Reviewerly/exHarmony huggingface dataset:
Construction of “Three Dimensions” Blended Teaching Mode in College English

In order to solve the teaching problems existed in college English teaching, this paper explores the new teaching mode which is the combination of classroom learning with online learning from three dimensions. The “three dimensions” blended teaching mode consists of three stages: first of all, students do the online preparation independently before each class to cultivate self-study abilities; the second stage, teachers and students interact with each other in class to internalize knowledge; finally, extend the limited in-class teaching to after-class study to fully grasp the knowledge. In this way, it can not only exhibit the essential role of teachers in guiding, but also stimulate students' learning interests and improve the quality of college English teaching.
Application of Large-scale Synchrotron X-Ray Fluorescence 2D Mapping of Alteration Styles to Understand Gold Mineralization at the Monument Bay Project, Stull Lake Greenstone Belt, Manitoba, Canada

Application of Large-scale Synchrotron X-Ray Fluorescence 2D Mapping of Alteration Styles to Understand Gold Mineralization at the Monument Bay Project, Stull Lake Greenstone Belt, Manitoba, Canada - Volume 26 Issue S2
Incremental learning of an abnormal behavior detection algorithm based on principal components

In this paper, we propose an incremental learning scheme for the abnormal behavior detection algorithm based on principal component. The results obtained on a UCSD dataset and our experimental videos at a different number of training samples show that error rates are similar to conventional learning. Moreover, the proposed scheme allows the incremental learning time to be significantly reduced in comparison with a method based on matrix eigendecomposition.
DEVELOPMENT OF THE AUTOMATED INFORMATION SYSTEM FOR ORGANIZATION’S INFORMATION SECURITY CULTURE LEVEL ASSESSMENT

Relevance of the research. Ensuring the effectiveness of the information security systems requires creation of an appropriate information security culture for the employees of the organization in order to reduce human-related risks. Target setting. The techniques currently available for assessing information security risk are excluded as a source of the potential vulnerability. Considering the role of the personnel in the organization's information security systems, there is a need to create automated systems of human-machine interaction assessment through the level of the personnel information security culture, and to determine the integral indicator of the organization's information security culture. Actual scientific researches and issues analysis. Open access publications on the problems of integrating the information security culture into the corporate culture of the organization as a tool for ensuring the proper information security level of business processes are considered. Uninvestigated parts of general matters defining. The absence of formalized models for assessing the organization's information security culture level, as well as an automated process for its assessing were revealed by source analysis. The research objective. The purpose of the article to build a model that describes the process of obtaining an organization's information security culture level assessment in IDEF0 notation. Then, to create an architecture and database for system of information security culture assessment to support the general organization's information security system. The statement of basic materials. According to functional requirements, a conceptual model of «The organization`s ISC level determination» development process was created. Input information, governing elements, execution elements and mechanism, and output information were defined. To accomplish these tasks, an architecture and database of information system for assessing the information security culture level of the organization were proposed. Conclusions. The functional model of top-level development process was proposed. Formed functional requirements became the basis for development of information system architecture with description of its modules and database structure.
A Data Management Strategy for Property Management Information System Based on the Internet of Things

In the era of big data, the traditional property management strategies can no longer meet the demand for modern development.The property management enterprise should enhance their service capacity and core competitiveness through scientific and standard informatization.This paper designs the basic framework of property management information system (PMIS), and specifies nine key functional modules of the system, namely, intelligent information query module, intelligent information transmission and reception module, and intelligent bulletin module.On this basis, a user small data (USD) service model was constructed, and a PMIS-oriented management strategy was presented for massive small data.Simulation results show that the proposed data management strategy achieved good effects on clustering and load balancing.
Link Prediction Using Higher-Order Feature Combinations across Objects

Link prediction, the computational problem of determining whether there is a link between two objects, is important in machine learning and data mining. Feature-based link prediction, in which the feature vectors of the two objects are given, is of particular interest because it can also be used for various identification-related problems. Although the factorization machine and the higher-order factorization machine (HOFM) are widely used for feature-based link prediction, they use feature combinations not only across the two objects but also from the same object. Feature combinations from the same object are irrelevant to major link prediction problems such as predicting identity because using them increases computational cost and degrades accuracy. In this paper, we present novel models that use higher-order feature combinations only across the two objects. Since there were no algorithms for efficiently computing higher-order feature combinations only across two objects, we derive one by leveraging reported and newly obtained results of calculating the ANOVA kernel. We present an efficient coordinate descent algorithm for proposed models. We also improve the effectiveness of the existing one for the HOFM. Furthermore, we extend proposed models to a deep neural network. Experimental results demonstrated the effectiveness of our proposed models.
Video Super-Resolution with Recurrent Structure-Detail Network

Most video super-resolution methods super-resolve a single reference frame with the help of neighboring frames in a temporal sliding window. They are less efficient compared to the recurrent-based methods. In this work, we propose a novel recurrent video super-resolution method which is both effective and efficient in exploiting previous frames to super-resolve the current frame. It divides the input into structure and detail components which are fed to a recurrent unit composed of several proposed two-stream structure-detail blocks. In addition, a hidden state adaptation module that allows the current frame to selectively use information from hidden state is introduced to enhance its robustness to appearance change and error accumulation. Extensive ablation study validate the effectiveness of the proposed modules. Experiments on several benchmark datasets demonstrate the superior performance of the proposed method compared to state-of-the-art methods on video super-resolution.
GEOMETRIC AND NON-LINEAR RADIOMETRIC DISTORTION ROBUST MULTIMODAL IMAGE MATCHING VIA EXPLOITING DEEP FEATURE MAPS

Abstract. Image matching is a fundamental issue of multimodal images fusion. Most of recent researches only focus on the non-linear radiometric distortion on coarsely registered multimodal images. The global geometric distortion between images should be eliminated based on prior information (e.g. direct geo-referencing information and ground sample distance) before using these methods to find correspondences. However, the prior information is not always available or accurate enough. In this case, users have to select some ground control points manually to do image registration and make the methods work. Otherwise, these methods will fail. To overcome this problem, we propose a robust deep learning-based multimodal image matching method that can deal with geometric and non-linear radiometric distortion simultaneously by exploiting deep feature maps. It is observed in our study that some of the deep feature maps have similar grayscale distribution and correspondences can be found from these maps using traditional geometric distortion robust matching methods even significant non-linear radiometric difference exists between the original images. Therefore, we can only focus on the geometric distortion when we deal with deep feature maps, and then only focus on non-linear radiometric distortion in patches similarity measurement. The experimental results demonstrate that the proposed method performs better than the state-of-the-art matching methods on multimodal images with both geometric and non-linear radiometric distortion.
Innovation and Growth Ambition of Female Entrepreneurs: A Comparison between the MENA Region and the Rest of the World

The aim of the current paper was to compare the regional differences in innovation and the resulting growth expectation among female entrepreneurs in the MENA region as compared with the rest of the world.Data on innovation and growth aspirations of 162,752 female entrepreneurs in 109 countries was extracted from GEM annual surveys in 2002-2016.A hierarchical linear modelling technique was used to analyse the data.Interestingly, results indicated that female entrepreneurs in the MENA region are more innovative than their peers in the rest of the world; however, there is no difference between the MENA female entrepreneurs and their counterparts in the rest of the world in terms of growth ambitions and benefiting from innovation for growth.Innovation was found to be positively related to shaping growth ambitions in female entrepreneurs.
PROBLEMS OF FORENSIC PHONOSCOPIC EXAMINATION IN THE LIGHT OF THE DEVELOPMENT OF DIGITAL TECHNOLOGIES

The article deals with some problems related to the production of forensic speech and audio analysis, taking into account global digitalization of all spheres of human life. Voicechanger, technology of voice synthesis signifi cantly complicate forensic phonoscopic examinations and put before the forensic experts a new, interesting challenges. We consider the algorithm for changing the voice using modern software, as well as features of voice synthesis technologies. Some variants of studying such phonograms are proposed.
DEEP LEARNING BASED FEATURE MATCHING AND ITS APPLICATION IN IMAGE ORIENTATION

Abstract. Matching images containing large viewpoint and viewing direction changes, resulting in large perspective differences, still is a very challenging problem. Affine shape estimation, orientation assignment and feature description algorithms based on detected hand crafted features have shown to be error prone. In this paper, affine shape estimation, orientation assignment and description of local features is achieved through deep learning. Those three modules are trained based on loss functions optimizing the matching performance of input patch pairs. The trained descriptors are first evaluated on the Brown dataset (Brown et al., 2011), a standard descriptor performance benchmark. The whole pipeline is then tested on images of small blocks acquired with an aerial penta camera, to compute image orientation. The results show that learned features perform significantly better than alternatives based on hand crafted features.
Differential Evolution-based Approach for Tone-Mapping of High Dynamic Range Images

Recently, high dynamic range (HDR) imaging has received significant attention from research community as well as the industrial companies due to valuable applications of HDR images in better visualization and analysis.However, HDR images need to be converted to low dynamic range (LDR) images for viewing on standard LDR display screens.Several tonemapping operators have been proposed for the conversion, however, so far, no significant works have been reported employing artificial intelligence to achieve better enhancement of the output images.In this paper, we present an optimizationbased approach, to enhance the quality of the tone-mapped LDR images using metaheuristics.More specifically, the optimization process is based on the differential evolution (DE) algorithm which takes tone-mapping function of an existing histogrambased method as initial guess and refines the histogram bins iteratively leading to progressive enhancement of the quality of LDR image.The final results produced by the proposed optimized histogram-based approach (OHbA) showed better performance compared to the existing state-of-the-art tonemapping algorithms.
An Interpretable Deep Learning System for Automatically Scoring Request for Proposals

The Managed Care system within Medicaid (US Healthcare) uses Request For Proposals (RFP) to award contracts for various healthcare and related services. RFP responses are very detailed documents (hundreds of pages) submitted by competing organisations to win contracts. Subject matter expertise and domain knowledge play an important role in preparing RFP responses along with analysis of historical submissions. Automated analysis of these responses through Natural Language Processing (NLP) systems can reduce time and effort needed to explore historical responses, and assisting in writing better responses. Our work draws parallels between scoring RFPs and essay scoring models, while highlighting new challenges and the need for interpretability. Typical scoring models focus on word level impacts to grade essays and other short write-ups. We propose a novel Bi-LSTM based regression model, and provide deeper insight into phrases which latently impact scoring of responses. We contend the merits of our proposed methodology using extensive quantitative experiments. We also qualitatively asses the impact of important phrases using human evaluators. Finally, we introduce a novel problem statement that can be used to further improve the state of the art in NLP based automatic scoring systems.
Self-supervised Temporal Discriminative Learning for Video Representation Learning

Temporal cues in videos provide important information for recognizing actions accurately. However, temporal-discriminative features can hardly be extracted without using an annotated large-scale video action dataset for training. This paper proposes a novel Video-based Temporal-Discriminative Learning (VTDL) framework in self-supervised manner. Without labelled data for network pretraining, temporal triplet is generated for each anchor video by using segment of the same or different time interval so as to enhance the capacity for temporal feature representation. Measuring temporal information by time derivative, Temporal Consistent Augmentation (TCA) is designed to ensure that the time derivative (in any order) of the augmented positive is invariant except for a scaling constant. Finally, temporal-discriminative features are learnt by minimizing the distance between each anchor and its augmented positive, while the distance between each anchor and its augmented negative as well as other videos saved in the memory bank is maximized to enrich the representation diversity. In the downstream action recognition task, the proposed method significantly outperforms existing related works. Surprisingly, the proposed self-supervised approach is better than fully-supervised methods on UCF101 and HMDB51 when a small-scale video dataset (with only thousands of videos) is used for pre-training. The code has been made publicly available on https://github.com/FingerRec/Self-Supervised-Temporal-Discriminative-Representation-Learning-for-Video-Action-Recognition.
Limit Shapes for Gibbs Partitions of Sets

This study extends a prior investigation of limit shapes for partitions of integers, which was based on analysis of sums of geometric random variables. Here we compute limit shapes for grand canonical Gibbs ensembles of partitions of sets, which lead to the sums of Poisson random variables. Under mild monotonicity assumptions, we study all possible scenarios arising from different asymptotic behaviors of the energy, and also compute local limit shape profiles for cases in which the limit shape is a step function.
Use Of Augmented Reality As A Learning Media In Early Childhood Education Solideo Perumnas I Jayapura

Learning media for young children is a tool used to introduce the theme as teaching materials or what is often called the curriculum for early childhood. The more sense tools are involved in the learning process, the more likely the child understands the information presented by the teacher. Recently, learning media still uses face to face methods or books. Meanwhile, early childhood is easily bored and more interested in new things. This research aims to develop a Learning media that used to introduce animals using Augmented Reality (AR) so that children can know the environment and food needed by the introduced animal. 83% of teachers and 91% of students enjoy using the application in animal recognition. KEYWORDS Learning Media, Animal Recognition, Augmented Reality, Mobile Phone
Pendampingan Penulisan Artikel Ilmiah dari Hasil Penelitian bagi Guru Matematika SMK Kota Kediri

Doing research and publication in the form of scientific article is not part of teachers’ duties as an educator. Conducting research particularly class action research, however, is a necessity for teachers to solve some problems experienced in class. The needs to publish an article from research result emerges from the importance of sharing research result conducting in class. This research result may solve similar problems happened in other classes and experinced by other teachers. High school mathematics teachers in Kediri city encounter difficulties in transforming research result into a scientific article. This community service program provides a training and accompaniment in writing a scientific article. This program involves some activities, i.e. (1) elucidating the structure of scientific articles and its description, (2) giving examples of scientific articles from research results, (3) practicing in writing scientific article, and (4) presenting article results from practice section. This program is expected to encourage high school mathematics teachers in Kediri city to have a scientific article and to publish the article in seminars or scientific journals. DOI : https://doi.org/10.26905/abdimas.v5i2.3820
Algorithm for the preliminary study the copies of handwritten details in framework the production of handwriting examination

The possibility of refi ning and unifying the algorithm of preliminary examination of copies of documents to establish their suitability/unsuitability for further research is considered. Examples of the possibility of establishing the fact of signs of editing in copies of documents presence are given. Empowerment the fi eld of handwriting examiners training through introducing special knowledge from the fi elds of questioned documents examination and computer forensic into the training program is proposed. The stages of the proposed algorithm are analyzed sequentially. The author’s algorithm for preliminary examination of copies of documents containing handwritten requisites images is given. Arguments in favor of the practical possibility of producing handwriting examination of copies of handwritten requisites are given. The correlation of examination of copies and originals of handwritten requisites have been undertaken. Illustrations demonstrating the dependence of the ability to evaluate some features on the resolution of the image of a handwritten object are presented.
Teknologi Augmented Reality pada Promosi Berbasis Android

Technological advances can be applied in the business world as a medium of promotional media. Augmented Reality (AR) is a technology that combines 2 dimensions and 3 dimensions into a ward, then projecting the virtual objects in real time. Augmented Reality can be utilized in a variety of needs, one of which is the utilization of promotional media on the Honda Genio brochure. The purpose of this research is to develop new interactive media in assisting the promotion media in Astra Motor Majenang with Android based Augmented Reality technology. The methods used are MDLC (Multimedia Development Life Cycle) consisting of six stages, the concept, the design, the material collecting, the assembly, testing, and the distribution (Distribuiton). The result of this research is an Android-based Augmented Reality promotional media app on the Honda Genio brochure that can display 3D motor objects and display specs in both text and audio form. The conclusion of the study was from the test results Alpha Test system developed spared from disability or failure of use. As for test results Beta Test, 91.2% of respondents answered strongly agree that it shows that the Augmented Reality app as a promotional medium on Android-based Honda Genio brochure is very interesting and acceptable for users.
Deep online classification using pseudo-generative models

In this work we propose a new deep learning based approach for online classification on streams of high-dimensional data. While requiring very little historical data storage, our approach is able to alleviate catastrophic forgetting in the scenario of continual learning with no assumption on the stationarity of the data in the stream. To make up for the absence of historical data, we propose a new generative autoencoder endowed with an auxiliary loss function that ensures fast task-sensitive convergence. To evaluate our approach we perform experiments on two well-known image datasets, MNIST and LSUN, in a continuous streaming mode. We extend the experiments to a large multi-class synthetic dataset that allows to check the performance of our method in more challenging settings with up to 1000 distinct classes. Our approach is able to perform classification on dynamic data streams with an accuracy close to the results obtained in the offline classification setup where all the data are available for the full duration of training. In addition, we demonstrate the ability of our method to adapt to unseen data classes and new instances of already known data categories, while avoiding catastrophic forgetting of previously acquired knowledge.