Miracle-dz/UniFAR-FTUs huggingface dataset:
Hypermap-Based Distributed Geospatial Data Model

With the development of internet technology, internet GIS have applied popularly. Distribution, interoperability, component are the main characters of internet GIS. In order to access, manage, process and distributed geodata, the paper proposes a distributed geospatial data model, which is based on hypermap concepts and distributed database principle. According to the distributed geospatial model, various geospatial data including electronic maps, images, pictures, videos, attributes, hyper documents can be constructed in the model, and they are linked by a hyper link. Every medium can have one or more hyper nodes, which link another medium. A hyper node recodes some information including the IP address and catalog of linked medium. After clicking a hyper node, we can move to another IP address and browse linked medium. Furthermore, the paper defines data structure of different type hyper nodes and discusses its implementation. An internet GIS software-GeoSurf based on the distributed hypermap geospatial model have developed by our university. Many application systems based on GeoSurf have been built. These applications has shown many advantages of the distributed hypermap geospatial data model.
Synthesis of Several Isomeric Tetrathiafulvalene .pi.-Electron Donors with Peripheral Sulfur Atoms. A Study of Their Radical Cations

An efficient synthesis of one new (10) and three previously reported (9, 11, and 12) π-electron donors, which have one sulfur atom in each of the outer five-membered rings attached to the TTF system, is presented. The radical cations derived from (9-11) have been studied by ESR spec- troscopy and the spin density distribution of these radical cations, as well as those of other related radical cations, have been determined by MO calculations and correlated with the ESR spectral data. The single- crystal X-ray structures of π-donors (9) and (12) are reported. The most remarkable fact of these structures is the transverse intermolecular contacts between interstacked molecules. The external sulfur atoms of (9) play a central role in its transverse structural pattern. Complexes of donors (9) and (10) with tetracyano-p-quinodimethane (TCNQ) and te- trafluorotetracyano-p-quinodimethane (TCNQF 4 ) have been obtained and characterized
Wireless Sensor And Actuator Networks Technologies Analysis And Design

Thank you for reading wireless sensor and actuator networks technologies analysis and design. Maybe you have knowledge that, people have search numerous times for their chosen readings like this wireless sensor and actuator networks technologies analysis and design, but end up in infectious downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they cope with some infectious virus inside their desktop computer.
RECOGNITION AND CLASSIFICATION OF HUMAN ACTIVITIES USING WEARABLE SENSORS

RECOGNITION AND CLASSIFICATION OF HUMAN ACTIVITIES USING WEARABLE SENSORS Aras Yurtman M.S. in Electrical and Electronics Engineering Supervisor: Prof. Dr. Billur Barshan September 2012 We address the problem of detecting and classifying human activities using two different types of wearable sensors. In the first part of the thesis, a comparative study on the different techniques of classifying human activities using tag-based radio-frequency (RF) localization is provided. Position data of multiple RF tags worn on the human body are acquired asynchronously and non-uniformly. Curves fitted to the data are re-sampled uniformly and then segmented. The effect of varying the relevant system parameters on the system accuracy is investigated. Various curve-fitting, segmentation, and classification techniques are compared and the combination resulting in the best performance is presented. The classifiers are validated through the use of two different cross-validation methods. For the complete classification problem with 11 classes, the proposed system demonstrates an average classification error of 8.67% and 21.30% for 5-fold and subject-based leave-one-out (L1O) cross validation, respectively. When the number of classes is reduced to five by omitting the transition classes, these errors become 1.12% and 6.52%. The system demonstrates acceptable classification performance despite that tag-based RF localization does not provide very accurate position measurements. In the second part, data acquired from five sensory units worn on the human body, each containing a tri-axial accelerometer, a gyroscope, and a magnetometer, during 19 different human activities are used to calculate inter-subject and interactivity variations in the data with different methods. Absolute, Euclidean, and dynamic time-warping (DTW) distances are used to assess the similarity of the signals. The comparisons are made using time-domain data and feature vectors. Different normalization methods are used and compared. The “best” subject is defined and identified according to his/her average distance to the other subjects.
Improving Optimization Speed for Genetic Algorithms

The disadvantage of the traditional mutation operator of GAs was analyzed in this paper, and a DMO (dyadic mutation operator) was presented to take the place of the traditional one. The function of DMO to prevent premature convergence was also discussed. Meanwhile, according to the features of binary based GAs, an implicit implementation for decoding the chromosomes for GAs was presented so that the run time of the improved program for GAs was shortened by 6~50 times compared with the original one. The performance of the genetic algorithm is tested based on the DMO (GADMO) in several aspects. The experimental results show that the GADMO can converge quickly and its robustness of parameters is strong. The GADMO can prevent the premature convergence effectively. By improving the mutation operator and the decoding algorithm, the optimization speed of GA is speeded up greatly.
A model for medium and long term load forecasting based on rough set theory and heuristic radial basic function neural network

According to the characteristics of the medium and long term load forecasting, a heuristic radial basic function (RBF) neural network load forecasting model is proposed based on multiple targets such as the load gross quantity in this year, the load growth quantity in this year, the load gross quantity in prior year, the load growth quantity in prior year, and the gross domestic product (GDP). Firstly, we use the orthogonal signal correction to process primary data, and use the partial least square method to fit the unit of GDP electricity consumption. Then taking unit of GDP electricity consumption as heuristic operator, we assume the load gross quantity of next year reasonably based on original load data, and use the heuristic operator to get the corresponding GDP. Combining with the other indexes, we construct neural network extended training samples, and transform the neural network extrapolation forecast into the interpolation evaluation creatively. Finally, the predicted value of heuristic neural network forecasting model is corrected on the basis of rough set theory to further improve the forecasted result. The results of practical examples show that the proposed method has higher forecast precision and the model has feasibility and validity.
Speech enhancement using deep learning

This thesis explores the possibility to achieve enhancement on noisy speech signals using Deep Neural Networks. Signal enhancement is a classic problem in speech processing. In the last years, researches using deep learning has been used in many speech processing tasks since they have provided very satisfactory results. As a first step, a Signal Analysis Module has been implemented in order to calculate the magnitude and phase of each audio file in the database. The signal is represented into its magnitude and its phase, where the magnitude is modified by the neural network, and then it is reconstructed with the original phase. The implementation of the Neural Networks is divided into two stages.The first stage was the implementation of a Speech Activity Detection Deep Neural Network (SAD-DNN). The magnitude previously calculated, applied to the noisy data, will train the SAD-DNN in order to classify each frame in speech or non-speech. This classification is useful for the network that does the final cleaning. The Speech Activity Detection Deep Neural Network is followed by a Denoising Auto-Encoder (DAE). The magnitude and the label speech or non-speech will be the input of this second Deep Neural Network in charge of denoising the speech signal. The first stage is also optimized to be adequate for the final task in this second stage. In order to do the training, Neural Networks require datasets. In this project the Timit corpus [9] has been used as dataset for the clean voice (target) and the QUT-NOISE TIMIT corpus[4] as noisy dataset (source). Finally, Signal Synthesis Module reconstructs the clean speech signal from the enhanced magnitudes and the phase. In the end, the results provided by the system have been analysed using both objective and subjective measures.
Analysis of multilateral software confidentiality requirements

Ensuring privacy and confidentiality concerns of data owners is an important aspect of a secured information system. This is particularly important for integrated systems, which allow data exchange across organizations. Governments, regulatory bodies and organizations provide legislations, regulations and guidelines for information privacy and security to ensure proper data handling. These are usually specified in natural language formats, contain default requirements and exceptions, and are often ambiguous. In addition, interacting concerns, which are often multilayered and from different stakeholders, e.g., jurisdictions, need to be considered in software development. Similar to other security concerns, analysis of confidentiality concerns should be integrated into the early phase of software development in order to facilitate early identification of defects—incompleteness and inconsistencies, in the requirements. This dissertation presents research conducted to develop a method to detect these defects using goal models which support defaults and exceptions. The goal models are derived from annotations of the natural language sources. A prototype tool is also developed to support the method. The evaluations conducted indicate the method and tool provide benefits, including distinguishing requirement interferences and conflicts, exception handling, and navigation between annotated documents and the goal models. Although current limitations of the method include a manual user driven annotation step, the method provides features that assist in early analysis of confidentiality requirements from natural language sources.
Predicting Author Traits Through Topic Modeling of Multilingual Social Media Text

One source of insight into the motivations of a modern human being is the text they write and post for public consumption online, in forms such as personal status updates, product reviews, or forum discussions. The task of inferring traits about an author based on their writing is often called "author profiling. " One challenging aspect of author profiling in today’s world is the increasing diversity of natural languages represented on social media websites. Furthermore, the informal nature of such writing often inspires modifications to standard spelling and grammatical structure which are highly language-specific. These are some of the dilemmas that inspired a series of "shared task" competitions, in which many participants work to solve a single problem in different ways, in order to compare their methods and results. This thesis describes our submission to one author profiling shared task in which 22 teams implemented software to predict the age, gender, and certain personality traits of Twitter users based on the content of their posts to the website. We will also analyze the performance and implementation of our system compared to those of other teams, all of which were described in open-access reports. The competition organizers provided a labeled training dataset of tweets in English, Spanish, Dutch, and Italian, and evaluated the submitted software on a similar but hidden dataset. Our approach is based on applying a topic modeling algorithm to an auxiliary, unlabeled but larger collection of tweets we collected in each language, and representing tweets from the competition dataset in terms of a vector of 100 topics. We then trained a random forest classifier based on the labeled training dataset to predict
A Research on Multimedia Traffic Streaming Load Balancing Scheme for IPTV over E2E based Integrated Wireless Access Networks

Multimedia services should be smoothly provided high quality service that satisfies the QoS (Quality of Service), QoE (Quality of Experience) focused on multimedia contents in an HD class and many of selected channels applying Multicast between the backbone and access. But now, there are several problems such as channel delay-time, network bandwidth limit, related traffic distribution control, and so on. So, as providing 2D, 3D multimedia data and using MPEG4 that realizes the optimized effect in a limited Bandwidth to boost safe synchronization and superior efficiency between the present video and network, we suggest an efficient network bandwidth control technique through HIASCR (Hierarchical Intelligent Access Control Router) which is a solution for network bandwidth control technique that may occur when many users access a high-definition service in a HD class at the same time as well as trend of video compression that can decrease cost for high-definition and network efficiency. This technique transmits floating high-definition data to many users depending on number of users and we offer a method to prevent the network load.
COMPARATIVE ANALYSIS OF HDR INTRACAVITARY BRACHYTHERAPY FRACTIONATION: IN-VITRO STUDY

Cervical cancer is commonly treated with high dose rate (HDR) brachytherapy. However, the optimal fractionation and dependency on time interval between fractions in HDR intracavitary brachytherapy (ICBT) technique remain controversial. In this study, three types of fractionated irradiation technique were investigated using human cervical cancer (HeLa) cells to determine the most effective fractionation regime that would be implemented in ICBT treatment. HeLa cells were irradiated using HDR remote-afterloading brachytherapy machine with 3 types of fractionation technique; a single fraction, hyperfraction and hypofraction. The fractionated dose used was 9 Gy/fraction with 18 Gy of the total dose. Cell survival curves were obtained from standard clonogenic assay fitted with Pade Linear Quadratic (PLQ) model using OriginPro 9.2 software. Radiobiological analysis of irradiated HeLa cells was performed based on the cell survival curves. The cell survival curves for single fraction, hyperfraction and hypofraction technique indicate the time interval between fractions impacting the cells survival. The survival curve shoulder of single irradiation tends to be constant as the doses increase more than 12 Gy. For hyperfractionation, the shoulder region was wider which indicates the cells have higher survival. Hypofractionated irradiation had steeper shoulder as the dose increase. In this case, the cancer cells may not have chances to recover and have a lower survival fraction. The radiobiological analysis presented by PLQ model shows that hypofractionated irradiation with 9 Gy/fraction delivered in two fractions might be the most effective fractionation to be implemented in cervical cancer treatment using HDR-ICBT irradiation.
Synthesis and Reactions of Nucleoside 5‘-Diphosphate Imidazolide. A Nonenzymatic Capping Agent for 5‘-Monophosphorylated Oligoribonucleotides in Aqueous Solution

We have synthesized adenosine and 7-methylguanosine 5‘-diphosphate imidazolides from imidazole and the corresponding nucleoside 5‘-diphosphates. The phosphorimidazolide bond of the compounds was susceptible to hydrolysis and hydrolyzed gradually in neutral aqueous solution, but it was more stable than that of the corresponding imidazolides of nucleoside 5‘-monophosphate. The 7-methylguanosine 5‘-diphosphate imidazolide reacted with guanosine 5‘-monophosphate or 5‘-monophosphorylated oligoribonucleotides in neutral aqueous solution in the presence of an Mn2+ ion catalyst converting to the cap portion of mRNA or the capped m7Gppp-oligoribonucleotides in substantial yields. The condensation reaction of adenosine 5‘-diphosphate imidazolide with adenosine 5‘-monophosphate took place similarly in neutral aqueous solution by a divalent metal ion-catalyst such as Mg2+ or Mn2+, yielding diadenosine 5‘,5‘-triphosphate.
Can trust and social benefit really help? Empirical examination of purchase intentions for wearable devices

This paper empirically examines the effects of critical determinants such as universal access, satisfaction, reputation, and familiarity on purchase intentions for wearable devices through a paradigm of trust and social benefit. Data was collected from an online survey. The formal questionnaire consisted of two sections. The first section measured respondent perceptions of each construct in the research model. The second section interpreted the basic personal data of respondents. The study results indicate that both trust and social benefit can positively influence purchase intention for wearable devices. Universal access and familiarity fail to influence trust directly but can influence purchase intention by way of social benefit, as expected. The research extends existing purchase intention literature for wearable devices by identifying the importance of universal access, context-awareness, reputation, and familiarity, and by probing the relationship of trust, social benefit, and purchase intention. The results suggest prescriptive strategies for sellers of wearable devices.
Unsupervised Event Extraction from News and Twitter

We appreciate the help of our client, Mohamed Magdy, a Ph.D. student of DLRL, Virginia Tech. We also thank NSF, who has funded the IDEAL project (NSF IIS – 1319578). Since we are working on some publications based on this project, we are not going to share our source code at this moment. We’ll consider sharing that after papers are published.
Deeply Supervised Multimodal Attentional Translation Embeddings for Visual Relationship Detection

Detecting visual relationships, i.e. triplets, has been a challenging Scene Understanding task approached in the past via linguistic priors or spatial information in a single feature branch. We introduce a new deeply supervised two-branch architecture, the Multimodal Attentional Translation Embeddings, where the visual features of each branch are driven by a multimodal attentional mechanism that exploits spatio-linguistic similarities in a low-dimensional space. We present a variety of experiments comparing against all related approaches in the literature, as well as by re-implementing and fine-tuning several of them. Results on the commonly employed VRD dataset [1] show that the proposed method clearly outperforms all others, while we also justify our claims both quantitatively and qualitatively.
Saliency Extraction Based on Visual Attention Model

Overview of the paper is proposed of static and dynamic saliency calculation based on attention mechanism.It mainly consists of two parts:saliency extraction of static image and saliency extraction of dynamic image.Static saliency calculation firstly introduces visual attention models of Itti model and Stentiford model,then analyze the technology of attention model based on segments.The model of saliency extraction in dynamic image is the fusion of static attention model and dynamic attention model,strong attention bias fusion and attention model based on motion.The paper introduces some visual attention models.Have a certain discussion in these visual attention models,and point out their advantages and disadvantages and the application.It provides some basis in image retrieval,human computer interaction,video surveillance and other fields for the visual attention model.
Regression diagnostics and resistant fits for generalized estimating equations

The Generalized Estimating Equations (GEE) procedure ofLiang and Zeger (1986) can be highly influenced by the presence ofunusual data points. Deletion diagnostics are introduced that consider leverage and residuals to measure the influence ofa subset ofobservations on the estimated regression parameters and on the estimated values ofthe linear predictor. Computational formulae are provided which correspond to the influence ofa single observation and ofan entire cluster ofcorrelated observations. The proposed diagnostics are generalizations ofDBETA (Belsley et al. (1980» and Cook's D (Cook (1977» oflinear regression. As an alternative approach, the influence ofobservations is addressed by Resistant Generalized Estimating Equations (REGEE). A generalization of the GEE procedure, REGEE gives parameter estimates and fitted values which are resistant to influential data. Robustness is achieved in REGEE through the inclusion of a diagonal weight matrix for each cluster in the estimating equations which downweight the multivariate response vector element-wise. The weights are defined with respect to leverage, corresponding to the Mallows class (Carroll and Pederson (1993», or residual, the Schweppe class (pregibon (1982». The large sample and small sample properties are studied. An example of medical practice data is given to illustrate the use ofthe deletion diagnostics and REGEE for correlated binary regression.
The Reaction Pattern of the MoCl5-Mediated Oxidative Aryl-aryl Coupling

The oxidative coupling of electron rich aryls by molybdenum pentachloride is reported. The stoichiometry of the coupling reaction reveals that MoCl 5 is a single electron acceptor. Interestingly, the 1,2-dialkoxy substitution pattern is pivotal for the aryl-aryl coupling.
The Mini-system Design Based on MSP430F169

The mini-system,mini-application system,is made up of the minimum components.In regard to MSP430 series microcontrollers,the mini-system generally includes MSP430F169,oscillator circuit and reset circuit.In the paper,the characteristics of MSP430F169 are introduced.Also the schematic diagrams of circuit theory about those modules such as power module,reset circuit module,oscillator circuit module,TAG module,and series module are designed and presented.In addition,the functions of all modules are put forward.The minisystem can be used for on-line download,emulation and debug,and as proved by lots of experiments,it is scientific,dependable,and can be broadly adapted to the fields of the education,scientific research and electric design.
Artificial Bee Colony Algorithm for Pairwise Test Generation

Our dependence on software applications has become dramatic in many activities of our daily life as they help to increase the efficiency of our tasks. These software applications have many sets of input values, parameters, software/hardware environments and system conditions, which need to be tested to ensure software reliability and quality. However, the whole comprehensive software testing is virtually not possible due to marketing pressure and resource constraints. In an attempt to solve this problem, there has been a development of a number of sampling and pairwise strategies in the literature. In this paper, we evaluated and proposed a pairwise strategy named Pairwise Artificial Bee Colony algorithm (PABC). According to the benchmarking results, the PABC strategies outdo some existing strategies to generate a test case in many of the system configurations taken into consideration. In a case where PABC is not at its optimal stage or its best performance, the experiments of a test case are effectively competitive. PABC progresses as a means to achieve the effective use of the artificial bee colony algorithm for pairwise testing reduction.