Miracle-dz/UniFAR-FTUs huggingface dataset:
Ensemble Approach for Twitter Sentiment Analysis

—Due to enlargement of social network and online marketing websites. The Blogs and reviews of the user are acquired from these websites. And these become useful for analysis and Decision making for various types of products, marketing and movie etc. with the extent of the usefulness of social Reviews. It is to be needed carefully analysis of that data. There are various techniques and methods are available that can accurately analyses the social information and provides greater accuracy for the analysis. But one of the major issues available with the social media data is that data is unstructured and noisy. It is to be required to solve this problem. So here in this paper a framework is proposed that includes latest data preprocessing techniques instead of noise removal like stemming, Lemmatization and Tokenization. After Pre-Processing of data ensemble methods is applied that increase the accuracy of previous classification algorithms. This method is inherent from bagging concept. First apply Decision Tree, Kneighbor and Naive Bayes classifier that not provide batter accuracy after that boosting concept is applied with the help of AdaBoost method that improves the accuracy of previous classical classifiers. At last our proposed ensemble method ExtraTree classifier is applied that inherent from bagging concept. Here we use the Extra Tree classifier that take the various sample are taken from training set and various random trees are created. It is also called as extremely randomized tree that provides extreme refined view. So that
Illustrated dictionary of environmental health and occupational safety

Information at your fingeritps. Alphabetical listing of more than 7,000 entries, with more than 600 clear, precise, detailed illustrations. Subjects include all important areas of environmental health and occupational safety: epidemiology, toxicology, risk assessment, statistics, instrumentation, medical terminology, basic sciences, and more.
Triad Constraints for Learning Causal Structure of Latent Variables

Learning causal structure from observational data has attracted much attention, and it is notoriously challenging to find the underlying structure in the presence of confounders (hidden direct common causes of two variables). In this paper, by properly leveraging the non-Gaussianity of the data, we propose to estimate the structure over latent variables with the so-called Triad constraints: we design a form of "pseudo-residual" from three variables, and show that when causal relations are linear and noise terms are non-Gaussian, the causal direction between the latent variables for the three observed variables is identifiable by checking a certain kind of independence relationship. In other words, the Triad constraints help us to locate latent confounders and determine the causal direction between them. This goes far beyond the Tetrad constraints and reveals more information about the underlying structure from non-Gaussian data. Finally, based on the Triad constraints, we develop a two-step algorithm to learn the causal structure corresponding to measurement models. Experimental results on both synthetic and real data demonstrate the effectiveness and reliability of our method.
Blockchain‐based security aspects in heterogeneous Internet‐of‐Things networks: A survey

Blockchain is a key technology that enables cryptocurrencies such as Bitcoin, Litecoin, etc. In recent years, researchers have ventured into tapping the potential of blockchain‐based ecosystems beyond cryptocurrencies. This paper explores the convergence of blockchain with the Internet of things (IoT) and its improved versions like software‐defined network (SDN)–based IoT, which are upcoming technologies in the area. This paper gives a brief description of the IoT and its challenges, especially the security aspects. It also discusses how blockchain concept can further resolve these challenges on the SDN‐based IoT platform. This paper first discussed the evolution of conventional IoT to the SDN‐based IoT, which can resolve many drawbacks of a conventional IoT system. Also, it is focused on how the concept of blockchain can be converged with SDN‐based IoT system to further improve its security aspects. Although the convergence of these different technologies has resulted into many advantages, many associated challenges still remain. This paper highlighted these challenges and opportunities to discover many other research issues that need to be resolved. The details of current literature on the same issue has also been highlighted in this paper.
Simulation-based comparison between two crane-bunk systems for loading work when considering energy-optimal motion planning

ABSTRACT Performing work for extended periods of time while using the lowest amount of resources is an important aspect for productivity in many industries. In forestry, the productivity of a forwarder is seen as the volume of material it can extract to a roadside landing in a certain amount of time, where the process of loading and unloading logs represents a large part of the work. During this process, the esnergy consumed by the machine is directly related to the speed of the crane. Thus, increasing productivity implies increasing the operating velocity of cranes. But according to current design of forestry cranes, this conversely leads to an undesired increase in consumption of resources (e.g. fuel). A second method is to alter the machine’s design, such as rotating the log bunk. This article considers both methods through a simulation-based comparison aiming to evaluate the energy consumption of two crane-bunk systems when loading. The first simulation system considers a forestry crane with a fixed log bunk (forwarder-like crane). The second simulation system takes into account a forestry crane and a rotating log bunk (harwarder-like crane). The analysis presented considers the fundamental mathematics required to analyze the dynamics of forestry cranes and the principles required to plan energy-optimal motions. The simulation results show that energy savings of 43% to 61% can be obtained by determining energy-optimal motions and using a harwarder-like crane architecture.
Parameter-free Sentence Embedding via Orthogonal Basis

We propose a simple and robust non-parameterized approach for building sentence representations. Inspired by the Gram-Schmidt Process in geometric theory, we build an orthogonal basis of the subspace spanned by a word and its surrounding context in a sentence. We model the semantic meaning of a word in a sentence based on two aspects. One is its relatedness to the word vector subspace already spanned by its contextual words. The other is the word’s novel semantic meaning which shall be introduced as a new basis vector perpendicular to this existing subspace. Following this motivation, we develop an innovative method based on orthogonal basis to combine pre-trained word embeddings into sentence representations. This approach requires zero parameters, along with efficient inference performance. We evaluate our approach on 11 downstream NLP tasks. Our model shows superior performance compared with non-parameterized alternatives and it is competitive to other approaches relying on either large amounts of labelled data or prolonged training time.
A Two-Step ADC With a Continuous-Time SAR-Based First Stage

This article presents a two-step analog-to-digital converter (ADC) that operates its first-stage successive approximation register (SAR) ADC in the continuous-time (CT) domain. It avoids the front-end sample-and-hold (S/H) circuit and its associated sampling noise. Hence, the proposed ADC allows the input capacitor size to be substantially reduced without incurring large sampling noise penalty. With input ac coupling, the first-stage CT-SAR can simultaneously perform input tracking and SAR quantization. Its conversion error is minimized by accelerating the SAR speed and providing redundancy. A floating-inverter-based (FIB) dynamic amplifier (DA) is used as the inter-stage amplifier and acts as a low-pass filter for the first-stage residue. To verify the proposed techniques, a 13-bit prototype ADC is built in 40-nm CMOS process. Its input capacitor is only 120 fF, which is over 20 times smaller than what would be needed in a classic Nyquist ADC with the S/H circuit. Operating at 2 MS/s, it achieves 72-dB signal-to-noise-and-distortion-ratio (SNDR) at the Nyquist rate while consuming only 25 $\mu \text{W}$ of power and 0.01 mm2 of area.
N-RPN: Hard Example Learning For Region Proposal Networks

The region proposal task is to generate a set of candidate regions that contain an object. In this task, it is most important to propose as many candidates of ground-truth as possible in a fixed number of proposals. In a typical image, however, there are too few hard negative examples compared to the vast number of easy negatives, so region proposal networks struggle to train on hard negatives. Because of this problem, networks tend to propose hard negatives as candidates, while failing to propose ground-truth candidates, which leads to poor performance. In this paper, we propose a Negative Region Proposal Network(nRPN) to improve Region Proposal Network(RPN). The nRPN learns from the RPN’s false positives and provide hard negative examples to the RPN. Our proposed nRPN leads to a reduction in false positives and better RPN performance. An RPN trained with an nRPN achieves performance improvements on the PASCAL VOC 2007 dataset.
Lexicon Based Emotion Analysis on Twitter Data

This paper presents a system that extracts information from automatically annotated tweets using well known existing opinion lexicons and supervised machine learning approach. In this paper, the sentiment features are primarily extracted from novel high-coverage tweet-specific sentiment lexicons. These lexicons are automatically generated from tweets with sentiment-word hashtags and from tweets with emoticons. The sentence-level or tweet level classification is done based on these word-level sentiment features by using Sequential Minimal Optimization (SMO) classifier. SemEval-2013 Twitter sentiment dataset is applied in this work. The ablation experiments show that this system gains in F-Score of up to 6.8 absolute percentage points.
Symbolic Nonlinear Analysis of Viscoelastic Pipes

In this study, a novel application domain of symbolic computation of nonlinear finite element method using a viscoelastic pipes has been presented. The problem of geometric nonlinearity due to the effect of high temperature, large displacement, small strain and moderate rotation of viscoelastic pipes was modeled and validated with numerical, symbolic and graphical computation in a unified manner called symbolic analysis. The current study has shown that the symbolic computation is effective and efficient, it saves computation time which can be seen in the symbolically integrated element-stiffness matrices in the nonlinear finite element method which by-passes time-consuming numerical quadrature operation, especially as the number of Gauss points increases. The symbolic computation also gives the analyst more visibility with respect to the solution method and the engineer can then more easily grasp the inter-relationship of the problem variables, recognize the simplification to be made and do a better and more accurate job. The computer codes of the finite element formulation used in this work was generated through the symbolic programming of the finite element computer code in AceGen and AceFEM computer program.
Information Constrained Control Analysis of Eye Gaze Distribution Under Workload

We describe a novel model of human eye gaze behavior under workload, derived from the basic principle of information constrained control. The model assumes two distributions over the visual field: A saliency distribution, which is nongoal oriented, and a reward task-related distribution. The eye gaze behavior is determined by the tradeoff between these two distributions, where the goal is to preserve the task-related constraints, while remaining as close as possible to the saliency distribution representing a comfort zone. Based on minimum Kullback–Liebler divergence principles, the model gives rise to a family of gaze distributions controlled by a single tradeoff parameter. The model was evaluated experimentally in a driving simulator that consisted of an immersive environment with clear tasks and accurate monitoring capabilities. The findings confirm the theoretical predictions with respect to the low rank manifold and order relations in the data. We show that the model can be used to visualize the unknown reward function associated with a task, and predict human workload based on gaze pattern.
Insect Classification Using Squeeze-and-Excitation and Attention Modules - a Benchmark Study

Insect recognition at the species level is an active research field with a variety of applications. With the advancement of convolutional neural networks an automatic fine-grained image classifier has displayed encouraging performance. Despite these recent advances, differentiating images at the species level is still a challenge. To address the problems arising from insect-specific peculiarities, this paper presents a novel network that consists of squeeze-and-excitation modules and attention modules, enabling the network to focus on more informative and differentiating features with a limited number of training iterations and a small dataset. The proposed model is trained on an insect dataset collected from Atlas of Living Australia. The results reveal that the integrated model achieves higher accuracy than several alternative methods on the introduced insect dataset.
User response to e-WOM in social networks: how to predict a content influence in Twitter

The purpose of this research is to find influential factors on electronic word-of-mouth effectiveness for e-retailers in Twitter social media, applying data mining and text mining techniques and through R programming language. The relationship between using hashtag, mention, media and link in the tweet content, length of the content, the time of being posted and the number of followers and followings with the influence of e-WOM is analysed. 48,129 tweets about two of the most famous American e-retailers, Amazon and eBay, are used as samples; results show a strong relationship between the number of followers, followings, the length of the content and the effectiveness of e-WOM and weaker relevance between having media and mention with e-WOM effectiveness on Twitter. Findings of this paper would help e-retailing marketers and managers to know their influential customers in social media channels for viral marketing purpose and advertising campaigns.
A Human-like View-invariant Representation of Faces in Deep Neural Networks Trained with Faces but not with Objects

: Face recognition depends on the generation of a view-invariant representation. Faces are known to engage specialized mechanisms and it is therefore of interest to reveal to what extent is specific experience with faces necessary for the development of this representation. This question is hard to study in humans, but can be studied with Deep Convolutional Neural Networks (DNNs) trained with faces or with objects. To examine whether a face-trained and an object-trained networks generate a human-like, view-invariant representation, we first examined the similarity between the representations of faces across different head views. We then examined whether the networks use the same view-invariant facial features that are used by humans for face recognition. Our findings show that a human-like view-invariant representation of faces emerges at higher layers of a face-trained DNN, but not the object-trained DNN. The representations of faces were similar at lower layers of the face-trained and object-trained networks. These findings may resemble the face and object pathways in the human brain that are similar in low-level areas and diverge at higher levels of the visual cortex. They further imply that invariant face recognition depends on experience with faces, during which the system learns to extract face-specific, invariant features.
An experimental platform for statistical fault diagnosis in propelled mechanical systems: an academic exercise

Statistical data analysis for fault diagnosis in mechanical systems is a fundamental tool, for instance, in applied mechanical engineering. In order to capture a feasible data set, a well designed electronic instrumentation and excitation system signal stages are mandatory. Hence, one objective of this paper is to develop a low cost vibration sensor based on an inductive LC-tank oscillator (a resonant inductive-capacitive electronic circuit carefully designed to produce an harmonic electrical signal), and then to tune an effective excitation system signal to our experimental platform. This platform uses a propelled drone motor mounted on a beam structure to emulate a propelled rotating machine. Essentially, two data set were acquired. One for the healthy behaviour of the developed system, and the other for a programmed faulty scenario. This defective case was realized by introducing a small mechanical fault in one blade extreme of the mechanical propelled system. To note, this faulty scenario is almost impossible to deduce by just seen the raw data. The other objective of this paper is to analyze the obtained data sets by utilizing a statistical data analysis tool. Then, by employing box-plot diagrams, the healthy and faulty cases become evidenced. Finally, and due to we are proposing a low-cost academic experimental platform for fault diagnosis based on data analysis, our platform’s toll was around 120 euros. Hence, this platform results applicable to teach data analysis from dynamical systems.
Efficient and Accurate Face Shape Reconstruction by Fusion of Multiple Landmark Databases

We propose an efficient and accurate regression-based 3D face shape reconstruction method. We use an encoder based on MobileNet to estimate parameters including face pose and coefficients of a parametric face model from a single face image. The encoder is trained only by 2D landmarks. Faces can be reconstructed by these parameters. Three contributions of our method are: 1) we propose a databases fusion method to train our network which can easily utilize multiple 2D landmark databases which have different landmark numbers and positions; 2) with the fusion method, we propose a simple MobileNet based network which is efficient, accurate and robust for face reconstruction even without complex training strategies; 3) we add an additional deformation field for shape correction to further improve our network’s performance. Experiments demonstrate our method can bring about great performance improvement on most test databases and also compare favorably to some state-of-the-art methods in performance and speed.
odMLtables: A User-Friendly Approach for Managing Metadata of Neurophysiological Experiments

An essential aspect of scientific reproducibility is a coherent and complete acquisition of metadata along with the actual data of an experiment. The high degree of complexity and heterogeneity of neuroscience experiments requires a rigorous management of the associated metadata. The odML framework represents a solution to organize and store complex metadata digitally in a hierarchical format that is both human and machine readable. However, this hierarchical representation of metadata is difficult to handle when metadata entries need to be collected and edited manually during the daily routines of a laboratory. With odMLtables, we present an open-source software solution that enables users to collect, manipulate, visualize, and store metadata in tabular representations (in xls or csv format) by providing functionality to convert these tabular collections to the hierarchically structured metadata format odML, and to either extract or merge subsets of a complex metadata collection. With this, odMLtables bridges the gap between handling metadata in an intuitive way that integrates well with daily lab routines and commonly used software products on the one hand, and the implementation of a complete, well-defined metadata collection for the experiment in a standardized format on the other hand. We demonstrate usage scenarios of the odMLtables tools in common lab routines in the context of metadata acquisition and management, and show how the tool can assist in exploring published datasets that provide metadata in the odML format.
Multi-Task Stance Detection with Sentiment and Stance Lexicons

Stance detection aims to detect whether the opinion holder is in support of or against a given target. Recent works show improvements in stance detection by using either the attention mechanism or sentiment information. In this paper, we propose a multi-task framework that incorporates target-specific attention mechanism and at the same time takes sentiment classification as an auxiliary task. Moreover, we used a sentiment lexicon and constructed a stance lexicon to provide guidance for the attention layer. Experimental results show that the proposed model significantly outperforms state-of-the-art deep learning methods on the SemEval-2016 dataset.
Evidence for a Hierarchy within Breast Cancer Stem Cells: Relevance to Metastasis and Therapy

of a dissertation at the University of Miami. Dissertation supervised by Joyce M. Slingerland, M.D., Ph.D. No. of pages in text. (92) Accumulating evidence over the past decade has established the existence of a cancer stem cell (CSC) subpopulation within breast cancers that is responsible for tumor initiation, progression and drug resistance. However, few models have characterized phenotypically distinct subsets of cells within CSC populations that may underlie the heterogeneity of tumor responses to standard chemo and radiation therapies. Similarly, despite the widely accepted postulate that CSCs generate metastasis, few studies have demonstrated experimentally the existence of heterogeneity within the CSCs with regard to metastatic potential. The work in this thesis was undertaken to identify whether such discrete subsets exist among CSCs of the most deadly form of breast cancer: that lacking estrogen and progesterone receptors and HER2 amplification (so called triple negative— hereafter TNBC). We postulated that, as for normal stem cells, primary TNBC-derived cultures and immortal lines would have a CSC hierarchy with precursor/progeny populations that differ in molecular pathways conferring self-renewal, tumorigenicity and metastatic potential. We show that CSC-enriched CD44CD24 breast cancer cells comprise a functional hierarchy: a minor CD44CD24 subpopulation generates CD44CD24 progeny with reduced self-renewal and tumorigenicity. In both triple negative breast cancer (TNBC) lines and primary dissociated tumor (DT) cultures, CD44CD24 generate more tumor spheres, while CD44CD24 -generated spheres decline with serial passage. Tumor-initiating CSCs were more frequent in CD44CD24: fewer cells yielded orthotopic xenograft tumors with reduced latency. Furthermore, metastasis arose exclusively from CD44CD24 cells which preferentially express embryonic stem cell gene profiles and metastatic gene signatures. CD44CD24 but not CD44CD24 cells express Notch-1 intracellular domain (N1-ICD) and a Notch-activated gene profile. N1-ICD transactivates SOX2 mediating Sox2-dependent increases in the percentaged of ALDH1+, CD44CD24 and sphere forming cells. Υ -Secretase Inhibitors (GSI) that block Notch pathway activation notably reduced serial sphere formation and xenograft growth from CD44CD24 cells while CD44CD24 showed no response. While Notch family members are implicated in stem cell self-renewal many cancers, the existence of stem cell hierarchies such as that observed herein could limit potential GSI efficacy.