Reviewerly/exHarmony huggingface dataset:
Subspace clustering based on alignment and graph embedding

In this paper, we propose a new subspace clustering method based on alignment and graph embedding (SCAGE). In SCAGE, we unify the image alignment process and clustering subspace learning process based on low rank and sparse representation. Besides, we use the label prediction information, error information and coefficients to conduct the graph embedding. In addition, the prior knowledge is used to initialize the label prediction matrix which not only speeds up the converge of the clustering process but also achieves a better result. Various experiments show that SCAGE achieves better performance than state-of-the-art algorithms.
Twitter Data Sentiment Analysis on a Malayalam Dataset Using Rule-Based Approach

Opinion characterization is nowadays a potential and intense research focus because of the hasty growth of social media such as blogs and social networking sites, where individuals put in freely their perspectives on different themes. Researches prove that people find it comfortable to opinionate in their mother tongue, be it verbal or written. Given that now almost all social platforms support most of the popular languages, the requirement to mine the sentiments in various dialects is on the rise. However, not all data may be relevant; some may not have any impact on the end result and some may have similar meanings. A preprocessing phase is hence required to help make the dataset concise. In this paper, the authors focus on finding out the polarity of the words input by various users through their reviews exhibited using the South Indian language, Malayalam. Malayalam like the other languages in the Dravidian family exhibits the characteristics of an agglutinative language. The preprocessing process consists of cleaning the data, tokenization, stopword removal, etc. In this paper, authors are focusing on the document-based polarity calculation of the Malayalam reviews. The overall polarity of the corpus is calculated based on the positivity and negativity values of individual documents. It is found that negativity value is higher for the user reviews in our corpus which shows their negative attitude toward the news thread with the classifier accuracy of 89.33%.
Gaze-Based Interaction for VR Environments

In this paper we propose a steering mechanism for VR headset utilizing eye tracking. Based on the fovea region traced by the eyetracker assembled into VR headset the visible 3D ray is generated towards the focal point of sight. The user can freely look around the virtual scene and is able to interact with objects indicated by the eyes. The paper gives an overview of the proposed interaction system and addresses the effectiveness and precision issues of such interaction modality.
Fault diagnosis of multistage centrifugal pump unit using non-local means-based vibration signal denoising

In real industry environment, the signal characteristics of multistage centrifugal pump vibration signal are easily submerged by strong background noise. To settle this problem, the nonlocal means (NLM) approach is proposed for the denoising of multistage centrifugal pump in this paper. Utilizing the similarity theory, the NLM method has achieved a wide range of applications in the fields of image processing and biomedical signal denoising. Due to the periodic characteristics and redundancy, NLM is successfully applied to the de-noising of 1-D machinery vibration signal. The numerical simulation experiments with different SNRs verify the effectiveness and the superiority of the proposed method. Besides, the selection principles of core parameters in NLM are discussed. The real engineering cases analysis demonstrates that the NLM can effectively filter out the background noise and realize the weak fault feature enhancements. The proposed noise reduction method is superior to traditional wavelet coefficient method.
Structuring Latent Spaces for Stylized Response Generation

Generating responses in a targeted style is a useful yet challenging task, especially in the absence of parallel data. With limited data, existing methods tend to generate responses that are either less stylized or less context-relevant. We propose StyleFusion, which bridges conversation modeling and non-parallel style transfer by sharing a structured latent space. This structure allows the system to generate stylized relevant responses by sampling in the neighborhood of the conversation model prediction, and continuously control the style level. We demonstrate this method using dialogues from Reddit data and two sets of sentences with distinct styles (arXiv and Sherlock Holmes novels). Automatic and human evaluation show that, without sacrificing appropriateness, the system generates responses of the targeted style and outperforms competitive baselines.
Explainable Machine Learning in Deployment

Explainable machine learning offers the potential to provide stakeholders with insights into model behavior by using various methods such as feature importance scores, counterfactual explanations, or influential training data. Yet there is little understanding of how organizations use these methods in practice. This study explores how organizations view and use explainability for stakeholder consumption. We find that, currently, the majority of deployments are not for end users affected by the model but rather for machine learning engineers, who use explainability to debug the model itself. There is thus a gap between explainability in practice and the goal of transparency, since explanations primarily serve internal stakeholders rather than external ones. Our study synthesizes the limitations of current explainability techniques that hamper their use for end users. To facilitate end user interaction, we develop a framework for establishing clear goals for explainability. We end by discussing concerns raised regarding explainability.
Watermarking Technique Based on Harris-Laplace Feature Point Detector Capable of Resisting Geometric Attacks

In this paper, a strong watermarking technique is proposed that first selects the more stable feature points of an image by using the Harris-Laplace detector and then adjusts the included angles among these feature points, centers of mass, and the most salient feature point to embed the secret message. Triangles are formed among the feature points before adjustment through Delaunay triangulation, and subsequently, the adjusted feature points are further connected. Finally, each triangle is fine-tuned through affine transformation to embed the watermarking. According to the experimental results, the proposed method possesses strong resistance to both geometric attacks and general image processing attacks.
Development of professional ICT competence for future teachers of the Tuva republic under the conditions of bilingualism

А. К. Тарыма, В. А. Шершнева
A Study on the Culture Governance System for Sustainable Urban Development

The 21st century is the “century of the city,” given that more than half of the world’s population is living in urban areas. This emergence of cities has led to the development of local areas, which causes various problems because of urbanization. Cooperation between the state and various interested parties is important in order to settle the problems caused by urbanization and achieve sustainable development of cities. In particular, discussions about governance have been increasing in the field of culture and arts policies. These discussions highlight the inefficiency of central government-oriented establishments and their implementation of cultural policies, emphasizing the need for new and different approaches. Therefore, for the sustainable development of cities, it is necessary to establish a form of cultural governance that is not state-centric; a network consisting of members from various interested parties such as local governments, culture and arts organizations situated in each city, universities, citizens, enterprises and international organizations, etc. The purpose of this study is to seek methods to establish a culture governance system for the sustainable development of cities. For this purpose, this study analyzed the following reports: the 2030 Agenda for Sustainable Development (2015) of the UN, Culture 2: Actions (2015) of the UCLG, and Reshaping Cultural Policies (2015, 2018) published by UNESCO. This study is different from existing research through its focus on the necessary measures for the establishment of sustainable cultural governance from the viewpoint of a transnational city network, and in the direction it provides towards the establishment of a cultural governance system for the sustainable development of cities.
pySRURGS - a python package for symbolic regression by uniform random global search

Symbolic regression is a regression analysis where we search for the mathematical expression that best describes the relationship between our independent variables and our dependent variable.pySRURGS is a python code for symbolic regression by uniform random global search.Typically, symbolic regression problems are solved using genetic programming (Koza, 1994), but the data demonstrating that genetic programming outperforms random search on symbolic regression problems is lacking.Comparing the performance of an algorithm against that of random search demonstrates the advantage gained by the algorithm's internal machinery.Our review of the literature found a large study examining the parameter space of genetic programming, which found few patterns with regards to how users should select genetic algorithm hyperparameters (Sipper, Fu, Ahuja, & Moore, 2018).These workers attempt to compare their genetic algoritm's performance to that of random search on several problems, including a symbolic regression problem.We reproduce their work and find they unfairly disadvantaged the random search by forcing it to only attempt the simplest solutions.There is little evidence that genetic algorithms outperform random search on the symbolic regression problem and the creation of pySRURGS addresses this need.
Dynamic sampling rate algorithm (DSRA) implemented in self-adaptive software architecture: a way to reduce the energy consumption of wireless sensors through event-based sampling

Abstract With the recent digitalization trends in the industry, wireless sensors are, in particular, gaining a growing interest. This is due to the possibility of being installed in inaccessible locations for wired sensors. Although great success has already been achieved in this area, energy limitation remains a major obstacle for further advances. As such, it is important to optimize the sampling with a sufficient rate to catch important information without excessive energy consumption, and one way to achieve sufficient sampling is using adaptive sampling for sensors. As software plays an important role in the techniques of adaptive sampling, a reference framework for software architecture is important in order to facilitate their design, modeling, and implementation. This study proposes a software architecture, named Rainbow, as the reference architecture, also, it develops an algorithm for adaptive sampling. The algorithm was implemented in the Rainbow architecture and tested using two datasets; the results show the proper operation of the architecture as well as the algorithm. In conclusion, the Rainbow software architecture has the potential to be used as a framework for adaptive sampling algorithms, and the developed algorithm allows adaptive sampling based on the changes in the signal.
Eye-wearable head-mounted tracking and gaze estimation interactive machine system for human–machine interface

In this study, a head-mounted camera was used to track eye behaviors and estimate the gaze point on the user’s visual plane. The integration of the elastic mechanism design makes the headset adaptable for various users. The wearable cases were prototyped with low-cost cameras to produce an efficient eye tracking solution. This proposed system can effectively extract and estimate pupil ellipse from a few camera images of an eye and compute the corresponding three-dimensional eye model. The system can match later images of the same pupil ellipse from a head-mounted camera to give the possible visual angles. To estimate the gaze point, the system uses multiple-point calibration to solve the related polynomial formula for future angle-to-gaze mapping. The proposed eye-tracking algorithms can provide a low-complexity solution with high accuracy, precision, and speed. This tracking system is a low-cost and promising system that can be used in headsets for virtual reality, auxiliary equipment, interactive machine, and human–machine interface applications. The proposed eye-tracking algorithm can achieve satisfactory performance without using a high-end high-speed camera and can be detected under different lighting sources, and the average errors of the detection results are stably within 9 pixels and at a distance of 50 cm from the screen; while the average error of the fixation mapping results is within 3°.
Factorization of bivariate sparse polynomials

We prove a function field analogue of a conjecture of Schinzel on the factorization of univariate polynomials over the rationals. We derive from it a finiteness theorem for the irreducible factorizations of the bivariate Laurent polynomials in families wi
PENGARUH PENGGUNAAN TEKNIK THINK TALK WRITE (TTW) TERHADAP KETERAMPILAN MENULIS TEKS FABEL SISWA KELAS VII SMP NEGERI 31 PADANG

ABSTRACT The purpose of this study there are three. First, describe the skills of writing fable text of grade VII students of SMP Negeri 31 Padang before using Think Talk Write (TTW) technique. Second, describe the skills of writing fable text of grade VII students of SMP Negeri 31 Padang after using Think Talk Write (TTW) technique. Third, analyze the influence of Think Talk Write (TTW) technique to the writing skill of fable text of grade VII students of SMP Negeri 31 Padang. Kata Kunci: Pengaruh, Teknik Think Talk Write, Teks Fabel
HGAB3C: A new hybrid global optimization algorithm

This paper proposes a new optimization algorithm, namely HGAB3C, and presents its performance on the CEC-2014 test suite.In HGAB3C, simple genetic algorithms (GAs) and big bang-big crunch (BB-BC) are hybridized.The algorithm carries out global searches using a simple GA.In every generation the BB-BC algorithm is used to carry out local searches.The addition of local search has improved the capability of simple GAs significantly.The performance of the proposed algorithm is compared with 17 other optimization algorithms on all 30 functions of the CEC-2014 benchmark suite.It is observed that HGAB3C outperforms all other algorithms on 4 benchmark functions.For the 3 other functions, its performance equaled the best of the competing algorithms, which makes HGAB3C's performance best in a total of 7 benchmark functions.Out of the 18 competing algorithms, the proposed algorithm ranked second for the unmatched best mean error measure.For the best performance measure (number of functions giving unmatched best and equaled best mean error), the proposed algorithm was the third best.As far as the speed of convergence is concerned, the algorithm gave an unmatched best performance for the shifted Schwefel function (function 10 of CEC-2014 test bench).It obtained a mean error value of 0.00E+00, outperforming the previous best of 1.23E-03, converging to the target result in an average of 346.44 generations, which no other algorithm could achieve.
Neural mechanisms of fairness formation in the perspective of social interactions

惩罚下提议者的分配金额更高, 且惩罚力 度越强, 分配越趋近公平分配。fNIRS 的结果显示, 惩罚下右侧背外侧前额叶皮层、顶下小叶和颞-顶联合区的脑 间活动同步性显著强于无惩罚条件, 而且两条件的分配金额差异越大, 右侧顶下小叶的脑间活动同步性差异也越 大。 综上, 脑间活动同步性可以作为惩罚下人际公平形成的客观脑指标, 研究为探讨人际公平的内在机制提供了新
Mineral Resources in Egypt (II): Non-metallic Ore Deposits

This chapter reviews four types of non-metallic ore deposits in Egypt. These deposits are phosphate, white sand (glass sand or silica sand), argillic and evaporite deposits.
Deep 3D-Zoom Net: Unsupervised Learning of Photo-Realistic 3D-Zoom

The 3D-zoom operation is the positive translation of the camera in the Z-axis, perpendicular to the image plane. In contrast, the optical zoom changes the focal length and the digital zoom is used to enlarge a certain region of an image to the original image size. In this paper, we are the first to formulate an unsupervised 3D-zoom learning problem where images with an arbitrary zoom factor can be generated from a given single image. An unsupervised framework is convenient, as it is a challenging task to obtain a 3D-zoom dataset of natural scenes due to the need for special equipment to ensure camera movement is restricted to the Z-axis. In addition, the objects in the scenes should not move when being captured, which hinders the construction of a large dataset of outdoor scenes. We present a novel unsupervised framework to learn how to generate arbitrarily 3D-zoomed versions of a single image, not requiring a 3D-zoom ground truth, called the Deep 3D-Zoom Net. The Deep 3D-Zoom Net incorporates the following features: (i) transfer learning from a pre-trained disparity estimation network via a back re-projection reconstruction loss; (ii) a fully convolutional network architecture that models depth-image-based rendering (DIBR), taking into account high-frequency details without the need for estimating the intermediate disparity; and (iii) incorporating a discriminator network that acts as a no-reference penalty for unnaturally rendered areas. Even though there is no baseline to fairly compare our results, our method outperforms previous novel view synthesis research in terms of realistic appearance on large camera baselines. We performed extensive experiments to verify the effectiveness of our method on the KITTI and Cityscapes datasets.
Intelligent Hypertext for Video Selection

In this paper, we describe our project DemoMedia, a software demonstrator that combines hypertext and recommender functionality in the context of video acquisition or use. DemoMedia fills the gap that exists in today's video platforms which include recommender functionalities, but only trivial support for users to structure information. Thus, users are forced to write down notes from or about videos (needed for various reasons) on additional media, such as paper. This opens a media gap between video platform and note-taking or communication to others. DemoMedia becomes a note taking and communication tool for the user, as it offers a knowledge space on which users can freely arrange and associate information. Furthermore, its intelligent parsers compute relations that are implicitly expressed and queries knowledge bases for relevant information or related videos. Those get positioned on the space in a semantically meaningful way. DemoMedia and the underlying component-based open hypermedia system Mother combine both the machine's capability of extracting knowledge from huge amounts of data and the human capability of sensemaking, intuition, and creativity.
National Research Universities: Training of Highly Qualified Scientific Personnel under Development Programmes

In 2008–2009, 29 national research universities (NRU) with approved priority areas of development were established in Russia. In 2019 the ten-year program of development of NRU comes to an end. The article presents the results of the analysis of the national research universities’ activity of training scientific personnel for high-tech industries. Two indicators were calculated for each priority area of development of NRU reflecting the shares of defenses of candidate and doctor theses in the national research universities in relation to all thesis defenses. This made it possible to assess what leading positions in priority areas are occupied by the national research universities. The analysis showed that NRU do not provide leading positions to 30% of the priority areas of development. In general, most of the national research universities occupy high positions in certain (specific) priority areas of development in terms of training scientific personnel for high-tech sectors of the economy.
Quantile-Based Estimation of the Finite Cauchy Mixture Model

Heterogeneity and outliers are two aspects which add considerable complexity to the analysis of data. The Cauchy mixture model is an attractive device to deal with both issues simultaneously. This paper develops an Expectation-Maximization-type algorithm to estimate the Cauchy mixture parameters. The main ingredient of the algorithm are appropriately weighted component-wise quantiles which can be efficiently computed. The effectiveness of the method is demonstrated through a simulation study, and the techniques are illustrated by real data from the fields of psychology, engineering and computer vision.
Extractive Summarization of Long Documents by Combining Global and Local Context

In this paper, we propose a novel neural single document extractive summarization model for long documents, incorporating both the global context of the whole document and the local context within the current topic. We evaluate the model on two datasets of scientific papers, Pubmed and arXiv, where it outperforms previous work, both extractive and abstractive models, on ROUGE-1, ROUGE-2 and METEOR scores. We also show that, consistently with our goal, the benefits of our method become stronger as we apply it to longer documents. Rather surprisingly, an ablation study indicates that the benefits of our model seem to come exclusively from modeling the local context, even for the longest documents.
Programmable SCOW Mesh Silicon Photonic Processor for Linear Unitary Operator

Universal unitary multiport interferometers (UMIs) can perform any arbitrary unitary transformation to a vector of input optical modes, which are essential for a wide range of applications. Most UMIs are realized by fixed photonic circuits with a triangular or a rectangular architecture. Here, we present the implementation of an N × N rectangular UMI with a programmable photonic processor based on two-dimensional meshes of self-coupled optical waveguide (SCOW) resonant structures. Our architecture shows a high tolerance to the unbalanced loss upon interference. This work enriches the functionality of the SCOW mesh photonic processors, which are promising for field-programmable photonic arrays.
Sentence Retrieval for Entity List Extraction with a Seed, Context, and Topic

We present a variation of the corpus-based entity set expansion and entity list completion task. A user-specified query and a sentence containing one seed entity are the input to the task. The output is a list of sentences that contain other instances of the entity class indicated by the input. We construct a semantic query expansion model that leverages topical context around the seed entity and scores sentences. The proposed model finds 46% of the target entity class by retrieving 20 sentences on average. It achieves 16% improvement over BM25 in terms of [email protected]
Reform of Diversified Talents Training Mode against the Background of Engineering Education Accreditation

The Engineering Education Accreditation is an internationally accepted quality assurance system for engineering education.The OBE concept is fully implemented in the Measurement and Control Technology and Instrument specialty of Beijing Information Science and Technology University, and the talent training system that accords with the accreditation standard of engineering education is established to ensure the training outcomes.On the basis of full coverage of ability reached, the reform of diversified training mode is actively explored.The talents are jointly trained with the domestic and foreign wellknown universities and research institutes.Under the guidance of CDIO, applied engineering talents training with the mode of industry-university-research cooperation is practiced.The remarkable results