Miracle-dz/UniFAR-FTUs huggingface dataset:
Open domain news text relationship extraction based on dependency syntax

Entity relation extraction is an important sub-task of information extraction. Its goal is to determine whether there is a semantic relationship between entities. For Chinese, semantic relations are more complex and the effect of entity relation extraction is more significant. Besides, the use of verbs as a relational expression in Chinese is likely to cause ambiguity in the relationship between entities due to the intricate nature of Chinese grammar. Starting from the relationship expression, we propose and implement an open-field Chinese relationship extraction method based on dependency analysis. Dependency analysis is performed on the input single sentence and whether the single sentence is a verb-predicate statement is determined by the dependency arc of the dependency analysis output. If it is a verb-predicate statement, the Chinese grammatical heuristic rule is used to extract the relationship expression. The triad is evaluated according to the position of the argument elements. At last, the qualified triple is output. The experiments on SogouCA and SogouCS corpus show that the proposed method is suitable for large-scale corpus and has good performance and portability. And experimental results F-score is up to 81.6%.
Improving Security Accesibility Using Speech Recognition System Based on Pitch, Cepstrum and Resonance Frequency

Access to the system becomes very important to note. Voice character becomes a significant differentiator to detect one person with another. The introduction discusses ways to use a userfriendly GUI, where each sample can be used and matched based on pitch, cepstrum and formant comparisons. The results of experiments using Internal and External software show stable results on the system being designed. The system can identify every user recorded in the data by bringing up photos that the user detected. Delay in decision-making by the system can be compared with the application of appropriate algorithm. The results show that the system can work optimally and accurately.
Liquid crystal elastomer shell actuators with negative order parameter

A theoretically predicted case of anti-ordered liquid crystal rubber is realized in the form of spherical artificial muscles. Liquid crystals (LCs) are nonsolids with long-range orientational order, described by a scalar order parameter 〈P2〉=12〈3cos2β−1〉. Despite the vast set of existing LC materials, one-third of the order parameter value range, −1/2 < 〈P2〉 < 0, has until now been inaccessible. Here, we present the first material with negative LC order parameter in its ground state, in the form of elastomeric shells. The optical and actuation characteristics are opposite to those of conventional LC elastomers (LCEs). This novel class of anti-ordered elastomers gives access to the previously secluded range of liquid crystallinity with 〈P2〉 < 0, providing new challenges for soft matter physics and adding a complementary type of LCE actuator that is attractive for applications in, e.g., soft robotics.
Robust Face Recognition Via Dual Nuclear Norm Low-rank Representation and Self-representation Induced Classifier

For robust face recognition, we particularly focus on the ubiquitous scenarios where both training and testing images are corrupted due to occlusions. In the previous low-rank based methods, each error image is stacked into a vector and formed an error matrix together, and then L1-norm or L2-norm is utilized to characterize the matrix. However, the structure information of error images will lose in the step of stacking. In this paper, we propose a novel method by utilizing a low rank hypothesis on the representation term and the error term simultaneously. For classification, we also adopt the discriminative self-representation induced classifier with more effectiveness and efficiency. Experimental results on different face recognition tasks show that our proposed method achieves comparable or superior performance to some state-of-the-art methods.
Guidelines on the current best practice for the management of type 1 diabetes, sport and exercise

With the overwhelming health benefits of regular exercise and the increasing recognition that individuals with type 1 diabetes can compete in all types of sport and exercise at all levels, there is increasing demand for evidence-based guidance on the management of type 1 diabetes, sport and exercise. Although there have been a number of recent studies advancing our understanding of possible management strategies, there is currently little in the way of practical guidance available for health care professionals. This article aims to empower health care professionals working in clinical practice within the field of type 1 diabetes, sport and exercise. The first section of the article summarises the physiological challenges to glucose homeostasis anticipated from different types of exercise in an individual with type 1 diabetes. The second section considers the current evidence base and recognised strategies available to overcome these physiological challenges. Although this article does not cover ‘every insulin regimen’ or ‘every eventuality’ it provides health care professionals with the knowledge to predict the impact of different sports and exercises on glucose homeostasis in an individual with type 1 diabetes, and an awareness of the potential strategies available to allow safe and successful participation in sport and exercise without compromise to glycaemic control. Copyright © 2009 John Wiley & Sons.
How to collect data to simulate the dynamic of trains-passengers' interaction

This paper presents a motivation-based model in order to explore crowd behavior. The case study is about what motivates the decision processes of passengers about choice of location on the station platform for ingressing and egressing trains. The goal of the research is twofold: to establish a cognitive generic crowd behavior modeling method and to respond to a major challenge of public transportation: to reduce dwell time to ensure a high level of service. We first introduce motivation-based modeling for the simulation of the dynamics of numerous cognitive agents and report the collection of passengers’ dynamics that was done through an extensive survey observation. Most significant variables were then extracted from factor analysis to compose and distinguish six main motivation based strategies that are to be used for the simulation of crowd behavior in the train station. Discussion is about the advantages of motivation-based simulation in terms of robustness and adaptability and conclusion about how Artificial Intelligence, Cognitive Psychology and Data Science operate together to model such complex systems.
Identification and Validation of a Novel Prognostic Gene Model for Colorectal Cancer

Aims Colon cancer (CRC), with high morbidity and mortality, is a common and highly malignant cancer, which always has a bad prognosis. So it is urgent to employ a reasonable manner to assess the prognosis of patients. We developed and validated a gene model for predicting CRC risk. Methods The Gene Expression Omnibus (GEO) database was used to extract the gene expression profiles of CRC patients (N = 181) from GEO to identify genes that were differentially expressed between CRC patients and controls and then stable signature genes by firstly using both robust likelihood-based modeling with 1000 iterations and random survival forest variable hunting algorithms. Cluster analysis using the longest distance method was drawn out, and Kaplan–Meier (KM) survival analysis was used to compare the clusters. Meanwhile, the risk score was evaluated in three independent datasets including the GEO and Illumina HiSeq sequencing platforms. The corresponding risk index was calculated, and samples were clustered into high- and low-risk groups according to the median. And survival ROC analysis was used to evaluate the prognostic model. Finally, the Gene Set Enrichment Analysis (GSEA) was performed for further functional enrichment analyses. Results A 10-gene model was obtained, including 7 negative impact factors (SLC39A14, AACS, ERP29, LAMP3, TMEM106C, TMED2, and SLC25A3) and 3 positive ones (CNPY2, GRB10, and PBK), which related with several important oncogenic pathways (KRAS signaling, TNF-α signaling pathway, and WNT signaling pathway) and several cancer-related cellular processes (epithelial mesenchymal transition and cellular apoptosis). By using colon cancer datasets from The Cancer Genome Atlas (TCGA), the model was validated in KM survival analysis (P ≤ 0.001) and significant analysis with recurrence time (P = 0.0018). Conclusions This study firstly developed a stable and effective 10-gene model by using novel combined methods, and CRC patients might be able to use it as a prognostic marker for predicting their survival and monitoring their long-term treatment.
JSEFuzz: Vulnerability Detection Method for Java Web Application

Modularity is an important feature of Java Web applications nowadays, which challenges traditional program analytical techniques. Symbolic execution and Fuzzing, as two promising methods, both have some defects. On one hand, fuzzing is difficult to detect the branch with harsh path conditions; on the other hand, symbolic execution makes it difficult to symbolize complex inputs in a modular context. To improve these defects, we have designed JSEFuzz, a vulnerability detection method for Java Web applications. JSEFuzz combines the methods of fuzzing and symbolic execution: using fuzzing to find module-level vulnerability triggering conditions and corresponding input data, using symbolic execution to transform module-level input data, and verifying vulnerability triggerability at the program level, which is proved feasibility through experiments.
Multi-Object Grasping Detection With Hierarchical Feature Fusion

Grasping in cluttered and tight scenes is a necessary skill for intelligent robotics to achieve more general application. Such universal robotics can use their perception abilities to visually identify grasps from a stack of objects. However, most existing grasping detection methods based on deep learning just focus on estimating grasping pose with single-layer features. In this paper, we present a novel grasp detection algorithm termed as multi-object grasping detection network, which can utilize hierarchical features to learn object detector and grasping pose estimator simultaneously. The network is mainly composed of two branches: 1) Object detection branch which is based on the single shot multibox detection approach to discriminate object categories and locate object positions by bounding boxes; 2) Grasping pose estimation branch where hierarchical features are fused together to predict grasping position and orientation. To improve grasping detection performance, attention mechanism is employed in hierarchical feature fusion. For evaluating the proposed model, we build a multi-object grasping dataset where every image contains numerous different graspable objects. The extensive experiments demonstrate that the multi-object grasping detection method achieves the state-of-the-art performance on both object detection and grasping pose estimation.
Supply Chain Digital Innovation in Complex Capital Projects

The present research investigates why and how organizations are adopting digital transformation in their operations and supply chains. Following a systematic review of the literature, a novel conceptual model is developed to help the management of the transition in organizations that run complex capital projects. The conceptual model is examined and afterwards will be refined based on practitioners’ feedback. The refined model is a result of crosschecking the views of senior executives in both early adopters of digitalization as well as the lagging project-based organizations. This paper is mainly reporting on the first phase of the research– that is, developing a preliminary conceptual model.
A new podocnemidid (Pleurodira: Pelomedusoides) from the Eocene of north-western Argentina, with comments on its evolutionary relationships and palaeoenvironmental settings

We report a new small podocnemidid, Gestemys powelli gen. et sp. nov., based on one nearly complete skull, lower jaw, two shells, and remains of the appendicular skeleton belonging to two specimens from the Eocene Geste Formation of the San Antonio de la Cobres Basin, Salta Province, north-western Argentina. As in other podocnemidid genera, Gestemys exhibits a fully developed and medially extensive cavum pterygoidei, an incisura columellae auris enclosing the stapes and Eustachian tube, and lacks an exoccipital quadrate contact. Gestemys powelli bears a huge foramen palatinum posterius and a short pterygoid flange that does not reach the basisphenoid suture, exposing the cavum pterygoid. Phylogenetic analyses place Gestemys powelli as a member of Podocnemididae more closely related to Erymnochelyinae than Podocnemidinae. In regards to the palaeoenvironmental settings of the Palaeogene formations (Maíz Gordo and Geste) of north-western Argentina, at least two features differentiate the palaeoenvironment where G. powelli was recovered and those of other podocnemidid turtles from the Maíz Gordo Formation: 1) G. powelli was found in situ with very little re-working; and 2) in contrast to the muddy rocks indicating the marginal lacustrine setting and humid climate of the Maíz Gordo Formation, G. powelli is associated with a coarse-grained floodplain setting and a dry, temperate climate. Such differences highlight the contrasting environmental dynamics in which podocnemidid turtles lived during the Palaeogene. http://zoobank.org/urn:lsid:zoobank.org:pub:0E631DA2-053F-4BD6-91BD-2401F34A5777
Temporal variability in zooplankton community in the western Yellow Sea and its possible links to green tides

Large-scale macro-algal blooms of Ulva prolifera (also called green tides) have appeared each summer since 2008 in the western Yellow Sea. In this study, we investigated the temporal variability in zooplankton community in the western Yellow Sea and its possible links to green tides using data from a long-term plankton survey off the coast of Qingdao, China. Environmental conditions observed in the study area during the green tide period (GTP: June–August, 2008–2013) were compared to the non-green tide period (NGTP: June–August, 2005–2007), to support the contention that variations observed in zooplankton community may be attributed to the green tides, as opposed to natural climatic or environmental variations. Zooplankton assemblage structure observed during the GTP was then compared to the NGTP. Significant variations were detected both in zooplankton abundance and assemblage structure between the two defined periods. The abundance of zooplankton, mainly copepods, was significantly decreased during the GTP. Meanwhile, the relative abundance of copepods decreased by approximately 10% and that of gelatinous zooplankton, including appendicularians, chaetognaths, and medusae, almost doubled (ca. increased by 6.4%). The dominant species of meroplankton completely changed, specifically, polychaeta, and echinoderm larvae were more dominant than decapod and bivalve larvae. With regard to zooplankton size structure, the NGTP showed a higher size diversity with more small-sized organisms, while the GTP showed a lower size diversity in the community. According to general linear models, the interannual variation in summer zooplankton abundance was significantly correlated with green tides. These results indicate that the temporal changes in zooplankton community may have a close link to the green tides.
Correction of Barrel Distortion in Fisheye Lens Images Using Image-Based Estimation of Distortion Parameters

Images acquired by a fisheye lens camera contain geometric distortion that results in deformation of the object’s shape. To correct the lens distortion, existing methods use prior information, such as calibration patterns or lens design specifications. However, the use of a calibration pattern works only when an input scene is a 2-D plane at a prespecified position. On the other hand, the lens design specifications can be understood only by optical experts. To solve these problems, we present a novel image-based algorithm that corrects the geometric distortion. The proposed algorithm consists of three stages: i) feature detection, ii) distortion parameter estimation, and iii) selection of the optimally corrected image out of multiple corrected candidates. The proposed method can automatically select the optimal amount of correction for a fisheye lens distortion by analyzing characteristics of the distorted image using neither prespecified lens design parameters nor calibration patterns. Furthermore, our method performs not only on-line correction by using facial landmark points, but also off-line correction described in subsection III-C. As a result, the proposed method can be applied to a virtual reality (VR) or augmented reality (AR) camera with two fisheye lenses in a field-of-view (FOV) of 195°, autonomous vehicle vision systems, wide-area visual surveillance systems, and unmanned aerial vehicle (UAV) cameras.
LDPC coded DBICM Scheme for Two Way Relay Channel with a Pipeline Decoding Structure

Low-density parity-check (LDPC) coded bit inter-leaved coded modulation (BICM) is a bandwidth efficient coded modulation scheme and has been successfully employed in the two-way relay channel (TWRC). However, LDPC-BICM scheme does not take the correlations between coded bits from the same modulated symbol into account so that it cannot achieve the maximum a-posteriori (MAP) decoding performance. The later proposed BICM with iterative decoding scheme or the joint channel and physical-layer network coding scheme indeed consider the correlations and improve the error correction performance, but suffer from implementation issues of higher computational complexity and larger latency. In this paper, we propose an LDPC-Delayed BICM (DBICM) scheme to make a successful tradeoff between the performance and the complexity. To this end, we add a delay module between the encoder and the modulator to introduce correlations between codewords. Based on it, we propose a pipeline decoding structure and feed back the decoding result of a codeword to the demodulator as the a priori information for the next codeword. The proposed LDPC-DBICM scheme thus can utilize the correlations without increasing latency too much. We verify the effectiveness of our proposed scheme by theoretical mutual information analysis and finite-length simulations.
Estimated Missing Data on Multiple Linear Regression Using Algorithm EM (Expectation-Maximization) for Prediction Revenue Company

This study aims to identify problems in the company which in the past two years have experienced unpredictable fluctuations in revenue and do not seem to improve the overall trend in two years. This research is a type of quantitative research involving continuous data types and using 20 independent variables and 1 dependent variable. Data collection was carried out at companies in Sukabumi, Indonesia with data collection methods using interviews and documentation. In the process of data collection, there are some missing data. Based on the type of data that is retrieved, the selection method used in this research is divided into two methods, Expectation-Maximization algorithm for estimating missing data and Multiple Linear Regression to determine the effect of variables on company revenue and to predict the company revenue in the next period. The results showed that using EM Algorithm using the reference variable that has the same character can be predicted the missing data from the initial data. From the calculation of hypothesis test can be concluded that only Luggage, Cleaning Aid, and Glass Ware which have no significant effect to the company's revenue; While the calculation using multiple linear regression analysis there are 5 types of goods that have a negative influence with company revenue, the 5 types are: Hair care, Luggage, Bakery, Cleaning Aid, and Snack. Thus, the overall type of goods that are very necessary to be evaluated by the company are: Luggage, Bakery, Cleaning Aid, Snack and Glass Ware, because of the type of goods that do not have significant influence or negatively effect to the increase of the company's revenue.
A Negative Regulatory Role for RKIP in Breast Cancer Immune Response

Simple Summary Breast cancer is the second most common cancer in women worldwide. Regulation of breast cancer metastasis remains an elusive phenomenon. Elucidating the mechanistic pathway of metastatic signaling may identify targets for regulating cancer metastatic potential. Raf-1 kinase inhibitor protein (RKIP) has been shown to negatively regulate signaling pathways involved in cancer progression and metastasis. RKIP may suppress metastasis of breast cancer cells by downregulating elements of the immune system. Abstract Raf-1 kinase inhibitor protein was first identified as a negative regulator of the Raf signaling pathway. Subsequently, it was shown to have a causal role in containing cancer progression and metastasis. Early studies suggested that RKIP blocks cancer progression by inhibiting the Raf-1 pathway. However, it is not clear if the RKIP tumor and metastasis suppression function involve other targets. In addition to the Raf signaling pathway, RKIP has been found to modulate several other signaling pathways, affecting diverse biological functions including immune response. Recent advances in medicine have identified both positive and negative roles of immune response in cancer initiation, progression and metastasis. It is possible that one way that RKIP exerts its effect on cancer is by targeting an immune response mechanism. Here, we provide evidence supporting the causal role of tumor and metastasis suppressor RKIP in downregulating signaling pathways involved with immune response in breast cancer cells and discuss its potential ramification on cancer therapy.
Tetrandrine Inhibits Skeletal Muscle Differentiation by Blocking Autophagic Flux

Tetrandrine is well known to act as a calcium channel blocker. It is a potential candidate for a tumor chemotherapy drug without toxicity. Tetrandrine inhibits cancer cell proliferation and induces cell death through apoptosis and autophagy. As cancer patients usually experience complications with sarcopenia or muscle injury, we thus assessed the effects of tetrandrine on skeletal muscle cells. We report in this study that a low dose of tetrandrine (less than 5 μM) does not affect the proliferation of C2C12 myoblasts, but significantly inhibits myogenic differentiation. Consistently, tetrandrine inhibited muscle regeneration after BaCl2-induced injury. Mechanistic experiments showed that tetrandrine decreased the p-mTOR level and increased the levels of LC3 and SQSTM1/p62 during differentiation. Ad-mRFP-GFP-LC3B transfection experiments revealed that the lysosomal quenching of GFP signals was suppressed by tetrandrine. Furthermore, the levels of DNM1L/Drp1, PPARGA1 and cytochrome C (Cyto C), as well as caspase 3 activation and ROS production, were decreased following tetrandrine administration, indicating that the mitochondrial network signaling was inhibited. Our results indicate that tetrandrine has dual effects on autophagic flux in myoblasts during differentiation, activation in the early stage and blockade in the late stage. The ultimate blocking of autophagic flux by tetrandrine led to the disruption of mitochondria remodeling and inhibition of myogenic differentiation. The inhibitory effects of tetrandrine on skeletal muscle differentiation may limit its application in advanced cancer patients. Thus, great attention should be paid to the clinical use of tetrandrine for cancer therapy.