Miracle-dz/UniFAR-FTUs huggingface dataset:
Reduction and Organization Selector Selection and Classification Non-Annotated Dataset Annotated Dataset Training Classifier user Learning Cycle

Statistical analysis and pattern recognition have become a daunting endeavour in face of the enormous amount of information in datasets that have continually been made available. In view of the infeasibility of complete manual annotation, one seeks active learning methods for data organization, selection and prioritization that could help the user to label the samples. These methods, however, classify and reorganize the entire dataset at each iteration, and as the datasets grow, they become blatantly inefficient from the user’s point of view. In this work, we propose an active learning paradigm which considerably reduces the non-annotated dataset into a small set of relevant samples for learning. During active learning, random samples are selected from this small learning set and the user annotates only the misclassified ones. A training set with new labelled samples increases at each iteration and improves the classifier for the next one. When the user is satisfied, the classifier can be used to annotate the rest of the dataset. To illustrate the effectiveness of this paradigm, we developed an instance based on the optimum path forest (OPF) classifier, while relying on clustering and classification for the learning process. By using this method, we were able to iteratively generate classifiers that improve quickly, to require few iterations, and to attain high accuracy while keeping user involvement to a minimum. We also show that the method provides better accuracies on unseen test sets with less user involvement than a baseline approach based on the OPF classifier and random selection of training samples from the entire dataset.
Predictive path tracking of mobile robots. Application to the CMU NavLab

Presents a new path tracking method and its application to an autonomous vehicle, the NavLab (road-worthy truck modified for research in outdoor navigation). The method applies predictive control theory to obtain the appropriated steering angle taking into account the vehicle velocity. The authors consider both the single-input single output case and the multivariable formulation of the predictive control problem. Moreover they describe the implementation of the NavLab controller. <<ETX>>
Deeper Learning With Advances in Discourse Science and Technology

The need for improving learning at deeper levels has been recognized in the national and international arena because language literacy, numeracy, and other core skills will not go the distance in handling 21st-century tasks. The challenge is aggravated by major gaps in school curricula and professional development of instructors. There is inadequate training of strategies for comprehending technical material, generating inferences, metacognition, integrating information from multiple documents, detecting misinformation, self-regulated learning, communicating through different media, collaborative problem solving, and other 21st-century skills. Some of these gaps can be filled with advances in discourse science and technology. Discourse scientists have identified the mental representations and processing mechanisms associated with different discourse levels. These levels stretch beyond word decomposition, vocabulary, and syntax (called basic literacy skills) into the deeper realms of semantics, situation (mental) models, rhetorical structure, and pragmatics. Computer tools automatically scale discourse on many levels so that individuals can get immediate multilevel feedback on text complexity, essay quality, and answers to questions. Discourse scientists have also identified effective strategies for training students how to improve comprehension of difficult material and mastery of a broad array of 21st-century competencies. These strategies have been implemented in intelligent, adaptive computer technologies, including those with conversational agents that take on different roles (e.g., peers, tutors, mentors). Future challenges lie in creating a curriculum for 21st-century skills that embrace discourse science and in scaling up discourse technologies that facilitate deeper learning.
A hybrid method for achieving high accuracy and efficiency in object tracking using passive RFID

Passive RFID tags have been widely utilized for object tracking in indoor environment due to their low cost and convenience for deployment. The RFID readings gathered from real world are often noisy. Existing approaches for tracking objects with noisy RFID readings are mostly based on using Particle Filter (PF). However, continuous execution of particle filter will suffer from high computational cost on resource constrained RFID-enabled devices. In this paper, we propose a hybrid method for tracking mobile objects with high accuracy and low computational cost. This is achieved by an adaptively switching between using WCL (Weighted Centroid Localization) and PF according to the estimated velocity of the moving object. We have evaluated the performance of our hybrid method through extensive simulations. We have also validated the performance results by implementing the method in two applications, namely, indoor wheelchair navigation and in-station LRV (Light Rail Vehicle) tracking in one of the Hong Kong MTR depots. The result shows that our proposed method outperforms both WCL and PF in either accuracy or computational cost.
Abnormal Brain Iron Metabolism in Irp2 Deficient Mice Is Associated with Mild Neurological and Behavioral Impairments

Iron Regulatory Protein 2 (Irp2, Ireb2) is a central regulator of cellular iron homeostasis in vertebrates. Two global knockout mouse models have been generated to explore the role of Irp2 in regulating iron metabolism. While both mouse models show that loss of Irp2 results in microcytic anemia and altered body iron distribution, discrepant results have drawn into question the role of Irp2 in regulating brain iron metabolism. One model shows that aged Irp2 deficient mice develop adult-onset progressive neurodegeneration that is associated with axonal degeneration and loss of Purkinje cells in the central nervous system. These mice show iron deposition in white matter tracts and oligodendrocyte soma throughout the brain. A contrasting model of global Irp2 deficiency shows no overt or pathological signs of neurodegeneration or brain iron accumulation, and display only mild motor coordination and balance deficits when challenged by specific tests. Explanations for conflicting findings in the severity of the clinical phenotype, brain iron accumulation and neuronal degeneration remain unclear. Here, we describe an additional mouse model of global Irp2 deficiency. Our aged Irp2−/− mice show marked iron deposition in white matter and in oligodendrocytes while iron content is significantly reduced in neurons. Ferritin and transferrin receptor 1 (TfR1, Tfrc), expression are increased and decreased, respectively, in the brain from Irp2−/− mice. These mice show impairments in locomotion, exploration, motor coordination/balance and nociception when assessed by neurological and behavioral tests, but lack overt signs of neurodegenerative disease. Ultrastructural studies of specific brain regions show no evidence of neurodegeneration. Our data suggest that Irp2 deficiency dysregulates brain iron metabolism causing cellular dysfunction that ultimately leads to mild neurological, behavioral and nociceptive impairments.
KNOWLEDGE AND SELF-EFFICACY AMONG YOUTH TO MAKE POSITIVE BEHAVIOR CHOICES

The role that schools play in the health of our youth is of public health significance because they have the ability to provide health services directly to children and affect health outcomes. Many of today's health challenges among youth are onset by behavioral choices that they make. Schools are in the unique position to make an impact on behavioral choices through the use of health education programs and services. As adolescent issue become more complex, it is essential for schools to take on a comprehensive school health education program. The comprehensive approach focuses on increasing knowledge, self-efficacy, and behavior intention, while providing an environment that is conducive to maintaining and sustaining positive health behaviors into adulthood. This paper advocates for the use of comprehensive health education programs to target adolescent problem areas and increase overall health and well-being. The main concept and theme in this paper is establishing, improving, and increasing self-efficacy using comprehensive school health programs to address the particular needs of adolescents based on theories of health behavior change that are discussed throughout the paper. Evidence from successful school-based programs suggests that the comprehensive approach is powerful in increasing knowledge, self-efficacy, and behavior intention/change. Policies need to be centered on improving school health, and schools also need to take the initiative to implement a strong framework into their systems. An increase in morbidity and mortality among adolescents suggests the need for continued coordinated efforts in order to decrease negative behaviors that may carry into adulthood.
Sonic Hedgehog Mediates the Proliferation and Recruitment of Transformed Mesenchymal Stem Cells to the Stomach

Studies using Helicobacter-infected mice show that bone marrow-derived mesenchymal stem cells (MSCs) can repopulate the gastric epithelium and promote gastric cancer progression. Within the tumor microenvironment of the stomach, pro-inflammatory cytokine interferon-gamma (IFNγ) and Sonic hedgehog (Shh) are elevated. IFNγ is implicated in tumor proliferation via activation of the Shh signaling pathway in various tissues but whether a similar mechanism exists in the stomach is unknown. We tested the hypothesis that IFNγ drives MSC proliferation and recruitment, a response mediated by Shh signaling. The current study uses transplantation of an in vitro transformed mesenchymal stem cell line (stMSCvect), that over-expresses hedgehog signaling, in comparison to non-transformed wild-type MSCs (wtMSCs), wtMSCs transfected to over-express Shh (wtMSCShh), and stMSCs transduced with lentiviral constructs containing shRNA targeting the Shh gene (stMSCShhKO). The effect of IFNγ on MSC proliferation was assessed by cell cycle analysis in vitro using cells treated with recombinant IFNγ (rmIFNγ) alone, or in combination with anti-Shh 5E1 antibody, and in vivo using mice transplanted with MSCs treated with PBS or rmIFNγ. In vitro, IFNγ significantly increased MSC proliferation, a response mediated by Shh that was blocked by 5E1 antibody. The MSC population collected from bone marrow of PBS- or IFNγ-treated mice showed that IFNγ significantly increased the percentage of all MSC cell lines in S phase, with the exception of the stMSCsShhKO cells. While the MSC cell lines with intact Shh expression were recruited to the gastric mucosa in response to IFNγ, stMSCsShhKO were not. Hedgehog signaling is required for MSC proliferation and recruitment to the stomach in response to IFNγ.
Impacts of Energy and Carbon Taxes on the US Pulp and Paper Industry

Market-based climate change policy instruments, such as energy and carbon taxes, have frequently been proposed as efficient means to stimulate industrial energy efficiency improvements and to reduce carbon emissions. This paper presents an assessment of the impacts that energy and carbon taxes may have on energy use and emissions profiles of the US pulp and paper industry. Time series data and engineering information are combined to endgenously specify changes in technologies and fuel mix within a dynamic computer model. The results of the model indicate under a wide range of assumptions that revenue-neutral energy or carbon taxes will increase carbon emissions when compared to the absence of those taxes, and that carbon emissions are higher under carbon taxes than under comparable energy taxes. Those seemingly counter-intuitive results have significant policy implications which are discussed in this paper.
Phytostabilization of a landfill containing coal combustion waste.

The establishment of a vegetative cover to enhance evapotranspiration and control runoff and drainage was examined as a method for stabilizing a landfill containing coal combustion waste. Suitable plant species and pretreatment techniques in the form of amendments, tilling, and chemical stabilization were evaluated. A randomized plot design consisting of three subsurface treatments (blocks) and five surface amendments (treatments) was implemented. The three blocks included (1) ripping and compost amended, (2) ripping only, and (3) control. Surface treatments included (1) topsoil, (2) fly ash, (3) compost, (4) apatite, and (5) control. Inoculated loblolly (Pinus taeda) and Virginia (Pinus virginiana) pine trees were planted on each plot. After three growing seasons, certain treatments were shown to be favorable for the establishment of vegetation on the basin. Seedlings located on block A developed a rooting system that penetrated into the basin media without significant adverse effects to the plant. However, seedlings on blocks B and C displayed poor rooting conditions and high mortality, regardless of surface treatment. Pore-water samples from lysimeters in block C were characterized by high acidity, Fe, Mn, Al, sulfate, and trace-element concentrations. Water-quality characteristics of the topsoil plots in block A, however, conformed to regulatory protocols. A decrease in soil-moisture content was observed in the rooting zone of plots that were successfully revegetated, which suggests that the trees, in combination with the surface treatments, influenced the water balance by facilitating water loss through transpiration and thereby reducing the likelihood of unwanted surface runoff and/or drainage effluent.
A Proximal Gradient Algorithm for Decentralized Composite Optimization

This paper proposes a decentralized algorithm for solving a consensus optimization problem defined in a static networked multi-agent system, where the local objective functions have the smooth+nonsmooth composite form. Examples of such problems include decentralized constrained quadratic programming and compressed sensing problems, as well as many regularization problems arising in inverse problems, signal processing, and machine learning, which have decentralized applications. This paper addresses the need for efficient decentralized algorithms that take advantages of proximal operations for the nonsmooth terms. We propose a proximal gradient exact first-order algorithm (PG-EXTRA) that utilizes the composite structure and has the best known convergence rate. It is a nontrivial extension to the recent algorithm EXTRA. At each iteration, each agent locally computes a gradient of the smooth part of its objective and a proximal map of the nonsmooth part, as well as exchanges information with its neighbors. The algorithm is “exact” in the sense that an exact consensus minimizer can be obtained with a fixed step size, whereas most previous methods must use diminishing step sizes. When the smooth part has Lipschitz gradients, PG-EXTRA has an ergodic convergence rate of O(1/k) in terms of the first-order optimality residual. When the smooth part vanishes, PG-EXTRA reduces to P-EXTRA, an algorithm without the gradients (so no “G” in the name), which has a slightly improved convergence rate at o(1/k) in a standard (non-ergodic) sense. Numerical experiments demonstrate effectiveness of PG-EXTRA and validate our convergence results.
Understanding SNS users' intention: An extension of the technology acceptance model

As a representative of virtual community, online social networks get rapid development, and become the “light spot” of the internet industry. Although online social networks gain rapid development, few researchers pay attention to the disciplinary of online social networks. In order to study the factors that influence the behavior intention of social networking users, a theoretical framework based on TAM is proposed, with introduction of four new variables, namely “Network externalities” “Realness” “Enjoyment” and “Trust”. Then, through the collection of 302 samples from 4 colleges in Beijing, the theoretical model is empirically tested. The results of this research show that both Network externalities and Realness have significant positive impacts on behavior intention to use online social networks, but through completely different ways: Network externalities affects the intention by influencing Enjoyment and Usefulness positively, while Realness affects the intention by influencing Usefulness and Trust positively. The research result has important theoretical and practical significance for companies and researchers.
Exploring Career Stages of Midcareer and Older Engineers---When Managerial Transition Matters

Engineers in certain organizations in which technical upward movement is prematurely truncated must often move into management positions to attain continued upward career mobility. This transition is difficult for both those who make it and those who do not, and may cause good engineers to become derailed managers. On the basis of career stage theory, this study proposes an instrument to explore the career states of midcareer and older engineers. The sample came from 254 members of the Chinese Institute of Engineers in Taiwan. The proposed career stage measurement, which combines organizational position ranks and personal perceptions of autonomy and influence at work, provides a valid frame for tracking career stages. The results differentiate six career stages, including one composed mostly of engineers in middle-level management positions who apparently have fallen off track. Compared with on-track careerists, these derailed managers perceive less job success and perceive fewer up-front managerial skill requirements. They appear less self-motivating and self-learning during their managerial transitions. Both the background of the sample, which offers some causes of this derailed situation, and the abilities that engineers need to avoid derailment as they progress from one managerial stage to another are presented.
Chapter 2 THE DOUBLE DENSITY DWT

This chapter takes up the design of discrete wavelet transforms based on oversampled filter banks. In this case the wavelets form an overcomplete basis, or frame. In particular, we consider the design of systems that are analogous to Daubechies’ orthonormal wavelets — that is, the design of minimal length wavelet filters satisfying certain polynomial properties, but now in the oversampled case. The wavelets are constructed using maximally flat FIR filters in conjunction with extension methods for paraunitary matrices. Because there are more degrees of freedom in the design problem, the wavelets described in this chapter are much smoother than orthonormal wavelets of the same support. The oversampled dyadic DWT considered in this chapter is based on a single scaling function and two distinct wavelets. Having more wavelets than necessary gives a closer spacing between adjacent wavelets within the same scale. Like the dual-tree DWT, the oversampled DWT presented here is redundant by a factor of 2, independent of the number of levels. In comparison, the redundancy of the undecimated DWT grows with the number of levels.
Response Monitoring in De Novo Patients with Parkinson's Disease

Background Parkinson's disease (PD) is accompanied by dysfunctions in a variety of cognitive processes. One of these is error processing, which depends upon phasic decreases of medial prefrontal dopaminergic activity. Until now, there is no study evaluating these processes in newly diagnosed, untreated patients with PD (“de novo PD”). Methodology/Principal Findings Here we report large changes in performance monitoring processes using event-related potentials (ERPs) in de novo PD-patients. The results suggest that increases in medial frontal dopaminergic activity after an error (Ne) are decreased, relative to age-matched controls. In contrast, neurophysiological processes reflecting general motor response monitoring (Nc) are enhanced in de novo patients. Conclusions/Significance It may be hypothesized that the Nc-increase is at costs of dopaminergic activity after an error; on a functional level errors may not always be detected and correct responses sometimes be misinterpreted as errors. This pattern differs from studies examining patients with a longer history of PD and may reflect compensatory processes, frequently occurring in pre-manifest stages of PD. From a clinical point of view the clearly attenuated Ne in the de novo PD patients may prove a useful additional tool for the early diagnosis of basal ganglia dysfunction in PD.
A SVM Regression Based Skip-Ngram Approach to MT Evaluation

This paper describes an automatic MT evaluation metric named SNR by Machine Intelligence and Translation Lab. of Harbin Institute of Technology, for NIST MetricsMATR 2008 evaluation. The metric extend the idea of skip-bigram with larger span and multiple statistics. SVM regression method is adopted to tune the weights of statistics in the metric. The experimental results show that SNR correlate with human assessments better than mainstream metrics on sentence level.
Development of a location and movement monitoring system to quantify physical activity

There are currently 1 billion overweight adults in the world according to the World Health Organization (WHO). Being overweight can pose a major risk to the health of an individual. The main reason for the increase in obesity in the West is the large changes in society, for example, our work and our leisure pursuits are becoming increasingly less physically demanding. One of the main ways that, according to the WHO, an individual can combat this is to undertake 30 minutes of moderate physical activity a day. However, it can be difficult for a person to gauge this moderate physical activity. This paper describes the design of a prototype system which collects and subsequently combines data from an activity monitor and GPS device in order to help people to examine their activity patterns. The intention is that the integrated system will provide data to the user and others that will enable them to assess the effectiveness of attempts to increase activity and indicate where barriers to increased activity may exist, particularly those caused by the external environment.
Electronic procurement systems: identifying factors that foster their adoption

In this study, we developed a model to explain electronic-procurement systems (EPS) adoption, considering the technology-organization-environment framework as well as the institutional theory. This model was tested with data collected from the 2500 biggest companies operating in Portugal. Based on the t-test for equality of means we found evidence that EPS adoption is positively and significantly related to (1) firm size, (2) technological capabilities,(3) the perception companies have about the EPS success of their competitors, (4) the extent of adoption among competitors and (5) the trading partner readiness to perform electronic transactions. The logistic regression supplied further evidence that technological capabilities, firm size, extent of adoption among competitors and trading partner readiness provide a reasonable estimate for each firm’s likelihood to adopt EPS. We also found evidence that firms which main activity is commerce are more likely to adopt EPSs than companies from manufacturing or services industries.