Miracle-dz/UniFAR-FTUs huggingface dataset:
2-tuple linguistic Muirhead mean operators for multiple attribute group decision making and its application to supplier selection

Purpose – The purpose of this paper is to develop some 2-tuple linguistic aggregation operators based on Muirhead mean (MM), which is combined with multiple attribute group decision making (MAGDM) and applied the proposed MAGDM model for supplier selection under 2-tuple linguistic environment. Design/methodology/approach – The supplier selection problem can be regarded as a typical MAGDM problem, in which the decision information should be aggregated. In this paper, the authors investigate the MAGDM problems with 2-tuple linguistic information based on traditional MM operator. The MM operator is a well-known mean type aggregation operator, which has some particular advantages for aggregating multi-dimension arguments. The prominent characteristic of the MM operator is that it can capture the whole interrelationship among the multi-input arguments. Motivated by this idea, in this paper, the authors develop the 2-tuple linguistic Muirhead mean (2TLMM) operator and the 2-tuple linguistic dual Muirhead mean (.
Growth hormone‐releasing hormone antagonists reduce prostatic enlargement and inflammation in carrageenan‐induced chronic prostatitis

Inflammation plays a key role in the etiology of benign prostatic hyperplasia (BPH) through multiple pathways involving the stimulation of proliferation by cytokines and growth factors as well as the induction of the focal occurrence of epithelial‐to‐mesenchymal transition (EMT). We have previously reported that GHRH acts as a prostatic growth factor in experimental BPH and in autoimmune prostatitis models and its blockade with GHRH antagonists offer therapeutic approaches for these conditions. Our current study was aimed at the investigation of the beneficial effects of GHRH antagonists in λ‐carrageenan‐induced chronic prostatitis and at probing the downstream molecular pathways that are implicated in GHRH signaling.
Delivery-Aware Cooperative Joint Multi-Bitrate Video Caching and Transcoding in 5G

This paper proposes a two-phase resource allocation framework (RAF) for a parallel cooperative joint multi-bitrate video caching and transcoding (CVCT) in heterogeneous virtualized mobile-edge computing (HV-MEC) networks. In the cache placement phase, we propose delivery-aware cache placement strategies (DACPSs) based on the available video popularity distribution (VPD) and channel distribution information (CDI) to exploit the flexible delivery opportunities, i.e., video transmission and transcoding capabilities. Then, for the delivery phase, we propose a delivery policy for given caching status, instantaneous requests of users, and channel state information (CSI). The optimization problems corresponding to both phases aim to maximize the total revenue of slices subject to the quality of services contracted between slices and end-users and the system constraints based on their own assumptions. Both problems are non-convex and suffer from their high-computational complexities. For each phase, we show how these two problems can be solved efficiently. We also design a low-complexity RAF (LC-RAF) in which the complexity of the delivery algorithm is significantly reduced. Extensive numerical assessments demonstrate up to $30\%$ performance improvement of our proposed DACPSs over traditional approaches.
A Hierarchical Structured Self-Attentive Model for Extractive Document Summarization (HSSAS)

The recent advance in neural network architecture and training algorithms has shown the effectiveness of representation learning. The neural-network-based models generate better representation than the traditional ones. They have the ability to automatically learn the distributed representation for sentences and documents. To this end, we proposed a novel model that addresses several issues that are not adequately modeled by the previously proposed models, such as the memory problem and incorporating the knowledge of document structure. Our model uses a hierarchical structured self-attention mechanism to create the sentence and document embeddings. This architecture mirrors the hierarchical structure of the document and in turn enables us to obtain better feature representation. The attention mechanism provides extra source of information to guide the summary extraction. The new model treated the summarization task as a classification problem in which the model computes the respective probabilities of sentence–summary membership. The model predictions are broken up by several features such as information content, salience, novelty, and positional representation. The proposed model was evaluated on two well-known datasets, the CNN/Daily Mail and DUC 2002. The experimental results show that our model outperforms the current extractive state of the art by a considerable margin.
The Diffusion of Passive Tracers in Laminar Shear Flow

A simple method to experimentally observe and measure the dispersion of a passive tracer in a laminar fluid flow is described. The method consists of first injecting fluorescent dye directly into a pipe filled with distilled water and allowing it to diffuse across the cross-section of the pipe to obtain a uniformly distributed initial condition. Following this period, the laminar flow is activated with a programmable syringe pump to observe the competition of advection and diffusion of the tracer through the pipe. Asymmetries in the tracer distribution are studied and correlations between the pipe cross-section and the shape of the distribution is shown: thin channels (aspect ratio << 1) produce tracers arriving with sharp fronts and tapering tails (front-loaded distributions), while thick channels (aspect ratio ~1) present the opposite behavior (back-loaded distributions). The experimental procedure is applied to capillary tubes of various geometries and is particularly relevant to microfluidic applications by dynamical similarity.
Software Visual Specification for Requirement Specification Validation

Informal specifications are commonly adopted for requirements engineering (RE), especially for customer-oriented system development projects, but their validity with respect to the user's requirements is usually difficult to perform. The common reason for the difficulty is that the specifications are often misinterpreted by the stakeholders due to the ambiguity of the natural language. This paper describes a procedure in transforming the informal specification into specification visual specification. The transformation procedure is developed based on the extended version of software visualization taxonomy. The underlying software visualization specification principle is called Requirement Engineering (specification) for Human Factor validation (REsHFv). The framework is devoted to human factor involvement in validating the informal specification using software visualization as interaction medium.
Using SVM and Stopword removal method in Microblogging Classroom

In recent year, Microblogging is a popular technology in social networking applications. All users publish online short text messages with the help of micro blogging which size less than 200 characters. It can be use via web and instant messaging clients etc.It can be an effective tool in the classroom and education community.Microblogging can be categorization for two types such as relevant and irrelevant questions. In relevant questions consistof questions that the teacher wants toaddress in the class. In a question text, empirical results and analysis show that personalizationbetween each other. With the help of question text Microblogging leads to better categorization accuracy.It is also important to utilize the correlation between questions, lecture materials, correlation between questions asked in a lecture. The experimental results likewise demonstrate that the end of stop words which prompts better relationship estimation in the middle of inquiries and arrangement exactness. Then again, fusing understudies' votes on the inquiries that don't enhance classification exactness, despite the fact that a comparable highlight has been indicated to be successful in group inquiry noting situations for evaluating question quality.
SNU_IDS at SemEval-2018 Task 12: Sentence Encoder with Contextualized Vectors for Argument Reasoning Comprehension

We present a novel neural architecture for the Argument Reasoning Comprehension task of SemEval 2018. It is a simple neural network consisting of three parts, collectively judging whether the logic built on a set of given sentences (a claim, reason, and warrant) is plausible or not. The model utilizes contextualized word vectors pre-trained on large machine translation (MT) datasets as a form of transfer learning, which can help to mitigate the lack of training data. Quantitative analysis shows that simply leveraging LSTMs trained on MT datasets outperforms several baselines and non-transferred models, achieving accuracies of about 70% on the development set and about 60% on the test set.
Perspectives for applying the additional study methods for diagnostics optimization of postoperative hypertrophic scars of the head and neck.

OBJECTIVE Introduction: The issue of improving the quality of diagnosis, prevention and treatment of patients with pathological scarring of the head and neck is being actively developed throughout the world. The aim of the research is to improve diagnostics of hypertrophic scars of the head and neck through the establishment of structural features of scar-modified tissues using RGB-system and ultrasound examination. PATIENTS AND METHODS Materials and methods: In this study, 60 patients with hypertrophic scars of the head and neck were examined. Under our supervision, there were patients in whom the type of scar was previously confirmed clinically and morphologically. RESULTS Results: Based on the optical analysis of the obtained images, it can be concluded that hypertrophic scars are well visualized and are different from intact skin. We created a standardized table of numerical values for distribution of color components in the RGB system. Analyzing the data as for the numerical differences in terms of echogenicity of hypertrophic scars, we found that the echogenicity changes in the area of medial and lateral edges of the scar had significant differences. CONCLUSION Conclusions: A comprehensive examination of patients with hypertrophic scarring of the head and neck, which implies the mandatory use of RGB-visualization and ultrasound examination, enables us to differentiate the type of scar tissue. The described comprehensive approach to examination of patients with scarring will provide the optimal choice of methods for treatment.
A REVIEW PAPER ON BIG DATA ANALYTICS

: We live in on-demand world with vast majority of data. People and devices are constantly generating data, while streaming a video, active in social media, playing games, search any location using GPS. This data increase day by day from many resources, various types of techniques and technologies. The data is categories as “Big Data”. Big Data is huge in Variety, Velocity and Sheer volume. It is structured and unstructured data and heterogeneous in nature. The goal of Big Data analysis is to extract useful values, suggest conclusions and/or support decision making. In this topic, we provide an extensive survey of big data analytics research, while highlighting the specific concern in big data world. According to Application evolution, we discuss six types of big data application such as structured data analytics, Text analytics, Web analytics, Multimedia analytics, and Mobile analytics. We illustrate the techniques of analyzing the big data such as A/B testing, classification, crowdsourcing, and data mining.
The Graphene Sensor Technology

Graphene has emerged as one of the strongest candidates for post-silicon technologies. One of the most important applications of graphene in the foreseeable future is sensing of particles of gas molecules, bio-molecules or different chemicals or sensing of radiation of particles like alpha, gamma or cosmic particles. Several unique properties of graphene such as its extremely small thickness, very low mass, large surface to volume ratio, very high absorption coefficient, high mobility of charge carriers, high mechanical strength and high Young’s modulus make it exceptionally suitable for making sensors. Graphene based materials have considerable attention for the fabrication of non-enzymatic sensors due to their low cost, high catalytic ability and good stability use in
Memo No . 85 06 / 2018 Deep Regression Forests for Age Estimation

Age estimation from facial images is typically cast as a nonlinear regression problem. The main challenge of this problem is the facial feature space w.r. t. ages is inhomogeneous, due to the large variation in facial appearance across different persons of the same age and the non-stationary property of aging patterns. In this paper, we propose Deep Regression Forests (DRFs), an end-to-end model, for age estimation. DRFs connect the split nodes to a fully connected layer of a convolutional neural network (CNN) and deal with inhomogeneous data by jointly learning input-dependant data partitions at the split nodes and data abstractions at the leaf nodes. This joint learning follows an alternating strategy: First, by fixing the leaf nodes, the split nodes as well as the CNN parameters are optimized by Back-propagation; Then, by fixing the split nodes, the leaf nodes are optimized by iterating a step-size free update rule derived from Variational Bounding. We verify the proposed DRFs on three standard age estimation benchmarks and achieve state-of-the-art results on all of them. This material is based upon work supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF-1231216. Deep Regression Forests for Age Estimation Wei Shen, Yilu Guo, Yan Wang, Kai Zhao, Bo Wang, Alan Yuille 1 Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai Institute for Advanced Communication and Data Science, School of Communication and Information Engineering, Shanghai University 2 Department of Computer Science, Johns Hopkins University 3 College of Computer and Control Engineering, Nankai University 4 Hikvision Research {shenwei1231,gyl.luan0,wyanny.9,zhaok1206,wangbo.yunze,alan.l. yuille}@gmail.com
Randomized Smoothing SVRG for Large-scale Nonsmooth Convex Optimization

In this paper, we consider the problem of minimizing the average of a large number of nonsmooth and convex functions. Such problems often arise in typical machine learning problems as empirical risk minimization, but are computationally very challenging. We develop and analyze a new algorithm that achieves robust linear convergence rate, and both its time complexity and gradient complexity are superior than state-of-art nonsmooth algorithms and subgradient-based schemes. Besides, our algorithm works without any extra error bound conditions on the objective function as well as the common strongly-convex condition. We show that our algorithm has wide applications in optimization and machine learning problems, and demonstrate experimentally that it performs well on a large-scale ranking problem.
Protein Profiles and Dehydrin Accumulation in Some Soybean Varieties (Glycine max L. Merr) in Drought Stress Conditions

Drought is one of environmental stresses which the most limiting to plant growth and productivity. Drought stress led to a series of changes including biochemical changes like accumulation of osmolit and specific proteins involved in stress tolerance. One of the proteins that play a role in the mechanism of drought resistance is dehydrin protein. This study aimed to identify the protein profiles and dehydrin accumulation in 7 varieties of local Indonesian soybeans: Tanggamus, Nanti, Seulawah and Tidar (tolerant), Wilis and Burangrang (moderate) and Detam-1 (drought stress sensitive). Plants were treated with drought stress by adjusting soil water content to 25% below field capacity and compared with plants which were grown on normal condition as control plants. The results of SDS-PAGE electrophoresis showed a new protein with the molecular weight of 13 and 52 kDa were induced in Tanggamus, Nanti, Seulawah and Tidar varieties. Western blotting analysis for dehydrin showed that the quantity of the protein in the leaves of all varieties except Tanggamus decreased in drought stress conditions. The quantity of dehydrin protein in tolerant varieties higher than the protein quantity in both moderate varieties and drought sensitive.
Proposal of a Visualizing Method of Data Transitions to Support Debugging for Java Programs

It takes much time to find the cause of a bug in debugging of programs. Finding the cause of a bug needs to comprehend a flow and data transitions in executing programs. It is difficult to grasp behavior in executing the programs whose behavior is unexpected by a bug. We propose a visualizing method of data transitions to support debugging for Java programs in order to improve efficiency of debugging by supporting to find the cause of a bug. We have implemented TVIS in order to confirm efficiency of the proposed method. The data transitions diagram is the most characteristic function of TVIS which shows the data transitions in executing programs as a table. It can show visually abnormal behavior: no data renewed at all, data abnormally renewed, and so on. Because abnormal behavior is detected in the data transitions diagram at first glance, it is useful for programmers in finding the cause of a bug. This paper shows that the method can support to find the cause of a bug.
Evaluation of an intervention programme addressing ability to perform activities of daily living among persons with chronic conditions: study protocol for a feasibility trial (ABLE)

Introduction The number of persons living with a chronic condition is increasing worldwide. Conditions are considered chronic when lasting 1 year or more and requiring ongoing medical attention and/or limiting activities of daily living (ADL). Besides medical treatment, physical exercise to improve body functions is recommended and prescribed. However, improvements in body functions do not necessarily improve ability to perform ADL. Thus, it is necessary to develop interventions aiming directly at enhancing ADL ability. As a part of the research programme ‘A Better Everyday Life’, the first version of the ABLE intervention programme was developed. Methods and analysis This feasibility study examine the perceived value and acceptability of the ABLE programme by evaluating the fidelity, reach, dose and potential outcomes using a pretest and post-test design involving 30 persons living with chronic conditions. Qualitative interviews among occupational therapists delivering and participants receiving the ABLE programme will be conducted to explore aspects affecting the intervention. Ethics and dissemination The results will form the base for refinement of the ABLE programme and planning of a large-scale randomised controlled trial investigating the effect of the programme on self-reported and observed ADL ability. Dissemination will include peer-reviewed publications and presentations at national and international conferences. Protocol version 7 November 2017: v ersion 1.0. 19 February 2018: v ersion 2.0. Trial registration number NCT03335709; Pre-results.
Transduction with Matrix Completion Using Smoothed Rank Function

In this paper, we propose two new algorithms for transduction with Matrix Completion (MC) problem. The joint MC and prediction tasks are addressed simultaneously to enhance the accuracy, i.e., the label matrix is concatenated to the data matrix forming a stacked matrix. Assuming the data matrix is of low rank, we propose new recommendation methods by posing the problem as a constrained minimization of the Smoothed Rank Function (SRF). We provide convergence analysis for the proposed algorithms. The simulations are conducted on real datasets in two different scenarios of randomly missing pattern with and without block loss. The results confirm that the accuracy of our proposed methods outperforms those of state-of-the-art methods even up to 10% in low observation rates for the scenario without block loss. Our accuracy in the latter scenario, is comparable to state-of-the-art methods while the complexity of the proposed algorithms are reduced up to 4 times.
Frustum PointNets for 3D Object Detection from RGB-D Data

In this work, we study 3D object detection from RGB-D data in both indoor and outdoor scenes. While previous methods focus on images or 3D voxels, often obscuring natural 3D patterns and invariances of 3D data, we directly operate on raw point clouds by popping up RGB-D scans. However, a key challenge of this approach is how to efﬁciently localize objects in point clouds of large-scale scenes (region proposal). Instead of solely relying on 3D proposals, our method leverages both mature 2D object detectors and advanced 3D deep learning for object localization, achieving efﬁciency as well as high recall for even small objects. Beneﬁted from learning directly in raw point clouds, our method is also able to precisely estimate 3D bounding boxes even under strong occlusion or with very sparse points. Evaluated on KITTI and SUN RGB-D 3D detection benchmarks, our method outperforms the state of the art by remarkable margins while having real-time capability.
Efficient Signature Generation for Classifying Cross-Architecture IoT Malware

Internet-of-Things IoT devices are increasingly targeted Uy adversaries due to their unique characteristics such as constant online connection, lack of protection, and full integration in people’s daily life. As attackers shift their targets towards IoT devices, malware has been developed to compromise IoT devices equipped with different CPU architectures. While malware detection has been a well-studied area for desktop PCs, heterogeneous processor architecture in IoT devices brings in unique challenges. Existing approaches utilize static or dynamic binary analysis for identifying malware characteristics, Uut they all fall short when dealing with IoT malware compiled for different architectures. In this paper, we propose an efficient signature generation method for IoT malware, which generates distinguishable signatures based on high-level structural, statistical and string feature vectors, as high-level features are more robust against code variations across different architectures. The generated signatures for each malware family can be used for developing lightweight malware detection tools to secure IoT devices. Extensive experiments with two datasets of 5,150 recent IoT malware samples show that our scheme can achieve 95.5% detection rate with 0% false positive rate. Moreover, the proposed scheme can achieve 85.2% detection rate in detecting novel IoT malware.
DVAE#: Discrete Variational Autoencoders with Relaxed Boltzmann Priors

Boltzmann machines are powerful distributions that have been shown to be an effective prior over binary latent variables in variational autoencoders (VAEs). However, previous methods for training discrete VAEs have used the evidence lower bound and not the tighter importance-weighted bound. We propose two approaches for relaxing Boltzmann machines to continuous distributions that permit training with importance-weighted bounds. These relaxations are based on generalized overlapping transformations and the Gaussian integral trick. Experiments on the MNIST and OMNIGLOT datasets show that these relaxations outperform previous discrete VAEs with Boltzmann priors. An implementation which reproduces these results is available at this https URL .