Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Approach for Finding Rank of the User based on User ’ s Information Diffusion Region

Social media is one of the best platform to diffuse an emergency information within a very short time period. To diffuse an emergency information in a region, many journalists, government information vendors and companies keep track of influential nodes of that region. But, due to the enormous growth of social media volume, it is quite challenging to identify influential node. Many researchers have focused on user’s microblogging activity to find influential node. We propose a new method to rank the node on the basis of user’s probable information diffusion region rather than counting user’s total micro-blogging activities. It is based on how many unique users are infected by the influence of targeted nodes. Using this ranking approach we can estimate users’ probable diffusion network and it could be used for future diffusion reference. We perform a set of experiments on real friendship network and random information flow data-sets to evaluate our findings. Our algorithm successfully able to find out top-influential nodes in case of email and random dataset. Consequently comparison result shows that our algorithm outperforms PageRank and Highest follower algorithms. Keywords—Most Influential node, network reachability, information diffusion region
Proposal for Standardization of Digital Color Imaging in Morphological Laboratory Diagnosis

Pictures of typical specimens selected from most kinds of morphological laboratory tests were digitized and their diagnostic reliability was evaluated. The quality of most of the properly prepared digitized pictures was almost the same as that of the slide film, but some of them required higher pixel density. In addition, there were large variations in the reproduction of colors among the displays, which may inadvertently cause erroneous diagnoses. Because some standardization of color images is indispensable to prevent this serious problem, a comprehensive model was assumed and possible methods were discussed. On the basis of the RGB system, color-matching technologies, color calibrators for displays and color charts used to adjust displays have been introduced to prevent such diagnostic accidents. Multispectral imaging is a more promising solution that can reproduce precise colors and compensate for differences in illuminant conditions. Unfortunately, the technologies used for digital color imaging will not be adequately standardized for medical application in the near future. Therefore, another temporary and practical solution based on the concept of 'diagnostic equivalence', in which a set of typical medical images with authorized diagnoses is used as a practical calibrator for common displays, should be considered at present. Intensive use of multimedia technology is rapidly progressing and digital images will soon be common not only in morphological laboratory diagnosis, but also in every other medical subfield, to which the expertise presented here is expected to be efficiently applied. 53 P roosal or Stanrdization of D iital C olor Im aing in M orplogical L abratory D ianosis
Synthesis of star-like ZnO nanoparticles and its application in dye-sensitized solar cell

In this work, we present a novel star-like ZnO nanoparticles synthesized by chemical solution method. Solution approaches to synthesize ZnO nanostructures are appealing because of their low growth temperature and good potential for scaling-up. The influences of reaction times on the thickness and morphology of star-like ZnO nanoparticles were presented. The XRD pattern showing quite intensity in every direction was also observed. The development of dye-sensitized solar cell utilized this nanostructure as photoanodes were presented. The dye-sensitized solar cell comprises a FTO glass with dense ZnO nanostructures as working electrode, a platinized FTO glass as counter electrode, N719-based dye, and I-/I3liquid electrolyte. The dye-sensitized solar cell fabricated using such nanostructure yields a highest power conversion efficiency of 1.16% and the IPCE photo-current efficiency is up to 15.32%. Electrochemical impedance spectroscopy is applied to investigate the characteristics of dye-sensitized solar cells in this study. The improvement of the electron transport in the ZnO photoanode has also been demonstrated.
ALGORITMOS GENÉTICOS APLICADOS A LA CATEGORIZACIÓN AUTOMÁTICA DE DOCUMENTOS

Resumen La categorización automática de documentos ha estado recibiendo creciente atención debido al incremento en la cantidad de información disponible en forma electrónica y a la necesidad cada vez mayor de encontrar la información buscada en un tiempo mínimo. Si bien existen numerosos algoritmos para categorizar documentos, todos ellos evaluan un subconjunto pequeño del espacio de posibles soluciones. Este trabajo presenta un algoritmo genético adaptado al problema de categorización de documentos. El algoritmo propuesto introduce 5 nuevos operadores, diseñados específicamente para la resolución del problema de categorización. Los resultados obtenidos demuestran que el algoritmo genético logra explorar el espacio de búsqueda más amplia y eficientemente que los algoritmos previos tomados como referencia.
Indoor 3 D Reconstruction with a Plane-Aware Network

In this work we present a method to train a planeaware convolutional neural network for dense depth and surface normal estimation as well as plane boundaries from a single indoor 360◦image. Using our proposed loss function, our network outperforms existing methods for singleview, indoor, omnidirectional depth estimation and provides an initial benchmark for surface normal prediction from 360◦images. Our improvements are due to the use of a novel plane-aware loss that leverages principal curvature as an indicator of planar boundaries. We also show that including geodesic coordinate maps as network priors provides a significant boost in surface normal prediction accuracy. Finally, we demonstrate how we can combine our network’s outputs to generate high quality 3D “pop-up” models of indoor scenes.
Emotion Regulation in Infancy and Attachment Classification at 15 Months

We examined early emotion regulation in children with different future attachment classifications with both mothers and fathers in 102 community families. Emotion regulation was observed in three standard laboratory anger paradigms at 7 months. Children’s attachment security with parents was assessed in the Strange Situation Paradigm at 15 months. In motherchild and father-child dyads, secure children (B) did not differ from insecure children (avoidant, A, resistant, C, disorganized/unclassifiable, D/U, all combined) in their emotion regulation scores. However, when the classification with mothers was considered, children who were classified as insecure resistant (C) with mothers had significantly lower scores than secure (B) and avoidant (A) children. There were no differences in emotion regulation among children with different attachment classifications with fathers. The findings inform the research on relations among child temperament, attachment, and emotion regulation.
Pacific Asia Conference on Information Systems ( PACIS ) 2007 Predicting College Student ’ Use of E-Learning Systems : an Attempt to Extend Technology Acceptance Model

This study makes an attempt to extend technology acceptance model (TAM) and presents a respecified conceptual model to examine the factors associated with college students’ use of asynchronous e-learning systems. A web-based learning platform was employed to assist the learning of an undergraduate-level course, management information systems (MIS), in a well-known institute of technology in the southern part of Taiwan. A cross-sectional survey was conducted. The partial least squares method was applied to validate the reliability and validity of the measurement model and assess the proposed conceptual model in this study. The empirical results indicated that college students showed great readiness and positive intentions towards the use of such e-learning system for the professional courses and suggested potential benefits from its use in the long term. The findings of this study not only can proffer practical implications for on-line professional course learning and teaching in business education, but also may serve as instrumental guidelines for e-learning system to be designed effectively to improve students’ interests and motivations in virtual learning environments.
Improve Cross Language Information Retrieval with Pseudo-Relevance Feedback

In dictionary-based Cross-language Information Retrieval systems, structured query translation has been shown to be an useful method for improving system performance. In this paper, we examine the effects of using pseudo relevance feedback to refine the structured query in the target language. We propose different methods for term weighting based on word distributions and the mutual information between expanded terms and original query terms. Our experimental results in a dictionary-based Vietnamese-English CLIR system show that while changing query terms weights has effects on improving precision, query expansion improves recall rates. The combination of these two techniques helps to improve system performance up to 12%, in terms of Mean Average Precision. Keywords— CLIR, dictionary-based, Vietnamese, structured query, Pseudo-relevance feedback, reweight query terms, query expansion
Active Power Control of aked ind Far s

Active power control can be used to balance the total power generated by wind farms with the power consumed on the electricity grid. With the increasing penetration levels of wind energy, there is an increasing need for this ancillary service. In this paper, we show that the tracking of a certain power reference signal provided by the transmission system operator can be significantly improved by using feedback control at the wind farm level. We propose a simple feedback control law that significantly improves the tracking behavior of the total power output of the farm, resulting in higher performance scores. The effectiveness of the proposed feedback controller is demonstrated using highfidelity computational fluid dynamics simulations of a small wind farm.
A Dual-TLB Method for MIPS Heterogeneous Virtualization

DBT (Dynamic Binary Translator) can directly translate and execute binary programs, enabling a compatible system by virtualizing one machine (guest) on another (host). However, the memory virtualization of guest brings in a great overhead, due to the several steps it takes to translate GVA (Guest Virtual Address) into HPA (Host Physical Address). For QEMU, a DBT with efficient memory virtualization mechanism, more than 60% of the translated code is used to virtualize memory, thus leading to a low performance of guest. In this paper, we employ the Co-Design methodology to optimize memory access performance of guest. This optimization is primarily focused on two aspects. First, hardware extensions are designed and implemented to conduct GVA to HPA translation directly. Second, we modify QEMU to cooperate with hardware to reduce translated code. By this means, the cost of memory virtualization is completely eliminated, resulting in a significant enhancement of the performance of the Loongson binary translation system. The experimental data implies that the performance of guest has been dramatically improved by 100 times for peak performance and 19.12% for average performance over the previous system. Keywords—Dual-TLB; Virtual Machine; Binary Translation; Heterogeneous Virtualization; MIPS; QEMU;
- 1-A Survey of the Research on Power Management Techniques for High Performance Systems

This paper surveys the research on power management techniques for high performance systems. These include both commercial high performance clusters and scientific high performance computing (HPC) systems. Power consumption has rapidly risen to an intolerable scale. This results in both high operating costs and high failure rates so it is now a major cause for concern. It is imposed new challenges to the development of high performance systems. In this paper, we first review the basic mechanisms that underlie power management techniques. Then we survey two fundamental techniques for power management: metrics and profiling. After that, we review the research for the two major types of high performance systems: commercial clusters and supercomputers. Based on this, we discuss the new opportunities and problems presented by the recent adoption of virtualization techniques, and again we present the most recent research on this. Finally, we summarise and discuss future research directions.
ANTIOXIDANT AND FREE RADICAL SCAVENGING ACTIVITY OF ANNULAR AND SEASONAL AYURVEDIC MEDICINAL PLANTS ELEPHANTOPUS SCABER L . AND BIOPHYTUM SENSITIVUM DC

1 Department of Biochemistry, Raja Serafoji Government College, Thanjavur-613005, Tamil Nadu, India *2 Department of Physics, Avvaiyar Government College for Women, Karaikal – 609 602, U.T of Puducherry, India 3 Department of Chemistry, Lady Doak College, Tallakulam, Madurai 625002, Tamilnadu, India 4 Trace Element Sciences Laboratory, UGC-DAE Consortium for Scientific Research, Kolkata 700 098, India 5 Radiation Biology Laboratory, UGC-DAE Consortium for Scientific Research, Kolkata 700 098, India *Corresponding Author Email: santhosh.physics@gmail.com
FIRM-AFL: High-Throughput Greybox Fuzzing of IoT Firmware via Augmented Process Emulation

Cyber attacks against IoT devices are a severe threat. These attacks exploit software vulnerabilities in IoT ﬁrmware. Fuzzing is an effective software testing technique for vulnerability discovery. In this work, we present F IRM -AFL, the ﬁrst high-throughput greybox fuzzer for IoT ﬁrmware. F IRM - AFL addresses two fundamental problems in IoT fuzzing. First, it addresses compatibility issues by enabling fuzzing for POSIX-compatible ﬁrmware that can be emulated in a system emulator. Second, it addresses the performance bottleneck caused by system-mode emulation with a novel technique called augmented process emulation. By combining system-mode emulation and user-mode emulation in a novel way, augmented process emulation provides high compatibility as system-mode emulation and high throughput as user-mode emulation. Our evaluation results show that (1) F IRM -AFL is fully functional and capable of ﬁnding real-world vulnerabilities in IoT programs; (2) the throughput of F IRM -AFL is on average 8.2 times higher than system-mode emulation based fuzzing; and (3) F IRM -AFL is able to ﬁnd 1-day vulnerabilities much faster than system-mode emulation based fuzzing, and is able to ﬁnd 0-day vulnerabilities.
Improved uncertainty estimates for geophysical parameter retrieval

Algorithms for retrieval of geophysical parameters from radiances measured by instruments onboard satellites play a large role in helping scientists monitor the state of the planet. Current retrieval algorithms based on neural networks are superior in accuracy and speed compared to physics-based algorithms like iterated minimum variance (IMV). However, they do not have any form of error estimation, unlike IMV. This thesis examines the suitability of several different approaches to adding in confidence intervals and other methods of error estimation to the retrieval algorithm, as well as alternative machine learning methods that can both retrieve the parameters desired and assign error bars. Test datasets included both current generation operational instruments like AIRS/AMSU, as well as a hypothetical future hyper- spectral microwave sounder. Mixture density networks (MDN) and Sparse Pseudo Input Gaussian processes (SPGP) were found to be the most accurate at variance prediction. Both of these are novel methods in the field of remote sensing. MDNs also had similar training and testing time to neural networks, while SPGPs often took three times as long to train in typical cases. As a baseline, neural networks trained to estimate variance were also tested, but found to be lacking in accuracy and reliability compared to the other methods.
Research on Index Construction and Path Optimization of College Students' Entrepreneurship Ability Based on Analytic Hierarchy Process

Faced with the current situation of college graduates every year in recent years, how to further enhance the entrepreneurial ability of college students by improving their innovative thinking and innovative ability, and then enhance their employment competitiveness, is an urgent problem to be solved. This paper uses the analytic hierarchy process (AHP) to make qualitative and quantitative analysis on the influencing factors of college students' entrepreneurship. It also puts forward the evaluation index system of college students' innovation and entrepreneurship ability, uses the expert correspondence method to determine the importance of each index, uses the scale method to determine the comparison dimension, and uses the analytic hierarchy process to calculate the weight and ranking of each evaluation index. It can fully understand the factors affecting entrepreneurship, avoid entering the misunderstanding of college students, and provide reference for college students to start a business.
Incidence of metastatic disease and survival among patients with newly diagnosed primary CNS tumors in the United States from 2004-2013

Background: Population-based estimates of the incidence and prognosis of metastatic disease at the initial diagnosis of primary central nervous system (CNS) tumors are currently lacking. Methods: A total of 43,455 patients diagnosed with a primary CNS tumor were enrolled to evaluate metastatic rates utilizing the data from the Surveillance, Epidemiology, and End Results (SEER) program. We used multivariate logistic regression to analyze the risk factors associated with the presence of metastasis at the first visit of patients with metastatic medulloblastoma (MB), atypical teratoid/rhabdoid tumor (ATRT), glioblastoma multiforme (GBM), or pilocytic astrocytoma (PA). Hazard ratios (HRs) and 95% confidence intervals (CIs) for cancer-specific death (CSD) of patients with these four CNS tumors were analyzed using multivariate Cox regression. Results: In patients with primary CNS embryonal tumors, the metastatic rates of patients with MB and ATRT were 14.51% and 19.25%, respectively. The metastatic rate for MB patients aged 0 to 18 years was 16.69%. In the patients with glioma, the metastatic rates of patients with PA and GBM were 1.55% and 1.39%, respectively. On multivariate logistic regression among patients with glioma, GBM (vs PA; OR, 2.12; 95% CI, 1.37 to 3.30; P=0.001) was associated with greater odds of having metastatic disease at diagnosis. On multivariate logistic regression among patients with GBM, MB, or ATRT, MB (vs GBM; OR, 4.66; 95% CI, 2.81 to 7.72; P<0.001) and ATRT (vs GBM; OR, 5.65; 95% CI, 3.27 to 9.75; P<0.001) were associated with greater odds of having metastatic disease at diagnosis. In the multivariate Cox proportional hazards model for CSD among patients with metastatic GBM or MB at diagnosis, gross total resection/total lobectomy (vs partial resection/partial lobectomy) was not related to a decreased or an increased risk of CSD. In patients with metastatic ATRT, compared to no surgery, gross total resection/total lobectomy or partial resection/partial lobectomy was not associated with a decreased risk of CSD. Conclusions: The findings in this study provide a population-based estimate of the incidence and prognosis of metastatic disease at the initial diagnosis of primary CNS tumors. These survival outcomes are relevant because they will help to prioritize future research directions to improve the treatment strategies of these metastatic CNS tumors.
A Retrospective Clinicopathological Study of Osteosarcoma Patients with Metachronous Metastatic Relapse

Purpose: Although osteosarcoma patients receive a standardized treatment, metachronous metastatic relapse still impairs the overall survival (OS). This study aimed to explore the clinicopathological features and prognostic factors of osteosarcoma patients with metachronous metastatic relapse. Patients and methods: We retrospectively analyzed 59 patients, between January 1st, 2004 and December 31st, 2013. Employed Chi-square test to recognize the differences in clinicopathological characteristics between early and late metastatic patients, and the differences between shorter and longer survival patients. Used the Kaplan-Meier method to evaluate the survival data, cox step proportional hazard test to analyze the prognostic factors associated with OS. Results: We found that early metastatic patients were prominently correlated with the male, tumor size ≥8 cm, histological grade G2, Enneking stages II, anatomic location of the distal femur, pathological of conventional types, and elevated alkaline phosphatase (ALP) level at diagnosis, (p<0.05). In parallel, the shorter survival patients were primarily linked to tumor size ≥8 cm, histological grade G2, Enneking stages II, early metastasis, multiple pulmonary metastases, lack of curative treatment after metastasis, increased level of ALP at diagnosis and LDH after metastasis, (p<0.05). The univariate analyses of the prognostic factors showed that patients who had these clinicopathological characteristics, such as male, tumor size ≥8 cm, Enneking stage IIB, multiple pulmonary metastases, lack of curative treatment after metastasis, the elevated ALP at diagnosis, elevated ALP and LDH after metastasis, had a worse OS in osteosarcoma patient with metachronous metastatic relapse, (p<0.05). The multivariate analyses showed that tumor size, type of metastasis and ALP level at diagnosis were independent factors for OS in osteosarcoma patient with metachronous metastatic relapse (p<0.05). Conclusion: These results indicated that osteosarcoma patients with metachronous metastatic relapse have special features which might be utilized to effectively predict the likelihood of early metastatic relapse and the prognosis.
Applied Production Analysis: A Dual Approach

List of figures Preface Notation Introduction 1. The production function 2. Cost functions 3. Structure of cost functions and short-run cost functions 4. The profit function 5. Flexible forms and aggregation 6. Technical change and its measurement 7. Multioutput technologies Appendix Bibliography Index.
Differences in the miRNA signatures of chronic musculoskeletal pain patients from neuropathic or nociceptive origins

Background The quality of life for millions of people worldwide is affected by chronic pain. In addition to the effect of chronic pain on well-being, chronic pain has also been associated with poor health conditions and increased mortality. Due to its multifactorial origin, the classification of pain types remains challenging. MicroRNAs (miRNA) are small molecules that regulate gene expression. They are released into the bloodstream in a stable manner under normal and pathological conditions and have been described as potential biomarkers. In the present study, we aimed to investigate whether pain may induce an aberrant, specific dysregulation of miRNA expression, depending on the origin of the pain. Methods and findings To do so, we measured the expression changes of 184 circulating miRNAs (c-miRNAs) in the plasma samples of patients with different origins of chronic musculoskeletal pain. After statistical analyses, we identified seven c-miRNA candidates that were differentially expressed depending on the nociceptive or neuropathic origin of the pain. We then developed a two c-miRNA signature (hsa-miR-320a and hsa-miR-98-5p) that was able to correctly classify the pain type of 70% of the patients from the validation set. Conclusions In conclusion, circulating miRNAs are promising biomarkers to identify and characterize the chronic pain type and to further improve its clinical management.