Miracle-dz/UniFAR-FTUs huggingface dataset:
Vehicle security device

The utility model provides a vehicle security device, which is connected with a vehicle device and can be communicated with a carried machine. The vehicle safety device is provided with a first communication mechanism which can carry out first encryption communication used for first mutual authentication with a carried machine. An identification code management mechanism is connected with the first communication mechanism and is provided with a first code and a second code, and the identification code management mechanism can carry out second encryption communication used for second mutually authentication with the first communication mechanism via the first code. The second communication mechanism is connected with the identification code management mechanism, and the second communication mechanism can carry out third encryption communication used for third mutually authentication with the identification code management mechanism via the second code. When the first, the second and the third mutual authentication are all correct, the utility model can allow the action of the vehicle device, therefore the utility model can increase the security level of vehicles.
Condensed representations of frequent sets : application to descriptive pattern discovery

Interesting pattern discovery has recently seen an impressive progress, due to an increasing pressure from owners of large data sets and to the response of scientists by numerous theoretical and practical results. The most of data sets addressed in the beginning of the surge were sales data and the interesting patterns were in form of association rules. Very efficient solutions to this practical problem were elaborated, the root of them was the so-called APRIORI algorithm. Then, the owners of other types of data wondered if these basic methods could help them. Unfortunately, their data were different. Often, these applications could not take advantage of APRIORI. The research following the elaboration of the basic solution addressed the important application areas, where the basic solution could not be used. We addressed the problems with mining frequent patterns in different applicative contexts, especially the problems related to the large number of interesting frequent patterns present in data that are not similar to the sales data. Our methods mine a collection of patterns that may be quite different from the target pattern collection, and hopefully much more efficient to be mined in some types of data. Moreover, that different pattern collection must allow a subsequent "regeneration" of the target collection in a very efficient manner. Since the intermediate representation will be often smaller than the target collection, we call it a condensed representation. We obtained a significant improvement of the performances. The use of condensed representations is relatively novel in the field. Then new major condensed representations of simple frequent patterns are proposed, the algorithms to mine them and derive the target pattern collections. We show the practical advantages of the proposed condensed representations over the past methods, and provide an abstract view of the proposed representations in the unified structure for condensed representations.
Molecular clock dating

This chapter reviews the history of the molecular clock, its impact on molecular evolution, and the controversies surrounding mechanisms of evolutionary rate variation and the application of the clock to date species divergences. We review current molecular clock dating methods, including maximum likelihood and Bayesian methods, with an emphasis on relaxing the clock and on incorporating uncertainties into fossil calibrations.
ADYNAMIC BUFFER ZONE BASED ALGORITHM FOR SINGLE M OBILE SINK IN W IRELESS SENSOR N ETWORKS

Limit energy is a severe bottleneck of wireless sensor networks (WSNs), and limits network lifetime of WSNs. To extend network lifetime, buffer zone has been proposed. Sensors send their data packets to buffer zone. The sensors in buffer zone buffer the data packets. And then the sink visits the buffer zone to collect the data packets. This leads to that the loads of the sensors in buffer zone are toohigh and the sensors die quickly. To further extend network lifet ime, an algorithm based on dynamic buffer zone has been proposed in this paper. The algorithm divides the whole network area into some areas, and lets all areas act as the buffer zone in turn. And the reasonable times each zone acts as the buffer zone are computed with linear programming. The simulation results have shown that our proposed algorithm notably extends network lifetime.
Large-scale semi-supervised learning for natural language processing

Natural Language Processing (NLP) develops computational approaches to processing language data. Supervised machine learning has become the dominant methodology of modern NLP. The performance of a supervised NLP system crucially depends on the amount of data available for training. In the standard supervised framework, if a sequence of words was not encountered in the training set, the system can only guess at its label at test time. The cost of producing labeled training examples is a bottleneck for current NLP technology. On the other hand, a vast quantity of unlabeled data is freely available. This dissertation proposes effective, efficient, versatile methodologies for 1) extracting useful information from very large (potentially web-scale) volumes of unlabeled data and 2) combining such information with standard supervised machine learning for NLP. We demonstrate novel ways to exploit unlabeled data, we scale these approaches to make use of all the text on the web, and we show improvements on a variety of challenging NLP tasks. This combination of learning from both labeled and unlabeled data is often referred to as semi-supervised learning. Although lacking manually-provided labels, the statistics of unlabeled patterns can often distinguish the correct label for an ambiguous test instance. In the first part of this dissertation, we propose to use the counts of unlabeled patterns as features in supervised classifiers, with these classifiers trained on varying amounts of labeled data. We propose a general approach for integrating information from multiple, overlapping sequences of context for lexical disambiguation problems. We also show how standard machine learning algorithms can be modified to incorporate a particular kind of prior knowledge: knowledge of effective weightings for count-based features. We also evaluate performance within and across domains for two generation and two analysis tasks, assessing the impact of combining web-scale counts with conventional features. In the second part of this dissertation, rather than using the aggregate statistics as features, we propose to use them to generate labeled training examples. By automatically labeling a large number of examples, we can train powerful discriminative models, leveraging fine-grained features of input words.
5. ADAPTIVE CONTROL TECHNIQUES FOR ARTERIAL BLOOD PRESSURE BY MEANS OF VASOACTIVE DRUGS

Automated drug administration systems are valuable in environments where experienced personnel may be required to continuously monitor critical patients or to assist chronic long-term patients. Examples of such situations are post-surgical regulation of the mean arterial pressure (MAP) using vasodilators and infusion of insulin in diabetic patients, respectively. Manual administration may consume precious time of experienced clinical staff and can be a tedious task, while automated systems have the potential to be fast, cost effective and eventually safer as a consequence of monitoring a greater number of data, incorporating sophisticated expert decision making schemes and providing fast detection of critical conditions. The dynamics involved in the various physiological processes in the human body are usually very complex and the mathematical models adopted in the design of automated drug administration systems tend to include many simplifications. In order to achieve satisfactory performance under simplified modeling (involving, possibly, order reduction, linear approximation, dependence only on slowly time-varying parameters), random perturbations (such as postural changes, ingestion of fluids, response to pain, environment temperature, among others), and various uncertainties (circadian cycles, individual characteristics, unforeseen effects of surgical procedures, for example), designers have used the concepts of adaptation and robustness, together with some measure of performance. In terms of adaptive control techniques, the alternatives are (i) Model Reference Adaptive Control MRAC, used, e.g., by Kaufman et al. [17] and
Using Kohonen map for the placement of regular VLSI designs

The paper presents the formulation of a VLSI placement problem for regular designs (gate arrays) using a Kohonen self-organizing map. An abstract specification of the design is converted to a set of appropriate input vectors using a mathematical method, called "multidimensional scaling". These vectors which have, in general, higher dimensionality, are fed to the self-organizing map at random in order to map them onto a 2-dimensional plane of the regular chip. The mapping is done in such a way that the cells with higher connectivity are placed close to each other, hence minimizing total connection length in the design. The results show improvement over other neural network based approaches in terms of both efficiency and the quality of results. The capability of our approach in handling external ports as well as nonrectangular (rectilinear) boundaries makes it appropriate for the placement of hierarchical designs.
Human Identification by ECG Signals through Neural Network

A cardiogram (ECG) may be a bioelectrical signal that records the hearts electrical activity versus time. it's a vital diagnostic tool for assessing heart functions. The interpretation of graphical record signal is AN application of pattern recognition. The techniques utilized in this pattern recognition comprise: signal pre-processing, QRS detection, feature extraction and neural network for signal classification. totally different graphical record feature inputs were utilized in the experiments to check and notice a fascinating options input for graphical record classification. Among totally different structures, it had been found that a 3 layer network structure with twenty five inputs, five neurons within the output layer and five neurons in its hidden layers possessed the most effective performance with highest recognition rate of ninety one.8% for 5 viscus conditions
Effects of Misleading Context on Spoken Word Recognition

Congruent linguistic context can facilitate rapid spoken word recognition. The present study examined the effects of incongruent linguistic context on spoken word comprehension, and the flexibility of context-dependent comprehension strategies relative to age and hearing acuity. Young adults with normal hearing acuity, and older adults with good and poor hearing acuity, were asked to identify target words using the word-onset gating paradigm in congruent, incongruent, and neutral context conditions. Word recognition threshold were compared when the participant knew a priori if the sentence context would be relevant and when they did not. It was found that aging and hearing acuity significantly affected word recognition thresholds, and that constraining context minimized the amount of word onset necessary to identify target words. Additionally, all three participant groups had lower recognition thresholds when the relevance of context was known. The present study suggests a flexible, context-dependent listening strategy for spoken word recognition that holds for older as well as younger adults.
Predictive text input engine for Indic scripts

Languages with many letters pose a problem for text entry on reduced keyboards. Using multitap is time consuming as there can be 6-9 characters per key on a mobile phone. For singletap methods more letters per key results in more words per key sequence, i.e. greater ambiguity when selecting which word to present to the user. Todays singletap methods for mobile phones mostly rely on a dictionary and word frequencies, this works remarkably well with the Latin alphabet. But this is not enough when the number of letters per key increases. In this master thesis we investigated different methods to improve the word disambiguation. These methods include word bigrams, part of speech n-grams and keypad remappings. We have chosen the Devanagari script for our implementation as it is one of the scripts with this problem. We have worked with Hindi for the language specific data. We found that a dictionary based solution with word bigrams combined with a remapped keypad layout gave the desired results. The use of these techniques gave an increase in disambiguation accuracy, from 77% to 94%. We also saw an improvement in KSPC, from 1.0856 to 1.0154. Unfortunately, we could not find an annotated corpus good enough for a part of speech based solution to be implemented, as we think it would improve the system further.
Auxiliary-vector filters and adaptive steering for DS/CDMA single-user detection

Single-user detectors for direct-sequence code-division multiple-access (DS/CDMA) systems are characterized by their architecture, the design optimality criterion, the optimization computational complexity, and ultimately by their average bit error rate (BER) performance. With a fixed tap-weight architecture that implies zero optimization cost, the conventional signature-matched correlator positions itself as the low-cost low-performance solution. This paper submits a proposal that maintains the same tap-weight architecture and introduces the concept of maximum cross-correlation auxiliary-vector filtering associated with minimum BER adaptive steering. Calculation of the auxiliary vector taps involves standard sample average estimation of the data autocorrelation matrix, and no matrix inversion operation is required. The resulting scalar parametrized filter is optimized adaptively in the minimum BER sense by a fast stochastic approximation recursion on /spl Rscr/. Simplicity and BER comparisons place the proposed receiver competitively on the cost versus performance curve. Numerical results under realistic small sample support scenarios support the theoretical developments and indicate that performance-wise the receiver compares favorably not only to the conventional matched-filter correlator, but also to the minimum-output-energy (MOE) detector the LMS-implemented minimum-mean-square-error (MMSE) filter, and the multiuser decorrelating receiver that assumes perfectly known interfering user population.
Employing webpage contents for author disambiguation

In this project, I developed a system which is able to disambiguate names. Currently, name disambiguation is done by using internal data data which is included in the citation as the only source of information. My system is one of the first to use external data, specifically web page contents to disambiguate names. It is able to solve both types of name ambiguity, several names referring to the same object and several objects having the same name. In this project, I also address challenges when extracting information from web pages. Details of the design, implementation and evaluation are presented in this report. Subject descriptors I.2. 7 Natural Language Processing H.3. 3 Information Search and Retrieval
Assessing the Flipped Classroom in Operations Management: A Pilot Study

The author delved into the results of a flipped classroom pilot conducted for an operations management course module. It assessed students’ perception of a flipped learning environment after making them experience it in real time. The classroom environment was construed using a case research approach and students’ perceptions were studied using qualitative and quantitative research methods. The analysis revealed that a flipped classroom scores higher than a conventional, lecture-oriented setup on the following critera: student involvement, task orientation, and innovation. The study corroborated the pedagogical effectiveness of the flipped learning model in promoting collaborative learning for operations management instruction. The findings underline the need to painstakingly structure the learning tasks for the fruition of a flipped classroom exercise.
A Secure Data Forwarding Approach for Cloud Storage System

Cloud computing is a concept that treats the resources on the Internet as a unified entity a cloud Users just use services without being concerned about how computation is done and storage is managed (2). General encryption schemes protect data confidentiality, but also limit the functionality of the storage system because a few operations are supported over encrypted data. Storing data in a third party's cloud system causes serious concern on data confidentiality. In order to provide strong confidentiality for messages in storage servers, a user can encrypt messages by a cryptographic method before applying an erasure code method to encode and store messages (5). Our method fully integrates encrypting, encoding, and forwarding. We analyses and suggest suitable parameters for the number of copies of a message dispatched to storage servers and the number of storage servers queried by a key server. These parameters allow more flexible adjustment between the number of storage servers and robustness.
Performance Analysis of Video Transmission Over IEEE802.11n Wireless Networks

Multimedia services over wireless networking environment have become increasingly popular, especially for the online video streaming services and applications. This research analyzes the performance of video transmission over IEEE 802.11n in term of throughput, delay, and peak signal to noise ratio (PSNR) to find the characteristics of video streaming over a wireless network and to also propose a method to improve the transmission performance. Videos on YouTube from various categories were employed as a video dataset for evaluation in this research. Video splitting, video blending, and optimized reconstruction were proposed as video pre-processing and video reconstruction techniques used for enhancing the transmission usage and the quality of the transmitted video. Results indicated that the approach can improve the PSNR to the desired level.
Link Prediction in Dynamic Weighted and Directed Social Network using Supervised Learning

Link Prediction is an area of great interest in social network analysis. Previous works in the area of link prediction have only focused on networks where the links once created cannot be removed. In many real world social networks, the links should be assigned strengths; for example, the strength of a link should decrease over time, if there are no interactions between the two nodes for a long time and increase if the two nodes interact often. In this thesis we modify existing methods of link prediction to apply to weighted and directed networks. The features, developed in previous works for unweighted and undirected networks, are extended to apply to networks whose links have weight and direction, and algorithms are developed to calculate them efficiently. These network features are used to train an SVM classifier to predict which nodes will be connected by a link and which links will be broken in the future. The results obtained using Twitter @-mention network demonstrate that the method developed in this thesis is very effective. Link Prediction in Dynamic Weighted and Directed Social Network using Supervised Learning by Ricky Laishram Submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science Syracuse University August 2015
Conflict detection and alerting for separation assurance systems

Many of the nation's airspace users desire an increase in efficiency in the air traffic control system. Some feel that an increase in efficiency can be achieved by moving away from a centralized control paradigm towards a distributed control paradigm. In a distributed control paradigm, the cockpit crew would have more freedom in selecting and modifying their routes. This new paradigm is part of the Free Flight concept. Increased autonomy of operations will require increases in cockpit information, pilot responsibility, and avionics capability. One of the key enabling capabilities will be the detection and resolution of air traffic conflicts. CD&R systems will serve in a separation assurance role for the flight deck. Conflict detection is the first step. This paper contains the following: background information on airborne conflict detection and alerting; a baseline conflict detection algorithm based on instantaneous state vectors; and an analysis of the algorithm's performance for flight data from the ADS-B Operational Evaluation.
On folding and unfolding with linkages and origami

We revisit foundational questions in the kinetic theory of linkages and origami, investigating their folding/unfolding behaviors and the computational complexity thereof. In Chapter 2, we exactly settle the complexity of realizability, rigidity, and global rigidy for graphs and linkages in the plane, even when the graphs are (1) promised to avoid crossings in all configurations, or (2) equilateral and required to be drawn without crossings (“matchstick graphs”): these problems are complete for the class ∃R defined by the Existential Theory of the Reals, or its complement. To accomplish this, we prove a strong form of Kempe’s Universality Theorem for linkages that avoid crossings. Chapter 3 turns to “self-touching” linkage configurations, whose bars are allowed to rest against each other without passing through. We propose an elegant model for representing such configurations using infinitesimal perturbations, working over a field R⟨ε⟩ that includes formal infinitesimals. Using this model and the powerful Tarski-Seidenberg “transfer” principle for real closed fields, we prove a self-touching version of the celebrated Expansive Carpenter’s Rule Theorem. We switch to folding polyhedra in Chapter 4: we show a simple technique to continuously flatten the surface of any convex polyhedron without distorting intrinsic surface distances or letting the surface pierce itself. This origami motion is quite general, and applies to convex polytopes of any dimension. To prove that no piercing occurs, we apply the same infinitesimal techniques from Chapter 3 to formulate a new formal model of self-touching origami that is simpler to work with than existing models. Finally, Chapter 5 proves polyhedra are hard to edge unfold: it is NP-hard to decide whether a polyhedron may be cut along edges and unfolded into a non-overlapping net. This edge unfolding problem is not known to be solvable in NP due to precision issues, but we show this is not the only obstacle: it is NP complete for orthogonal polyhedra with integer coordinates (all of whose unfolding also have integer coordinates). Thesis Supervisor: Erik D. Demaine Title: Professor of Computer Science and Engineering
Mining XML Document Based on Structure

With the growing number of XML documents on the Web it becomes essential to effectively organize these XML documents in order to retrieve useful information from them. A possible solution is to apply clustering on the XML documents to discover knowledge that promotes effective data management, information retrieval and query processing. This paper presents a framework for clustering XML documents by structure. Modelling the XML documents as rooted ordered labeled trees, we study the usage of structural distance metrics in hierarchical clustering algorithms to detect groups of structurally similar XML documents. We suggest the usage of structural summaries for trees to improve the performance of the distance calculation and at the same time to maintain or even improve its quality.