Reviewerly/exHarmony huggingface dataset:
Effect of knowledge sources on firm-level innovation in Tanzania

ABSTRACTThis paper analyses the impact of different sources of knowledge on product and process innovation in Tanzania using firm-level data. We specifically analyse the separate impacts of internal knowledge, external knowledge and the combined impact of both types of knowledge on firms’ product and process innovation decisions. The analysis reveals that the purchase of machinery, equipment or software, year of firm establishment, the sector a firm belongs to, and internal research and development impact on product and process innovation. Product innovation is more constrained by a lack of external knowledge than process innovation. External research and development do not affect product or process innovation and the joint effect of internal and external knowledge on product innovation exceeds the separate effects of internal and external knowledge on innovation. Furthermore, external knowledge acquisition and firm spending on internal research and development affect product and process innovation more e...
Generating Accurate Virtual Examples for Lifelong Machine Learning

Lifelong machine learning (LML) is an area of machine learning research concerned with human-like persistent and cumulative nature of learning. LML system's objective is consolidating new information into an existing machine learning model without catastrophically disrupting the prior information. Our research addresses this LML retention problem for creating a knowledge consolidation network through task rehearsal without retaining the prior task's training examples. We discovered that the training data reconstruction error from a trained Restricted Boltzmann Machine can be successfully used to generate accurate virtual examples from the reconstructed set of a uniform random set of examples given to the trained model. We also defined a measure for comparing the probability distributions of two datasets given to a trained network model based on their reconstruction mean square errors.
Breast Cancer Diagnosis from Perspective of Class Imbalance

Introduction: Breast cancer is the second cause of mortality among women. Early detection is the only rescue to reduce the risk of breast cancer mortality. Traditional methods cannot effectively diagnose tumor since they are based on the assumption of well-balanced dataset.. However, a hybrid method can help to alleviate the two-class imbalance problem existing in the diagnosis of breast cancer and establish a more accurate diagnosis. Material and Methods: The proposed hybrid approach was based on improved Laplacian score (LS) andK-nearest neighbor (KNN) algorithms called LS-KNN. An improved LS algorithm was used for obtaining the optimal feature subset. The KNN with automatic K was utilized for classifying the data which guaranteed the effectiveness of the proposed method by reducing the computational effort and making the classification more faster. The effectiveness of LS-KNN was also examined on two biased-representative breast cancer datasets using classification accuracy, sensitivity, specificity, G-mean, and Matthews correlation coefficient. Results: Applying the proposed algorithm on two breast cancer datasets indicated that the efficiency of the new method was higher than the previously introduced methods. The obtained values of accuracy, sensitivity, specificity, G-mean, and Matthews correlation coefficient were 99.27%, 99.12%, 99.51%, 99.42%, respectively. Conclusion: Experimental results showed that the proposed approach worked well with breast cancer datasets and could be a good alternative to the well-known machine learning methods
PENERAPAN METODE INVESTIGASI KELOMPOK DALAM PEMBELAJARAN MENULIS TEKS EKSPLANASI MELALUI PENELITIAN TINDAKAN KELAS

&lt;em&gt;The purpose of this study isto increase motivation and ability writetext&lt;strong&gt; &lt;/strong&gt;explanation on student class XI A P 1 SMK N 1 Surakarta with apply method group investigation. This is classrrom action research which carried out in two cycles, each cycle consisting of planning, action, observation, and reflection. The results showed that through the application of methods group investigationcan increase motivation and potentiality write explanatory texts in class XI A P 1 SMK N 1 Surakarta from action practice to the first cycle and from the first cycle to the second cycle. Motivation students and in ability to write explanation text increase although not optimal yet there are still some deficiencies both from the teacher and student factors and need to be improved by the second cycle. The conclusions are: (1) implementation method group investigation enhancing motivation on student, (2) application of group investigation methods enhancing the ability to write explanatory text on students with concrete steps taken.&lt;/em&gt;
Real-time Rendering Technology of Large-scale Underwater Scene on Web Terminal

Zhang, Z.; Liu, Y.; Yang, P.; Ye, W.; Weng, S.; Liu, T., and Li, Y., 2018. Real-time rendering technology of large-scale underwater scene on web terminal. In: Liu, Z.L. and Mi, C. (eds.), Advances in Sustainable Port and Ocean Engineering. Journal of Coastal Research, Special Issue No. 83, pp. 490–494. Coconut Creek (Florida), ISSN 0749-0208.With the continuous development of computer software and hardware technology, three-dimensional graphics rendering technology has gradually become one of the main research contents of computer graphics. It plays a great role in virtual reality, digital city, film and television advertising, computer aided design and scientific computing visualization. The computer simulation of natural phenomena is the key technology in the rendering of three-dimensional graphics. Natural scenery has the characteristics of complex form, random change and so on. It is difficult to express and study with a definite mathematical model. Compared with natural phenomena such as fire, cloud, mountain and rain, underwater scenes are more challenging because of their complex motion rules and optical characteristics. Therefore, this study analyzedthe research status in China and abroad, improved and innovated the underwater scene rendering technology and large-scale underwater scene management technologybased on the research of underwater scene grid algorithm, which completedthe large-scale underwater real-time rendering and the optimization algorithm of underwater scene management for underwater scene geometry modeling. The simulation experiment is used to detect the rendering effect, the experimental structure proves that the method has strong practicability and can effectively improve the accuracy and authenticity of underwater scene reduction.
Advances in data representation and learning for pattern analysis

Collaborative representation (CR) is one of the well-known representation methods and has been widely used in computer vision and pattern recognition. The collaborative representation-based classification (CRC) and its extension called the probabilistic collaborative representation-based classification (PCRC) have obtained promising performance in image classification. However, the representation fidelity is usually measured by the ℓ2-norm , which is not robust to outliers. Moreover, CRC and PCRC only consider the global distribution of data and ignore the locality of data. To overcome those problems, in this paper, we propose the residual-based extensions and the weighted version of CRC and PCRC. Specifically, for the residual-based extensions of CRC and PCRC, we measure the representation fidelity by jointing ℓ1-norm and the ℓ2-norm of coding residuals. For the weighted extensions of CRC and PCRC with different coding residuals, we constrained the representation coefficients with locality of data. To verify the effectiveness of the proposed methods, we conduct extensive experiments on six popular face databases and three image databases. Experimental results have demonstrated that the ℓ1-norm of coding residual, jointing both the ℓ1-norm and the ℓ2-norm of coding residuals and the locality constraint of representation coefficients can enhance pattern discrimination effectively.
Investigation of Fuzzy Inductive Modeling Method in Forecasting Problems

This paper is devoted to the investigation and application of fuzzy inductive modeling method in problems of forecasting in the financial sphere. The generalization of GMDH in case of uncertainty - new method fuzzy GMDH is described which enables to construct fuzzy models almost automatically. The algorithm of fuzzy GMDH is considered. The problem of adaptation of fuzzy models obtained by FGMDH is considered and the corresponding adaptation algorithm is described. The experimental investigations of the suggested FGMDH were carried out.
Architecture and Reference Terminology for Remote Attestation Procedures

Remote ATtestation ProcedureS (RATS) architecture facilitates theattestation of device characteristics that, in general, are based onspecific trustworthiness qualities intrinsic to a device or service.It includes trusted computing functionality provided by devicehardware and software that allows trustworthiness qualities to beasserted and verified as part of, or pre-requisite to, the device'snormal operation. The RATS architecture maps corresponding attestationfunctions and capabilities to specific RATS Roles. The goal is toenable an appropriate conveyance of evidence about devicetrustworthiness via network protocols. RATS Roles provide the endpointcontext for understanding the various interaction semantics of theattestation lifecycle. The RATS architecture provides the buildingblock concepts, semantics, syntax and framework for interoperableattestation while remaining hardware-agnostic. This flexibility isintended to address a significant variety of use-cases and scenariosinvolving interoperable attestation. Example usages include, but arenot limited to: financial transactions, voting machines, criticalsafety systems, network equipment health, or trustworthy end-userdevice management. Existing industry attestation efforts may behelpful toward informing RATS architecture. Such as: Remote IntegrityVERification (RIVER), the creation of Entity Attestation Tokens (EAT),software integrity Measurement And ATtestation (MAAT)
Cyclic codes over R3,m

Let [Formula: see text] be the ring [Formula: see text]. We study cyclic codes of arbitrary length [Formula: see text] over the ring [Formula: see text] and obtain a complete characterization for them by presenting a unique set of generators for these codes. For example, we try to determine all distinct cyclic codes of length [Formula: see text] over the ring [Formula: see text] by a practical algorithm.
Effectively Constructible Fixed Points in Sacchetti’s Modal Logics of Provability

We give a purely syntactical proof of the fixed point theorem for Sacchetti’s modal logics K+□(□np→p)→□p (n≥2) of provability. From our proof, an effective procedure for constructing fixed points in these logics is obtained. We also show the existence of simple fixed points for particular modal formulas.
KAJIAN FRAKTAL i-FIBONACCI WORD GENERALISASI GANJIL DENGAN MENGGUNAKAN L-SYSTEM

The i-Fibonacci Words are words over {0,1}. The i-Fibonacci Word can be associated with a fractal curve by using odd-even drawing rule and L-System methods, then also known as an i-Fibonacci Word fractal. L-System is one of methods that is used to create objects with repetitive self-similiarity. Framework of L-System consists of axiom and rules. L-System is a parallel rewriting system with existing rules. The purpose of this research is to look for the LSystem rules of i-Fibonacci Word special for odd i, then look how its characteristics. The LSystem rules for i-Fibonacci Word odd i are divided into two types, the rules for i=1 and the others odd i. The characteristic of i-Fibonacci Word fractal is the more generation and i value of fractal, then the more segments and archs of fractal curve. Next, the words of i-Fibonacci Word fractal segments number is a subwords of the i-Fibonacci Word digit numbers. It is also known that the fractal curve will be stretched as the decreased angle.&#x0D; Keywords: Fractal, i-Fibonacci Word, L-System
What and Where the Themes Dominate in Image

The image captioning is to describe an image with natural language as human, which has benefited from the advances in deep neural network and achieved substantial progress in performance. However, the perspective of human description to scene has not been fully considered in this task recently. Actually, the human description to scene is tightly related to the endogenous knowledge and the exogenous salient objects simultaneously, which implies that the content in the description is confined to the known salient objects. Inspired by this observation, this paper proposes a novel framework, which explicitly applies the known salient objects in image captioning. Under this framework, the known salient objects are served as the themes to guide the description generation. According to the property of the known salient object, a theme is composed of two components: its endogenous concept (what) and the exogenous spatial attention feature (where). Specifically, the prediction of each word is dominated by the concept and spatial attention feature of the corresponding theme in the process of caption prediction. Moreover, we introduce a novel learning method of Distinctive Learning (DL) to get more specificity of generated captions like human descriptions. It formulates two constraints in the theme learning process to encourage distinctiveness between different images. Particularly, reinforcement learning is introduced into the framework to address the exposure bias problem between the training and the testing modes. Extensive experiments on the COCO and Flickr30K datasets achieve superior results when compared with the state-of-the-art methods.
Why say it that way?: evasive answers and politeness theory

Abstract Examples of evasive answers frequently appear in discussions of non-literal meaning comprehension. A considerable amount of work on this topic has focused on how this kind of non-literal meaning is generated. Of the researchers who have dealt with speakers’ motives for using evasive answers, and how hearers’ awareness of these motives affects their interpretations, the majority have focused on evasive answers that are not intended to be recognized as such&amp;#x00A0;– in Gricean terms, those that violate the Maxim of Relation. Comparatively little research has dealt with answers that are blatant in their failure to answer the question&amp;#x00A0;– that is, those that flout Relation. This paper proposes that the majority of answers in the latter category can be understood using Brown and Levinson’s (1978, 1987) model of politeness&amp;#x00A0;– in particular, that evasive answers are motivated by considerations for both speaker and hearer’s positive face-wants. Evasive answers are defined according to Roberts’ (2012) model of “the question under discussion” and characterized in terms of violating, infringing, or flouting Grice’s (1975) Maxim of Relation. Various contexts in which the latter category of evasive answers occur are identified and discussed with reference to their role in avoiding face-threatening acts. Potential exceptions to the proposition that blatantly evasive answers can be explained using Brown and Levinson’s (1978, 1987) model of politeness are identified, and problems with treating violating, infringing, and flouting as clearly distinct categories are discussed.
Upsweep Pole Top Bracket with Single 6 Foot Arm

Upsweep pole top bracket with single 6 ft arm. Fits 2-3/8 tenon. As low as $227.20. Item #: BRU61
A dynamic modelling framework for human hand gesture task recognition

Gesture recognition and hand motion tracking are important tasks in advanced gesture based interaction systems. In this paper, we propose to apply a sliding windows filtering approach to sample the incoming streams of data from data gloves and a decision tree model to recognize the gestures in real time for a manual grafting operation of a vegetable seedling propagation facility. The sequence of these recognized gestures defines the tasks that are taking place, which helps to evaluate individuals' performances and to identify any bottlenecks in real time. In this work, two pairs of data gloves are utilized, which reports the location of the fingers, hands, and wrists wirelessly (i.e., via Bluetooth). To evaluate the performance of the proposed framework, a preliminary experiment was conducted in multiple lab settings of tomato grafting operations, where multiple subjects wear the data gloves while performing different tasks. Our results show an accuracy of 91% on average, in terms of gesture recognition in real time by employing our proposed framework.
Online course: Bitcoin and Blockchain

Gain a greater understanding of the fundamentals of cryptocurrencies, how it works and how it could alter how we perform transactions.
Editorial for the 4th Special Issue on advances in mixture models

The issue of model-based clustering of longitudinal data has attracted increasing attention in past two decades. Finite mixtures of Student’s-t linear mixed-effects (FM-tLME) models have been considered for implementing this task especially when data contain extreme observations. This paper presents an extended finite mixtures of Student’s-t linear mixed-effects (EFM-tLME) model, where the categorical component labels are assumed to be influenced by the observed covariates. As compared with the naive methods assuming the mixing proportions to be fixed but unknown, the proposed EFM-tLME model exploits a logistic function to link the relationship between the prior classification probabilities and the covariates of interest. To carry out maximum likelihood estimation, an alternating expectation conditional maximization (AECM) algorithm is developed under several model reduction schemes. The technique for extracting the information-based standard errors of parameter estimates is also investigated. The proposed method is illustrated using simulation experiments and real data from an AIDS clinical study.
RANCANG BANGUN MESIN AMPLAS DENGAN SISTEM MEKANIS BELT

Sanding work is a stage that is carried out before entering the finishing process to remove defects and smooth the surface of the object. The main purpose of making sanding machines with this mechanical belt system is to help and simplify the sanding process. The method for making sanding machines with mechanical belt systems is: needs analysis, problem analysis and specifications, problem statements, concept design, technical analysis, work drawing and tool testing. The results of the design of the sanding machine with a mechanical belt system are obtained in the form of a design with a working drawing of a sanding machine with a mechanical belt system. The sanding machine with a mechanical belt system has specifications that are 500 mm long, 350 mm wide, and 1400 mm high. The source of driving the sanding machine with a mechanical belt system is an electric motor ¼ HP with 2800 rpm rotation. The transmission system uses pulleys. The sanding machine with a mechanical belt consists of several components, namely the engine frame, electric motor, pulleys, tables, belt aplas, and adjustment of the belt strength of the sandpaper. The belt pulley used is from ST 37 with a diameter of 25 mm and pulley rotation of 2800 rpm. Frame construction uses hollow iron measuring 40x40x3 mm from ST 37 material. The table uses 590x150 acrylic material.
Precise Classification of Calcified Nodule as Malignant and Benign Using Variogram and Statistical Parameters

Background: Imaging using Computer tomography (CT) is responsible in the classification of pulmonary nodule. Identifying not only small malignant tumors but also benign solitary pulmonary nodule (SPN) is of utmost importance. This will save the patients from undergoing unneeded surgery. Certain criteria should be satisfied to classify the calcification in a benign SPN. Calcification of benign should enclose over 10% of the SPN and calcification should be Central, Diffuse, Popcorn type or Laminated. Every other patterns of calcification should not be viewed as a benign. Methods: Based on the differential diagnosis, the features can be extracted (Statistical Parameters, variogram) and on application as feature vector to neural network (supervised/unsupervised), classification can be done. The imaging interpretation of pulmonary nodule is dependent on clinical history, size distribution and the gross representation of the nodule as well as feasibility of getting a tissue diagnosis.Results: We have used Self Organizing Map (SOM) and Learning Vector Quantization (LVQ) as learning methods for neural network and LVQ has given precise classification results even with small number of samples.Conclusion: The combination of variogram analysis and statistical features such as Mean, Variance, Skewness and Kurtosis has produced accurate results. The variogram analysis is not only used as tool for feature extraction in the field of Geology by geostatisticians but it has also proved its worthiness in Bio-Medical Engineering for the purpose of feature extraction.
Additional OAuth Parameters for Authorization in Constrained Environments (ACE)

This specification defines new parameters for the OAuth 2.0 token andintrospection endpoints when used with the framework forauthentication and authorization for constrained environments (ACE).These are used to express the proof-of-possession key the clientwhishes to use, the proof-of-possession key that the AS has selected,and the key the RS should use to authenticate to the client.
Highly Repeatable Feature Point Detection in Images Using Laplacian Graph Centrality

Image registration is an indispensible task required in many image processing applications, which geometrically aligns multiple images of a scene, with differences caused due to time, viewpoint or by heterogeneous sensors. Feature-based registration algorithms are more robust to handle complex geometrical and intensity distortions when compared to area-based techniques. A set of appropriate geometrically invariant features forms the cornerstone for a feature-based registration framework. Feature point or interest point detectors extract salient structures such as points, lines, curves, regions, edges, or objects from the images. A novel interest point detector is presented in this paper. This algorithm computes interest points in a grayscale image by utilizing a graph centrality measure derived from a local image network. This approach exhibits superior repeatability in images where large photometric and geometric variations are present. The practical utility of this highly repeatable feature detector is evident from the simulation results.
Robust Text Classifier on Test-Time Budgets

Md Rizwan Parvez, Tolga Bolukbasi, Kai-Wei Chang, Venkatesh Saligrama. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
Open data using Cloud infrastructure

In this paper, we discuss Microsoft Research Open Data, a new data repository in the cloud dedicated to facilitating collaboration across the global research community. The repository provides a single, convenient location for research datasets. In a single, convenient, cloud-hosted location, research datasets across many domains such as computer science, social science, biology, genomics and others, representing many years of data curation efforts by researchers. The datasets are accompanied by meaningful research assets such as meta data and publications. The data can seamlessly be copied to a data user's cloud subscription on powerful data science virtual machines that accelerate research reproducibility and further research outcomes using the data.