstationary over time, which creates potential to disrupt desired radar operation. The cognitive radar system discussed herein mitigates the effects of RFI by sensing and adapting the transmitted waveform to the widest available (unoccupied) instantaneous bandwidth in real time. Consequently, the radar range resolution and target detection are ameliorated. Results of a prototype cognitive radar implemented on an Ettus X310 software-defined radio are presented.
On the optimum spectrum partitioning in a microcell/macrocell cellular layout with overflow

An analytical method to find the optimal spectrum partitioning for a system of microcells and their overlaying macrocells is presented. The spectrum partitioning is calculated based on the blocking probability in each layer ensuring a grade of service of 2% and allowing overflow traffic from microcells to macrocells. The teletraffic analysis is based on a multidimensional birth and death process. The calculation assumes perfect assignment of mobiles to each of the layers according to their speeds (i.e. the probability of erroneous assignment of a call is insignificant). The model is general and any velocity probability density function in the system could be used to assess the blocking probability in each layer. The spectrum partitioning method could be dynamically implemented.
Analytical assessment rubrics to facilitate semi-automated Essay grading and feedback provision

Assessment is an essential part of the learning process, both in formative learning settings and traditional summative assessment. Both types are challenging, as it can be difficult to ensure consistency, reliability and absence of bias. In formative assessment the problem of workload and timely results is even greater, as the task is carried out more frequently. Information technology is able to assist teachers in these challenges to various degrees depending on the type of test items. The essay test item, besides the well-known application to train language skills and to acquire foreign languages, is widely used to test higher order thinking skills and therefore it can be applied in a great variety of subject domains at different educational levels. Evaluating essays is a time-consuming task hence supporting technologies can deliver great advantages. In this paper we introduce a semi-automated approach to essay grading based on analytical assessment rubrics, the use of which facilitate feedback provision. A prototype system is described in terms of requirements derived from the authors' own experience and the published literature, a workflow model. Reflection of the development experience and user feedback informs further development of the system.
Analysis of feature selection on the performance of multimodal keystroke dynamics biometric systems

New security systems, methods or techniques need to have their performance evaluated in conditions that closely resemble a real-life situation. Moreover, biometric systems need a realistic set of biometrics data to test their accuracy when classifying individuals between legitimate users or impostors. The use of similar modalities may influence the use of the same features, however, there is no indication that basic biometrics will perform well using the same set of features. This report aims to be the first to investigate the impact of feature selection in two similar yet different biometric modalities: keyboard keystroke dynamics and touchscreen keystroke dynamics. We have found that an efficient feature selection method, chosen to suit the needs of the classification algorithm employed by the system, can multiply accuracy rates while diminishing the number of features to be processed to a small subset - which also improves the system’s processing time and overall usability.
A general platform and markup language for text to speech synthesis

A developer is faced with two challenges when writing applications which work with every commercially available text to speech synthesizer. The first challenge is controlling "presentation of speech" or the manner in which text is spoken. This includes how particular words, phrases, or constructs are spoken. A collection of "experts" identify and process these particular constructs so they are spoken in a natural manner. A markup language allows applications to control this process. The second challenge exists because each specific synthesizer presents its own API for which the developer cannot "special-case" code. A "Speech Manager" provides a single API to applications and access to many synthesizers. Thesis Supervisor: Christopher Schmandt Title: Principal Research Scientist, M.I. T. Media Laboratory
Fault detection in a class of nonlinear systems via adaptive sliding observer

The paper presents the design of an adaptive sliding mode observer under some matching conditions. First the design of the observer for a class of nonlinear systems with slowly varying parameters is addressed. Next, the application of the observer for fault detection purposes is considered. In this context, the residual generation and evaluation problems are addressed, and in addition an adaptive scheme for threshold setting is described. Finally, the proposed fault detection approach is illustrated through simulation results.
TDNN labeling for a HMM recognizer

A system which combines the good short-time classification properties of the time delay neural network (TDNN) with the good integration and overall recognition capabilities of hidden Markov models (HMMs) is proposed for a speaker-independent speech recognizer. The standard vector quantization is replaced by a TDNN labeler giving phonelike labels. In order to avoid hand segmentation for the training of the TDNN, a separate HMM and a Viterbi alignment derived from it are used. This gives a coarse phonetic segmentation of the training data. <<ETX>>
PERFORMANCE COMPARISON OF ROW PER SLAVE AND ROWS SET PER SLAVE METHOD IN PVM BASED PARALLEL MATRIX MULTIPLICATION

Parallel computing operates on the principle that l arge problems can often be divided into smaller one s, which are then solved concurrently to save time by taking advantage of no n-local resources and overcoming memory constraints . Multiplication of larger matrices requires a lot of computation time. This p aper deals with the two methods for handling Parall el Matrix Multiplication. First is, dividing the rows of one of the input matrices into set of rows based on the number of slaves and assigning one rows set for each slave for computation. Second method is, assigning one row of one of the input matrices at a time for each slave starting from first row to first slave and second row to second slave a nd so on and loop backs to the first slave when las t slave assignment is finished and repeated until all rows are finished assigning. The se two methods are implemented using Parallel Virtu al Machine and the computation is performed for different sizes of mat rices over the different number of nodes. The resul ts show that the row per slave method gives the optimal computation time in PVM ba sed parallel matrix multiplication.
High-speed DRAM architecture development

This paper is an overview of the high-speed DRAM architecture developments. We discuss developments on density growth, interface technology, memory-core architecture, and DRAM+ASIC technology. We can find the developments of density as 2/spl times/ growth instead of 4/spl times/ by each generation. Interface technologies will have a tendency to use the terminated bus structure for higher data rate. Memory-core architecture developments are the trials for actual bandwidth improvements. DRAM+ASIC technologies seem to require universal interface solutions. We tried to show that no single solution is able to cover the wide diversity of future system requirements.
Detection Method for Abnormal User of Social Network Based on Behavior Characteristics

In this paper, an anomaly user detection method based on user's basic characteristics is proposed for abnormal users in social networks. Using the G-N community discovery algorithm to divide users into orphan users and community users, combined with rough set theory, the basic characteristics of the user weight, according to the feature weight of the feature selection, the use of feature weight and feature trust value to calculate the user's credibility, and establish an exception user detection model. The experimental results show that the proposed model is suitable for detecting large-scale data sets, and has high stability and high accuracy compared with traditional methods.
Adaptive-Transmission-Power Ad Hoc On-Demand Distance Vector Routing Protocol for Mobile Ad hoc Network

The conventional routing protocols in mobile ad hoc network (MANET) using conventionally a common transmission power for both tranmsission and overheads to transfer the data packet from the source to the destination node have been revisted. Hence, an technique was developed to establish an algorithmAdaptive-Transmission-Power Ad Hoc OnDemand Distance Vector (ATP-ADOV) routing protocol to control the transmission of power dynamically and overheads in MANET. The proposed ATP-ADOV reduced the energy consumption in the networks and improved the lifetime of the particiapting mobile nodes as well as that of the lifespan of the networks.
The Role of Interactivity in Web-Based Educational Material

Summary: The effect of interactivity within an educational website was examined regarding learning outcomes, website satisfaction, and student time-on-task. The participants consisted of 72 seventh grade students from 5 classrooms. Each participant was randomly assigned to use one of three science websites with similar content but varying levels of interactivity. Results indicate that interactivity positively influenced learning outcomes, website satisfaction, and time-on-task.
Design and Simulation of AGC and AVR for a Multi Area Interconnected System

This work deals with the automatic generation control (AGC) of interconnected thermal systems with combination of the automatic voltage control (AVR) and load Management. In this particular work thermal unit is considered with three area concept. The primary purpose of the AGC is to balance the total system generation against system load and losses so that the desired frequency and power interchange with neighboring systems are maintained. Any mismatch between generation and demand causes the system frequency to deviate from scheduled value. Thus high frequency deviation may lead to system collapse. Further the role of automatic voltage regulator is to hold terminal voltage magnitude of synchronous generator at a specified level. The interaction between frequency deviation and voltage deviation is analyzed in this paper. System performance has been evaluated at various loading disturbances. On the other hand, in those days more emphasis on generation side rather than load side. In this paper load management scheme is also considered. Load management is normally used to reduce the total load demand of power systems during periods of peak demands in order to maintain the security of the system. Research has been carried out in order to identify additional functions and benefits that load management can bring to end users and utilities. Keywords: Automatic Generation Control, automatic Voltage Generator, Area Control Error, fuzzy Logic Control.
A comparative analysis of Cisco Performance Routing (PfR) and other performance enhancing techniques - Cisco QoS and Path Control

Eleven years ago Cisco introduced three types of applications on the same converged network and named it AVVID (Architecture for Voice, Video and Integrated Data). In spite of the initial interest and hype surrounding AVVID, the feature was and still is unable to confront problems such as: data priority, load balancing and network congestion. The work in this report addressed these issues within the network. Different Cisco Internetwork Operating System (IOS) methods: routing protocols, Cisco IOS QoS (including LLQ, LFI and Header Compression), Path Control and Cisco Performance Routing (PfR) were all tested to see which feature would work best at enhancing network performance. A practical network was created in which these features were carefully implemented one after another. In every step, the results were observed and recorded and if necessary repeated in order to check their validity. The results for RTT (Round Trip Time) and MOS (Mean Opinion Score) showed that in comparison to Path Control and Cisco IOS QoS (Quality of Service) mechanisms, Cisco PfR was able to generate routes on the fly during times of network congestion. This cannot be achieved with the use of Cisco QoS and Path Control which makes Cisco Performance Routing a far more superior feature. Although the focus of the work was VoIP (Voice over Internet Protocol) data, it is possible to construct a network with any application as Cisco PfR is able to reroute data. Cisco PfR is an easily accessible IOS feature which, once implemented, requires very little updating and maintenance.