Reviewerly/exHarmony huggingface dataset:
Adaptive Multilingual Representations for Cross-Lingual Entity Linking with Attention on Entity Descriptions

Cross-lingual entity linking is the task of resolving ambiguous mentions in text to corresponding entities in knowledge base, where the query text and knowledge base are in different languages. Recent multilingual embedding based methods bring significant progress in this task. However, they still meet some potential problems: (1) They directly use multilingual embeddings obtained by cross-lingual mapping, which may bring noise and degrade the performance; (2) They also rely on the pre-trained fixed entity embeddings, which only carry limited information about entities. In this paper, we propose a cross-lingual entity linking framework with the help of more adaptive representations. For the first problem, we apply trainable adjusting matrices to fine-tune the semantic representations built from multilingual embeddings. For the second problem, we introduce attention mechanisms on entity descriptions to obtain dynamic entity representations, exploiting more clues about entity candidates according to the query mentions. Experiments on the TAC KBP 2015 Chinese-English cross-lingual entity linking dataset show that our model yields better performance than state-of-the-art models.
Generative Adversarial Networks for Oracle Generation and Discrimitation

Generative Adversarial Networks (GANs) have become the most prevalent generative models in recent years. However, due to the oracle image is a specially and rarely dataset, it brings huge challenge for interpreting and recognizing oracle information. Therefore, it becomes especially important to obtain more generated oracle images. In this paper, different from the original image, we use Gaussian pyramid (GP) to preprocess oracle, and analyze different generated models based on GANs. Experiment results show that our approach can synthesize highly realistic images of oracle bones, which is difficult to distinguish from the real oracles.
Software System for the Support of Mouse Tracking Tests

Among the tests that contribute to improve the usability of a software product are those based on mouse tracking. Currently, there are tools for the execution of mouse tracking tests, most of which do not allow to discriminate the analysis by areas of the screen or the analysis of the trace in a specific task of a user test. In this paper we present a software system for the execution of usability tests under the mouse tracking approach, which allows the capture of the mouse trace, the analysis of the areas of interest per task and the generation of the video of the session. Through the proposed software system, we hope to obtain specific recommendations regarding the adequate disposition of the elements on the screen. The proposal can serve as a starting point in future work and help in the development of more usable applications.
A Domain Experts Centric Approach to Formal Requirements Modeling and V&amp;V of Embedded Control Software

Formal method is a promising solution for precise software requirements modeling and V&V (Validation and Verification). However, domain experts are suffering from using complex mathematics formal notations to precisely describe their domain specific software requirements. Meanwhile, the lack of systematic engineering methodologies that can effectively encompass precise requirements modeling and rigorous requirements V&V makes the application of formal methods in industry still a big challenge. To tackle this challenge, in this paper, we present a domain experts centric approach to the formal requirements modeling and V&V in the domain of embedded control software. The major advancements of the approach are: 1) a domain-specific and systematic engineering approach to the formal requirements specification construction and 2) scenario-based requirements validation and verification requirements technique. Specifically, the approach offers a domain-specific template for formal specification construction through a three-step specification evolution process. For formal requirements V&V, diagrams are derived from formal specification and domain experts' concerned scenarios can be checked based on the diagrams. These modeling and V&V technologies are coherently incorporated in the approach and fully automated by a supporting tool. We have applied the approach real software projects of our industrial partners. The experimental results show that it significantly facilitates the formal modeling and V&V in industry.
Load-dependent collagen fiber architecture data of representative bovine tendon and mitral valve anterior leaflet tissues as quantified by an integrated opto-mechanical system

The data presented in this article provide load-dependent collagen fiber architecture (CFA) of one representative bovine tendon tissue sample and two representative porcine mitral valve anterior leaflet tissues, and they are stored in a MATLAB MAT-file format. Each dataset contains: (i) the number of pixel points, (ii) the array of pixel's x- and y-coordinates, (iii) the three acquired pixel intensity arrays, and (iv) the Delaunay triangulation for visualization purpose. This dataset is associated with a companion journal article, which can be consulted for further information about the methodology, results, and discussion of the opto-mechanical characterization of the tissue's CFA's (Jett etal. [1]).
Quantitative assessment of China’s mineral resources part 2

• Integration of metallogeny and IT-tech for Mineral Resources Assessment in China. • Prediction model interpreted by mass multiple spatial datasets. • GIS-based comprehensive methodology was used via software tool developed. • To provide scientific decision-making for deployment of mineral exploitation in China.
Zero-Shot Fashion Products Clustering on Social Image Streams

Computer Vision methods have been proposed to solve the problem of matching photographs containing some products from users in social media to products in retail catalogues. This is challenging due to the quality of the photographies, difficulties in dealing with garments and their category taxonomy. A N-Shot Learning approach is required as retail catalogues may contain hundreds of different products for which, in many cases, only one image is provided. This framework can be solved by means of Deep Metric Learning (DML) techniques, in which a metric to discriminate similar than dissimilar samples is learnt. The performance of different authors tackling this problem varies a lot but even if they perform reasonably well, the set of elements they need to return in order to include the exact product is large. As after the query there is a person curating the results, it is important to return the smallest set of elements possible, being ideally just to return only one: the related product. This paper proposes to solve the image-to-product image matching problem through a product retrieval system using DML and Zero-short Learning, focusing on garments, and applying some of the last advances on clustering techniques.
A Deep Convolutional Deblurring and Detection Neural Network for Localizing Text in Videos

Scene text in the video is usually vulnerable to various blurs like those caused by camera or text motions, which brings additional difficulty to reliably extract them from the video for content-based video applications. In this paper, we propose a novel fully convolutional deep neural network for deblurring and detecting text in the video. Specifically, to cope with blur of video text, we propose an effective deblurring subnetwork that is composed of multi-level convolutional blocks with both cross-block (long) and within-block (short) skip connections for progressively learning residual deblurred image details as well as a spatial attention mechanism to pay more attention on blurred regions, which generates the sharper image for current frame by fusing multiple surrounding adjacent frames. To further localize text in the frames, we enhance the EAST text detection model by introducing deformable convolution layers and deconvolution layers, which better capture widely varied appearances of video text. Experiments on the public scene text video dataset demonstrate the state-of-the-art performance of the proposed video text deblurring and detection model.
Exploring Noise Deep Q Network Based on Cross-Connected

Deep Q Network, which is a combination of CNN and Q-Learning, is an important turning point in deep reinforcement learning. DQN algorithm has achieved great success on the Atari 2600 game platform. However, in some complex game environments, on the one hand, CNN is not accurate enough to extract the original environment feature information points, which leads to unreasonable action strategy selection in the later stage of DQN; on the other hand, the local ε-perturbation strategy adopted by DQN can not be effectively explored on a large scale, resulting in the optimal strategy of action space perturbation. To solve these problems, a new exploratory noise deep Q network based on cross-connected is proposed. Firstly, the model is a six-layer network structure, which includes the input layer, three convolution layer, one full-connection layer and the output layer. The second convolution layer is connected to the full-connection layer, making full use of the characteristics of low-level. Secondly, parameter noise is introduced into the whole connection layer and output layer, which adds parameter noise to its weight and causes network output changes. According to the changes, a large number of new states are explored, more abundant samples are provided, and effective decision information is provided. Finally, the experimental results show that the effectiveness of the model is validated in multiple game environments of Atari 2600.
Proactive Data-driven UAV State Estimation via Online End-to-end Learning

In this paper, we study two fundamental problems in UAV state estimation, namely, data-driven dynamics modeling and data-driven Kalman filter (KF) to achieve high estimation accuracy via harnessing online flight data. First, a long short-term memory (LSTM)-based recurrent neural network (RNN) is proposed to address the mis-modeled system and the hidden latent state in the (accurate) modeling of the system state transition. More precisely, the RNN-based system modeling is designed to capture higher-order dynamics due to, e.g., the impact of aerodynamics and actuator dynamics. To address the need for learning noise parameters in KF, e.g., noise covariances, in obtaining more accurate UAV state estimation, we propose online machine learning based algorithms that learn the noise parameters automatically (minimizing the residual prediction error and maximizing the prediction likelihood) from the flight data. We further propose an online end-to-end learning method for proactive UAV state estimation by integrating refined UAV dynamics using the LSTM-based RNN state transition model and KF-based state estimation with active noise parameters learning. Both the LSTM-based RNN model and the noise parameters are updated based on the online flight data. The performance of the proposed UAV state estimation is evaluated based on two metrics using a pre-collected evaluation dataset. The root-mean-square (RMS) state prediction errors of the position and orientation using the online end-to-end learning method are 1.4% and 1.6% via minimizing the residual prediction error, and are 1.2% and 1.5% via maximizing the prediction likelihood. The proposed method outperforms the traditional methods in the absence of data-driven dynamics modeling and KF-based state estimation.
Automatic Dataset Expansion With Structured Feature Learning for Human Lying Pose Detection

In this study, we developed a framework to localize human lying poses by a camera positioned above. Our framework is motivated by the fact that detecting lying poses is fundamentally more difficult than detecting pedestrians or localizing nondeformable objects such as cars, roads, and buildings due to the large number of poses, orientations, and scales that a human lying on the ground can take. An important problem with lying pose detection is the training dataset, which hardly accounts for each possible body configuration. As a solution, we propose a geometric expansion procedure that uses a virtual camera to increase the number of training images. We also use a Gibbs sampler to generate more training samples in the feature space on which the system can train its model. Once the training is completed, detection is performed on a multiscale and multirotational space. Because our framework accommodates a variety of object detection systems, we report the results for the Faster R-CNN, FPN, and RefineDet models. The results show that using automatic dataset expansion models systematically improves the results.
On the Study of Lithosphere Temperature from Electromagnetic Sounding Results

The article presents a method of calculating of the lithospheric temperature dependence vs depth on the base of a-priori information of electrical resistivity change vs depth (based on the results of electromagnetic soundings), the composition of rocks at depth (based on geological assumptions) and with the use of laboratory data of electrical properties of dry rocks at high temperature. It is assumed that at a depth of more than 10–15 km, the resistivity of the lithosphere is mainly determined by the composition of the rocks and the effect of temperature. The solution of the problem is illustrated by the example of calculations performed for two types of geoelectrical section of the Fennoscandian shield - “normal” and “anomalous”. Information on geoelectrical sections was obtained from the results of electromagnetic soundings with industrial power lines (FENICS experiment). The deep geological structure of the lithosphere is assumed to be the same in “normal” and “anomalous” areas. The maximum temperature difference within these two areas was about 70 °C in the depth range of 30–60 km.
An Effective BI-encoded Schema for Mention Extraction

We present a neural-encoded mention-hypergraph (named as NEMH in this paper) model for mention-extraction and classification in this paper. Through extraction of textual mention entities, a model is proposed that applies a hypergraph-encoding schema to neural networks. Comparing the results of the proposed model with the previous approaches, the proposed model can thus identify unlimited-length nested mention entities, which is a major milestone in the field. Several experiments are conducted on many datasets used in the baseline approaches, and the obtained results indicated that the designed model has high effectiveness compared to the existing models.
Exploring the Effectiveness of Cooperative Learning at Secondary Level in Bangladesh

In general, cooperative learning refers to the use of small groups of students to encourage them to&#x0D; work together and help each other to learn, accomplish learning goals, and increase their learning&#x0D; potential. This study attempts to explore the effectiveness of cooperative learning at secondary level&#x0D; classroom learning and students' achievement in particular. It also compares the achievements in&#x0D; means of scores between a student group taught using cooperative learning strategies and another&#x0D; group taught using non-cooperative learning strategies. This study is designed following a quasiexperimental&#x0D; research&#x0D; technique.&#x0D; 60&#x0D; students&#x0D; from&#x0D; a&#x0D; secondary&#x0D; school&#x0D; of&#x0D; class&#x0D; 9&#x0D; taking&#x0D; Science&#x0D; from&#x0D; &#x0D; 2&#x0D; &#x0D; different sections were selected as samples and were divided into equal number experimental&#x0D; group and control group for this study. A pretest was conducted before administering the&#x0D; cooperative learning technique which was followed by a post-test after on to determine the&#x0D; effectiveness in the form of learning achievement. Students' achievements were measured through a&#x0D; self-constructed achievement test which was used in both the pretest and post-test. The data were&#x0D; analyzed through t-test using SPSS. The comparison of these results indicated a meaningful&#x0D; statistical difference between the two groups, and it was also found from the teachers that students&#x0D; who work in the cooperative learning groups were more engaged, more responsible in completing&#x0D; group assignments and more organized while working in their respective groups. Therefore, in&#x0D; order to promote quality learning, the results of this study may encourage the practice of cooperative&#x0D; learning for the secondary school level. To make the learning of students more enjoyable, effective,&#x0D; and sustained at secondary level; schools and teachers need to use the cooperative learning&#x0D; technique in the classrooms
When “Natural Nouns” Surface as Verbs in Old Chinese: A Lexical Semantic Exploration

This paper reports a study of “natural nouns” that are used as verbs in Old Chinese, focusing on the lexical semantic analysis of natural nouns based on Generative Lexicon Theory. The detailed investigation of 39 natural nouns reveals that each of them encodes information of events as various types of Conventionalized Attributes (CAs) in their qualia structures, and is verbalized by activating or exploiting a particular type of CA and realizing this CA as the core meaning of the denominal verb. The discussion shows that the relative salience of a particular CA in the qualia structure largely determines the CA’s probability of triggering N-V conversion. Moreover, there is a clear tendency in CA exploitation: CAs encoding events with human participants are much more likely to be exploited by N-V conversion than CAs encoding events without human participants. This tendency could be accounted for by people’s anthropocentric view of the world.
Parallel Algorithm for Reduction of Data Processing Time in Big Data

Abstract Technological advances have allowed to collect and store large volumes of data over the years. Besides, it is significant that today’s applications have high performance and can analyze these large datasets effectively. Today, it remains a challenge for data mining to make its algorithms and applications equally efficient in the need of increasing data size and dimensionality [1]. To achieve this goal, many applications rely on parallelism, because it is an area that allows the reduction of cost depending on the execution time of the algorithms because it takes advantage of the characteristics of current computer architectures to run several processes concurrently [2]. This paper proposes a parallel version of the FuzzyPred algorithm based on the amount of data that can be processed within each of the processing threads, synchronously and independently.
A Distributed Deep Memory Hierarchy System for Content-based Image Retrieval of Big Whole Slide Image Datasets

Whole slide images (WSIs) are very large (30-50GB each in uncompressed format), multiple resolution tissue images produced by digital slide scanners, and are widely used by pathology departments for diagnostic, educational and research purposes. Content-based Image Retrieval (CBIR) applications allow pathologists to perform a sub-region search on WSIs to automatically identify image patterns that are consistent with a given query patch containing cancerous tissue patterns. The results can then be used to draw comparisons among patient samples in order to make informed decisions regarding likely prognoses and most appropriate treatment regimens, leading to new discoveries in precision and preventive medicine. CBIR applications often require repeated, random or se- quential access to WSIs, and most of the time the images are preprocessed into smaller tiles, as it is infeasible to bring the entire WSI into the memory of a computer node. In this study, we have designed and implemented a distributed deep memory hierarchy data staging system that leverages Solid-State Drives (SSDs) and provides an illusion of a very large memory space that can accommodate big WSI datasets and prevent subsequent accesses to the file system. An I/O intensive sequential CBIR workflow for searching cancerous patterns in prostate carcinoma datasets was parallelized and the I/O paths were altered to include the proposed memory system. Our results indicate that the parallel performance of the CBIR workflow improves and our deep memory hierarchy, staging framework produces negligible overheads for the application performance even when the number of staging servers and their memory sizes are limited.
Applying the Autonomous Anthology Compilation Algorithm Using the Inductive Reasoning Method

Recently, the extensive research activity is aimed at creating a semantic network as a future form of the Internet. Web search as a key technology of the Internet is evolving into a new form of search on the semantic Internet. A promising approach to such a semantic search on the Internet is based on combining standard search engines as the main output of semantic search results. In this article, we propose an improved approach to semantic search. This, in particular, is the ability to handle inconsistencies, noise, and incompleteness that are likely to occur in distributed and heterogeneous environments such as the Internet.
Use of Second-Order Statistics in Texture Synthesis-by-Analysis

Synthesis-by-analysis is a well established paradigm for reproduction of textures having random appearance. In this contribution, in order to simplify the overall procedure in the case of colored textures, we exploit the second-order correlation existing between the texture chrominance and luminance components. Specifically, we perform the synthesis exclusively on the luminance component, while the chrominance components are reproduced by applying properly designed Wiener filters, whose coefficients have been previously calculated in the analysis stage. This approach yields excellent results especially on natural textures, which intrinsically present a high degree of correlation between the luminance and chrominance components. Moreover, to enhance the synthesis resolution, the same paradigm is then applied to reproduce high-order components of a texture multi-resolution representation based on the Circular Harmonic Functions expansion.
The principles of the formation of personnel academic strategies in Belarus

The article presents the results of a scientific study of the current state of the motivational component of academic workers in the Republic of Belarus. The trends of academic behavior of the faculty of Belarusian universities are determined. The factors affecting the academic motivation of teachers are identified. The analysis and evaluation of academic strategies for the development of universities in the socio-economic conditions of Belarus is presented, the characteristics of the factors and principles affecting the formation of personnel strategies are given.
Matters of Logic

Since STT is a logical theory, its relation to logic are very close. The task of this chapter consists in presenting logical concepts and theories relevant for the further discussion of STT. Particular sections are devoted to propositional calculus, first-order logic, metalogic, definitions of logic (the universality of logic is its essential attribute) and historical notes on metalogic and metamathematics.