Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Record DeDuplication Identifying Using Febrl Framework

Record linkage is the problem of identifying similar records across different data sources. The similarity between two records is defined based on domain-specific similarity functions over several attributes. De-duplicating one data set or linking several data sets are increasingly important tasks in the data preparation steps of many data mining projects. The aim is to match all records relating to the same entity. Different measures have been used to characterize the quality and complexity of data linkage algorithms, and several new metrics have been proposed. An overview of the issues involved in measuring data linkage and de-duplication quality and complexity. A matching tree is used to overcome communication overhead and give matching decision as obtained using the conventional linkage technique. Developed new indexing techniques for scalable record linkage and de-duplication techniques into the febrl framework, as well as the investigation of learning techniques for efficient and accurate indexing.
Anomaly Detection and Modeling of Trajectories

Abstract : The recent boom in the availability and use of geolocation technologies has created a great need to understand datasets of trajectories. However, trajectories have several intrinsic attributes that make them difficult to analyze. First, their time-series nature makes applying traditional techniques challenging. Second, most datasets contain trajectories of many points, making for a high-dimensional modeling problem. Third, there are several competing notions of similarity/difference in trajectories. To deal with these challenges, this thesis proposes several methods using statistics and machine learning that provide a deep understanding of trajectory datasets. In particular, the thesis brings forth methods to perform anomaly detection and density estimation and to create spatial graphical models. A technique is presented for detecting anomalous trajectories in a dataset in an unsupervised fashion using support vector machines (SVMs) and various spatial representations of trajectories. The thesis also focuses on techniques for density estimation, that is providing a likelihood for each trajectory in a dataset. To effectively perform density estimation on trajectories, a combination of a Markovian assumption on the independence of the next position of a trajectory given its previous positions and kernel density estimation (KDE) is explored. Lastly, the thesis explores spatial graphical models. Undirected graphical models detail the conditional independence structure of a set of random variables. Given sparsity assumptions, this concept is used to build graphical models for indicator variables that have spatial locations associated with them, indicating if an agent has come near the corresponding location. Experiments were run using two real-world datasets: Automatic Identification System-tracked shipping vessels in the English Channel and every Atlantic Ocean tropical storm and hurricane track from 1949 to 2011.
Fast Sequential LS Estimation for Sinusoidal Modeling and Decomposition of Audio Signals

This work demonstrates a sequential Least Squares algorithm applied to the decomposition of sounds into sines-plus-residual models. For a given basis of r distinct frequency components, the algorithm derives recursively the Least Squares estimates of the associated amplitudes and phases. While a direct calculation achieves a O(nr2) complexity the main cost of our implementation is only of 4r multiplications per sample, whatever the length n of the analysis window. The technique is extended to basis of exponentially increasing or decreasing frequency components, which provides a fast and enhanced decomposition of rapidly varying segments of the sound. Finally, the proposed method is successfully applied to a real piano note.
A Journey Through Micro-Political Processes

During the last decades we have seen an enormous growth of change concepts available. Availability of these concepts has grown considerably due to the ongoing globalization process. A growing amount of literature has recently been focusing on the processes of the spread of these concepts. We try to take this discussion further by analyzing the implementation of such a global change concept in a Norwegian oil company. The global change concept is based on a “Business Process Reengineering” (BPR) implementation of an ERP-system. Our focus is the micro political processes that appear in the process were this global concept meets the local context, the realization of the concept into concrete action. The different perspectives, the different experiences and interests between affected actors cause processes of translations, interpretations, negotiations and constructions of meaning. The result is a complexity of agendas and understandings influencing the change initiatives. Through these discussions we illustrate the mechanisms affecting the implementation process and its unpredictability. Introduction This paper focuses on the importance of micro political processes in Organizational Development. “Every” organization with self-respect is doing OD. They have several ODprograms going on most of the time, they have their own specialists on OD-processes, they demand engagement from the operational staff, and they even pay lots of money to consultancy firms. The changes in the business sector are accelerating. Globalization and technology development is a key driver for these changes (Glasmeier1999, Amin and Thrift 1994, 1997). Market-dependent organizations have to change consequently. To get closer to a definition of what OD is, we will like to draw on several recognized writers. French, Bell & Zwaki (1994:1) puts it this way; “Organizational development consists of intervention techniques, theories, principles, and values that show how to take charge of planned change efforts and achieve success. ” Cummings & Worley (1993:2) contributes with the following approach; “. a systemwide application of behavioral science knowledge to the planned development and reinforcement of organizational strategies, structures, and processes for improving an organizations effectiveness. ” We find both these definitions reasonable, but like Levin & Klev (2002) we are critical to the focus and understanding of OD as an activity that can be planned extensively from start to closure. Our findings support the shift from control to the increasing acceptance of drift in development projects (Ciborra 2000). We argue that there is upon time to review the strategy of organizational challenges (Mintzberg 1994).
An Evaluative Study of a Distance Teacher Education Program in a University in Ghana

The study used an adaptation of Provus’ discrepancy evaluation model to evaluate a distance teacher education program in the University of Cape Coast, the premier teacher education institution in Ghana. The study involved comparing performance data of the program as perceived by students and faculty/administrators to standards prepared from the program’s design. Performance data was obtained by administering two survey instruments to a random sample of students and faculty/administrators. Discrepancies between performance and standards were reported. The study concluded that although there were some discrepancies between program standards and performance the program is fulfilling its purpose of upgrading the professional and academic performance of a large number of teachers in the public K-8 schools in Ghana.
Expression of VEGF, HGF, IL-6, IL-8, MMP-9, Telomerase in Peripheral Blood of Patients with Head and Neck Squamous Cell Carcinoma

Objectives This study investigated the telomerase expression in peripheral blood mononuclear cells (PBMCs) and the relationship between the serum level of several soluble factors such as vascular endothelial growth factor (VEGF), hepatocyte growth factor, interleukin (IL)-6, IL-8, and matrix metallopeptidase-9 and the clinicopathological features of patients with head and neck squamous cell carcinoma (HNSCC). Methods Peripheral blood samples were collected from 50 HNSCC patients and 15 normal controls. The telomerase activity in the PBMCs was measured by Telomere Repeat Amplification Protocols. The serum levels of the soluble factors were analyzed by enzyme-linked immunosorbent assay. Results The expression of telomerase in the PBMCs of HNSCC patients was significantly correlated with the N and American Joint Committee on Cancer (AJCC) stages. The serum VEGF level was significantly higher in the patients with an advanced T stage, N stage and AJCC stage. Serum VEGF was significantly related with the expression of telomerase in the PBMCs. The telomerase expression and the VEGF expression were shown to be independent factors associated with poor survival. Conclusion The telomerase expression in the PBMCs and the serum VEGF level of HNSCC patients were significantly correlated with the N stage, the AJCC stage and the prognosis.
Long-term survival of a patient with scalp angiosarcoma and multiple metastases treated using combination therapy: A case report

Angiosarcoma is a rare and deadly malignancy originating from the vascular endothelial cells. Surgery is the most effective method to cure this disease, but for metastatic angiosarcoma, a chemotherapy-based treatment is the main therapeutic choice. However, there is currently no standard chemotherapy regimen. The current study reports the case of a 66-year-old male with post-operative scalp angiosarcoma recurrence and multiple metastases. The patient obtained a complete response to first-line combination chemotherapy consisting of cyclophosphamide, epirubicin, vincristine and dacarbazine, with a progression-free survival time of eight months. After benefitting from subsequent comprehensive treatment including, cyclophosphamide, epirubicin, vincristine, dacarbazine, docetaxel, cisplatin, gemcitabine and radiotherapy and anti-angiogenic therapy, the patient obtained an overall survival time of 38 months following initial diagnosis.
Flow visualization with surface particles

A technique for rendering particles in general, and surface particles in particular, is presented. This technique incorporates an improved shading model, the use of Gaussian filters to prevent spatial and temporal artifacts, and an efficient scan-conversion algorithm. The steps of the rendering are described: shading, filtering, scan conversion, and occlusion. Ways in which the process may be varied to further improve the results and widen the range of applications are discussed. <<ETX>>
Novel inverse dynamics control strategy with different phases for the quadruped robot

Aiming to reduce the computation and implement compliant control, this paper proposes a novel inverse dynamics control strategy based on the floating-base rigid body system. The control strategy assumes that each leg of the quadruped robot organizes itself into an independent autonomous system, a serial robot. Based on this assumption, the kinematics and the dynamics models of the quadruped robot have been created. The dynamical model supposes two different models according to the leg's state. In the stance phase the serial robot affixes its base frame to the shank and iterates the rigid body dynamics algorithms from the knee joint to the body. When the serial robot is in the swing phase, the dynamics algorithm is propagated from the hip joint to the shank, whose computing direction is just the reverse against the direction of which the serial robot is in the stance phase. The quadruped system doesn't need the fixed base to the system and avoids calculating the virtual joints of 6-DOF. Therefore, the algorithm proposed in this paper makes real-time computation of the quadruped robot dynamics possible. In order to evaluate the efficiency of the inverse dynamical control strategy, experiments are accomplished based on a practical quadruped robot. The experiments, which were done on a rubber mat and on asphalt, demonstrated that the quadruped robot is able to walk adaptively.
A Bayesian Analysis of Ordinal Data Using

SUMMARY In this paper, we discuss a Bayesian method for the analysis of ordinal data. It is assumed that the ordinal data arise from an underlying latent variable which is related to a set of covariates in a generalized linear model sense. The link function which associates the class probabilities to the set of covariates is estimated as a nite mixture of probit links, thereby introducing exibility in the choice of the link function. The Bayesian model provides an easy-to-implement simulation environment and therefore avoids pitfalls of the expensive and numerically unstable maximum likelihood analysis. The class probabilities are estimated by considering the predictive distribution of a future observation given the set of covariates. Computations are performed using Gibbs sampling. The method is illustrated by using both simulated and real data sets.
Optimal data compression for multisensor target tracking with communication constraints

Target tracking using multiple sensors is one important application in military surveillance and industrial sensing. Due to the communication constraints between each sensor and the data processor that estimates the target state using all the data from multiple sensors, it is crucial for each sensor to compress its measurements optimally so that the data processor can estimate the target state with minimum mean square error. We limit the data compression at each sensor to be a linear transform that reduces the measurement dimension. We use the results by Zhang et al. (2003) to do measurement compression and obtain the optimal linear transform matrix for each sensor based on steady state analysis. To activate or remove a sensor dynamically, we consider a sequential update scheme to modify the data compression matrix for each sensor with an arbitrary dimensional requirement due to the communication constraint. We compare our approach with traditional centralized and distributed tracking schemes and indicate the advantages of using sensor data compression for tracking in a sensor network environment. Simulation results with three sensors show that the estimation accuracy of the proposed scheme is very close to that of the centralized estimator.
Wettability of End-Grafted Polymer Brush by Chemically Identical Polymer Films

Previous experiments on chemically homogeneous polymer melt−brush systems have found large deviations from theory. By using X-ray reflectivity and atomic force microscopy, we find that the equilibrium structure of these systems is complex, composed of dewetted droplets of the constituent polymers residing on a residual film of the end-grafted brush swollen by the free melt chains. When the molecular weight of the melt or the grafting density of the brush is decreased, the thickness of the residual film increases and the contact angle decreases, both indicating improved wettability. At the same time, the profile of the dewetted droplets exhibits a progressively longer and gentler tail at the ends. But a wetting−dewetting transition is never observed. By comparing the measurements to recent results of the self-consistent field theory, good quantitative agreement is found. On the other hand, Leibler’s analytical theory—which has been used almost exclusively for comparison with experiment—provides a poor desc.
Short-term relaxation responses to a voice-guided mobile phone relaxation application and self-guided relaxation

Short-term relaxation responses to a mobile phone based voice guided relaxation (SelfRelax) and a self-guided relaxation are studied. The relaxation responses are measured with State-Trait Anxiety Inventory's Form Y-1 (state), blood pressure, heart rate, and heart rate variability features (HRV). Data from fourteen subjects and 73 relaxation sessions were included in the analysis. Relaxation sessions were made in two days during normal daily life. The relaxation decreased STAI Form Y-1 scores by 5.5 points on average when pre- and post-relaxation levels were compared. Small differences were observed between the two relaxation methods. Systolic blood pressure decreased significantly with both relaxation methods in subjects whose pre-relaxation pressures were ≫ 140 mmHg. Data collected with a beat-to-beat heart rate logger turned out to be poor quality and were analysed as separate cases. Overall, the subjects gave positive feedback on the mobile phone application.
Emergent formations of a Lagrangian swarm of unmanned ground vehicles

In this paper, we present a Lagrangian swarm model that shows emergent formations of multiple autonomous unmanned ground vehicles (UGVs). The Direct Method of Lyapunov is used to construct the instantaneous velocity of each individual in the swarm. The velocity controllers ensure the cohesion and therefore the stability of the swarm. Via computer simulations, we illustrate self-organizations such as concentric circular and concentric elliptic formations.
ALORITHM FOR SHADOW DETECTION IN REAL COLOUR IMAGES

— Shadow detection in real scene images is always a challenging but yet interesting area. Most shadow detection and segmentation methods are based on image analysis. This paper aimed to give a comprehensive and critical study of current shadow detection methods. Various approaches have been discussed related to shadow detection in images. The principles of these methods rely on intensity difference or texture analysis of the shadow area and the bright area of the same surface. A real-time shadow detection scheme for color images is presented in this paper. The RBG ellipsoidal region technique is used to detect shadow in colour image.
From Lexical Entailment to Recognizing Textual Entailment Using Linguistic Resources

In this paper, we introduce our Recognizing Textual Entailment (RTE) system developed on the basis of Lexical Entailment between two text excerpts, namely the hypothesis and the text. To extract atomic parts of hypotheses and texts, we carry out syntactic parsing on the sentences. We then utilize WordNet and FrameNet lexical resources for estimating lexical coverage of the text on the hypothesis. We report the results of our RTE runs on the Text Analysis Conference RTE datasets. Using a failure analysis process, we also show that the main difficulty of our RTE system relates to the underlying difficulty of syntactic analysis of sentences.
TNF and TNF Receptor Superfamily Members in HIV infection: New Cellular Targets for Therapy?

Tumor necrosis factor (TNF) and TNF receptors (TNFR) superfamily members are engaged in diverse cellular phenomena such as cellular proliferation, morphogenesis, apoptosis, inflammation, and immune regulation. Their role in regulating viral infections has been well documented. Viruses have evolved with numerous strategies to interfere with TNF-mediated signaling indicating the importance of TNF and TNFR superfamily in viral pathogenesis. Recent research reports suggest that TNF and TNFRs play an important role in the pathogenesis of HIV. TNFR signaling modulates HIV replication and HIV proteins interfere with TNF/TNFR pathways. Since immune activation and inflammation are the hallmark of HIV infection, the use of TNF inhibitors can have significant impact on HIV disease progression. In this review, we will describe how HIV infection is modulated by signaling mediated through members of TNF and TNFR superfamily and in turn how these latter could be targeted by HIV proteins. Finally, we will discuss the emerging therapeutics options based on modulation of TNF activity that could ultimately lead to the cure of HIV-infected patients.
Context-Sensitive and Role-Dependent Spoken Language Understanding Using Bidirectional and Attention LSTMs

To understand speaker intentions accurately in a dialog, it is important to consider the context of the surrounding sequence of dialog turns. Furthermore, each speaker may play a different role in the conversation, such as agent versus client, and thus features related to these roles may be important to the context. In previous work, we proposed context-sensitive spoken language understanding (SLU) using role-dependent long short-term memory (LSTM) recurrent neural networks (RNNs), and showed improved performance at predicting concept tags representing the intentions of agent and client in a human-human hotel reservation task. In the present study, we use bidirectional and attention-based LSTMs to train a role-dependent context-sensitive model to jointly represent both the local word-level context within each utterance, and the left and right context within the dialog. The different roles of client and agent are modeled by switching between role-dependent layers. We evaluated label accuracies in the hotel reservation task using a variety of models, including logistic regression, RNNs, LSTMs, and the proposed bidirectional and attention-based LSTMs. The bidirectional and attention-based LSTMs yield signiﬁcantly better performance in this task.
Laryngotracheal transplantation

Laryngeal transplantation offers the potential for patients without a larynx to recover their voice, which is critical in our communication age. We report clinical and functional outcomes from a laryngotracheal transplant. Widespread adoption of this technique has been slowed due to the ethical concerns of life‐long immunosuppression after a nonvital organ transplant. Our patient was already on immunosuppressive medication from prior kidney–pancreas transplantation, and therefore was not exposed to added long‐term risk. We describe the unique technical advances, clinical course, and rehabilitation of this patient and the implications for future laryngeal transplantation.
Triangle Mesh-Based Surface Modeling Using Adaptive Smoothing and Implicit Texture Integration

This paper presents a framework of surface modeling from multi-view range data. The input to the algorithms are triangle meshes, each of which is from a single view range scan. The triangle meshes generated from raw data are first processed by the proposed area decreasing flow for surface denoising. Although the proposed flow is mathematically equivalent to the mean curvature flow, it can avoid the difficulty in curvature estimation and provide an optimal flowing step size. We introduce an adaptive triangle mesh smoothing scheme based on crease edge strength of each vertex using tensor voting of the normal vector field inside a geodesic window. The smoothing result makes surface normal estimation more accurate which is then used in surface mesh integration. Based on Hilton’s implicit surface-based method, surfaces from multiple views are integrated into a single 3D model. We incorporate color images to generate textured models. The algorithms are successfully applied to surface modeling from range data using two types of range scanners.
Classi cation by Pairwise Coupling

We discuss a strategy for polychotomous classi cation that involves estimating class probabilities for each pair of classes and then cou pling the estimates together The coupling model is similar to the Bradley Terry method for paired comparisons We study the na ture of the class probability estimates that arise and examine the performance of the procedure in real and simulated datasets Clas si ers used include linear discriminants nearest neighbors and the support vector machine