Reviewerly/exHarmony huggingface dataset:
CASE: Context-Aware Semantic Expansion

In this paper, we define and study a new task called Context-Aware Semantic Expansion (CASE). Given a seed term in a sentential context, we aim to suggest other terms that well fit the context as the seed. CASE has many interesting applications such as query suggestion, computer-assisted writing, and word sense disambiguation, to name a few. Previous explorations, if any, only involve some similar tasks, and all require human annotations for evaluation. In this study, we demonstrate that annotations for this task can be harvested at scale from existing corpora, in a fully automatic manner. On a dataset of 1.8 million sentences thus derived, we propose a network architecture that encodes the context and seed term separately before suggesting alternative terms. The context encoder in this architecture can be easily extended by incorporating seed-aware attention. Our experiments demonstrate that competitive results are achieved with appropriate choices of context encoder and attention scoring function.
Social And Cultural Transformation Of Teacher Training Tools Under Digitalization Conditions

The article analyzes basic cultural characteristics of the younger generation referred to as a Z generation, main vectors of transformation of teacher training tools under digitalization of educational processes. The sociocultural approach examines the phenomena in the context of the unity of culture and sociality, reveals factors influencing education, including factorial effects of education. The leading ideas of the approach and its specificity are determined by three dimensions of human existence: man interacting with society, culture and sociality. The article analyzes basic categories of the sociocultural approach: a person as a bearer of interaction; society as a set of interacting individuals; culture as a set of knowledge, values and norms. The research aims to identify features of perception of information by a new generation. These qualities are decisive for designing new competencies of teachers and determining methods. Teacher training tools were developed in the following interrelated aspects: transformation of worldview features of the “digital” generation affecting cognitive and information perception processes; transformation of professional qualities of the teacher in the conditions of digitalization; transformation of methods, means and forms of the educational process aimed at maximizing efficiency of the digital generation. These data suggest that the sociocultural approach allows us to consider the process of designing pedagogical tools as transformation of the worldview of young people and identification of the most effective means for developing a professional worldview. This unity is achieved by synthesizing social and cultural analysis, subject and object sides of the digital culture development.
Joint Recognition of Names and Publications in Academic Homepages

Academic homepages are an important source for learning researchers' profiles. Recognising person names and publications in academic homepages are two fundamental tasks for understanding the identities of the homepages and collaboration networks of the researchers. Existing studies have tackled person name recognition and publication recognition separately. We observe that these two tasks are correlated since person names and publications often co-occur. Further, there are strong position patterns for the occurrence of person names and publications. With these observations, we propose a novel deep learning model consisting of two main modules, an alternatingly updated memory module which exploits the knowledge and correlation from both tasks, and a position-aware memory module which captures the patterns of where in a homepage names and publications appear. Empirical results show that our proposed model outperforms the state-of-the-art publication recognition model by 3.64% in F1 score and outperforms the state-of-the-art person name recognition model by 2.06% in F1 score. Ablation studies and visualisation confirm the effectiveness of the proposed modules.
Ensemble forecasting for electricity consumption based on nonlinear optimization

Accurate electricity power demand forecasting can provide scientific decision-making basis for policy making and planning implementation and the electricity-generating target. In this paper, a novel ensemble forecasting model with nonlinear optimization is proposed to predict the demand of electricity. The results of basic forecasting models including exponential smoothing, ARIMA, SVR and extreme learning machine are integrated. Taking clean electricity demand of world’s major regions as sample, the results reveal that the ensemble approach performs much better than the single and average integrated models in terms of the accuracy.
MANAGEMENT OF FLOOD HAZARD AREAS IN PASAMAN RIVER BASIN OF WEST PASAMAN REGENCY WEST SUMATRA PROVINCE

Floods have caused many losses to human life. Indonesia has around 30 percent of its area is flood hazard. If the disaster hazard areas occupied by the low capacity of society, this condition will cause a high risk of disaster. Arranging the river basin management policy is one of the flood mitigation efforts. This study aims to determine the arrangement of river basin management policy in Pasaman. Geographic Information System (GIS) approach is used to determine the flood hazard areas in the Pasaman river basin. Indicators in determining flood hazard areas are slope, rainfall, land type, land use, and landform. In arranging the policy directives of the Pasaman river basin management, this research used Interpretative Structural Modeling (ISM) approach. In setting the directives policy, twenty experts from relevant stakeholders work together. The results show that around 25 percent of river basin areas in Pasaman is a high flood hazard risk areas. The result also set a priority to reduce sedimentation in rivers in the Pasaman river basin management policy.
Detection of Malaria Diseases with Residual Attention Network

To describe a model using classic machine learning techniques for creating machine learning systems, a person who specializes in this technique needs to extract feature vectors.This period also breaks into expert time.Also, these methods could not process raw data without preprocessing and expert assistance.Deep learning has made great progress in solving problems at this point, and machine learning research has continued for many years.Unlike traditional machine learning and image processing techniques, deep networks enable learning processes using raw data.In this study, a deep learning approach for the classification and diagnosis of malaria is developed.For this purpose, Residual Attention Network (RAN) a deep learning Convolutional Neural Network (CNN) technique was used with previously classified datasets.The goal is to design computer-aided software for classifying blood cell images (blood samples) as "parasitized" or "uninfected".In the program, a decision support system was implemented by a deep learning approach.As a result, the RAN model achieved the best ability to produce better results in processing and classification images compared to other algorithm types.RAN model's training simulation results showed a 95.79% classification accuracy rate.Using the Support Vector Machine (SVM) obtained only 83.30% classification accuracy rate.Besides, it is evaluated that for the classification of blood cell images and diagnosis of malaria using deep learning methods can be used successfully.In addition, deep learning methods have the advantage of automatically learning features from input data and require minimal input by specialists in automated malaria diagnosis.
A comprehensive study on eye issues and modern developments in visual rehabilitation for people with impaired vision

The eye is an important part of the human body and is a window that reveals the outside world to mankind.The eye is divided into anterior and posterior sections, each of which contains specific features and diseases.One of the most important problems with the human eye is blindness that can have hereditary or acquired causes.Many diseases, including fetal measles, retinopathy in premature infants, microphthalmosis, cataracts, etc. can cause blindness, and diabetes is one of the most important causes of blindness in the world.In this review study, we describe and review the features and components of the eye structure, familiarity with the concept of blindness, the diseases that cause blindness, and systems designed for blind use such as the white cane, the braille line, the finger reader technology, the guiding dogs, the money recognition device, the vibrotac technology, etc.
Geometry-Constrained Car Recognition Using a 3D Perspective Network

We present a novel learning framework for vehicle recognition from a single RGB image. Unlike existing methods which only use attention mechanisms to locate 2D discriminative information, our work learns a novel 3D perspective feature representation of a vehicle, which is then fused with 2D appearance feature to predict the category. The framework is composed of a global network (GN), a 3D perspective network (3DPN), and a fusion network. The GN is used to locate the region of interest (RoI) and generate the 2D global feature. With the assistance of the RoI, the 3DPN estimates the 3D bounding box under the guidance of the proposed vanishing point loss, which provides a perspective geometry constraint. Then the proposed 3D representation is generated by eliminating the viewpoint variance of the 3D bounding box using perspective transformation. Finally, the 3D and 2D feature are fused to predict the category of the vehicle. We present qualitative and quantitative results on the vehicle classification and verification tasks in the BoxCars dataset. The results demonstrate that, by learning such a concise 3D representation, we can achieve superior performance to methods that only use 2D information while retain 3D meaningful information without the challenge of requiring a 3D CAD model.
Video Stabilization for Camera Shoot in Mobile Devices via Inertial-Visual State Tracking

Due to the sudden movement during the camera shoot, the videos retrieved from the hand-held mobile devices often suffer from undesired frame jitters, leading to the loss of video quality. In this paper, we present a video stabilization solution in mobile devices via inertial-visual state tracking. Specifically, during the video shoot, we use the gyroscope to estimate the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rotation</i> of camera, and use the structure-from-motion among the image frames to estimate the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">translation</i> of camera. We build a camera projection model by considering the rotation and translation of the camera, and the camera motion model to depict the relationship between the inertial-visual state and the camera's 3D motion. By fusing the inertial measurement (IMU)-based method and the computer vision (CV)-based method, our solution is robust to the fast movement and violent jitters, moreover, it greatly reduces the computation overhead in video stabilization. In comparison to the IMU-based solution, our solution can estimate the translation in a more accurate manner, since we use the feature point pairs in adjacent image frames, rather than the error-prone accelerometers, to estimate the translation. In comparison to the CV-based solution, our solution can estimate the translation with less number of feature point pairs, since the number of undetermined degrees of freedom in the 3D motion directly reduces from 6 to 3. We implemented a prototype system on smart glasses and smart phones, and evaluated the performance under real scenarios, i.e., the human subjects used mobile devices to shoot videos while they were walking, climbing or riding. The experiment results show that our solution achieves 32 percent better performance than the state-of-art solutions in regard to video stabilization. Moreover, the average processing time latency is 32.6ms, which is lower than the conventional inter-frame time interval, i.e., 33ms, and thus meets the real-time requirement for online processing.
Transformational Leadership and Employees’ Creativity: A Multi-Mediation Model

This research aims to examine the relationship between transformational leadership and employees’ creativity through mediating role of trust and job autonomy. The study employed cross sectional survey method for the collection of data from 187 employees working in construction and banking sectors of Pakistan. The findings reveal that transformational leadership fosters employees’ creativity through trust and job autonomy. Both trust and job autonomy have statistically significant mediating effect. However, trust being the mediator, plays a more significant role in boosting up the transformational leadership and employees’ creativity relationship as compared to job autonomy. This research helps to know how transformational leadership enhances employees’ creativity by enlightening the mediating role of trust and job autonomy. Further, the findings of this research also help the managers to understand and create such environment which enhances employees’ creativity by focusing on the factors identified in this study. This research contributes to advance the literature on the mediating role of trust and job autonomy in describing the relationship of transformational leader and employee’s creativity and highlighting that trust plays a more important role to enhance creativity in contrast to job autonomy. Further, this is the first attempt to enhance the employee creativity through transformational leadership style and the mediating role of both trust and job autonomy in Pakistan.
AUC Optimization with a Reject Option

Making an erroneous decision may cause serious results in diverse mission-critical tasks such as medical diagnosis and bioinformatics. Previous work focuses on classification with a reject option, i.e., abstain rather than classify an instance of low confidence. Most mission-critical tasks are always accompanied with class imbalance and cost sensitivity, where AUC has been shown a preferable measure than accuracy in classification. In this work, we propose the framework of AUC optimization with a reject option, and the basic idea is to withhold the decision of ranking a pair of positive and negative instances with a lower cost, rather than mis-ranking. We obtain the Bayes optimal solution for ranking, and learn the reject function and score function for ranking, simultaneously. An online algorithm has been developed for AUC optimization with a reject option, by considering the convex relaxation and plug-in rule. We verify, both theoretically and empirically, the effectiveness of the proposed algorithm.
Medical Image Encryption Using Modified Identity Based Encryption

The development of technology and communication also affects the level of security needed for digital image transmission. It is known that digital images now have important meanings in both communication and video conference. In this paper, we propose a security method for medical encryption in the form of images. The proposed method is implemented in the modified Identity-Based Encryption scheme. The encryption algorithm used is Elliptic Curve Cryptography (ECC) to generate key pairs and the Advanced Encryption Standard (AES) to generate symmetric keys and encrypt process. This method has been tested based on computation time, histogram analysis and statistical analysis. The results of the test were obtained that the proposed method was resistant to multiple attacks despite having slower computing time. The proposed compute time error percentage is 1.69% for key generator stages and 0.07% for total compute time at the encrypt-decrypt stage.
Analysis of negative flow of gravitational waves

In this article, we made the mathematical explanation of the anti-gravitational waves, by the inspiration that we got from the observed positron in cosmic rays. Then, we analyzed the mathematical difference between positive and negative flows of gravitational waves; and we calculated the spin of the negative flow of gravitational waves, which is used to stabilize the movement of the waves. In the mathematical formulas we found that positive and negative flows move in opposite directions from each other; therefore, if we see the spin (rotation) of the waves from the planet that emits the waves, the positive flow rotates anti-clockwise, while the negative flow rotates clockwise. We also investigated the possible origin of gravitational waves, and concluded that the negative flow can occur when the positive flow appears, leaving holes behind, in the gravitational field, which is trig-gered by the movements of a large mass of the planet.
Productivity and Predictability for Measuring Morphological Complexity

We propose a quantitative approach for quantifying morphological complexity of a language based on text. Several corpus-based methods have focused on measuring the different word forms that a language can produce. We take into account not only the productivity of morphological processes but also the predictability of those morphological processes. We use a language model that predicts the probability of sub-word sequences within a word; we calculate the entropy rate of this model and use it as a measure of predictability of the internal structure of words. Our results show that it is important to integrate these two dimensions when measuring morphological complexity, since languages can be complex under one measure but simpler under another one. We calculated the complexity measures in two different parallel corpora for a typologically diverse set of languages. Our approach is corpus-based and it does not require the use of linguistic annotated data.
CFGNN: Cross Flow Graph Neural Networks for Question Answering on Complex Tables

Question answering on complex tables is a challenging task for machines. In the Spider, a large-scale complex table dataset, relationships between tables and columns can be easily modeled as graph. But most of graph neural networks (GNNs) ignore the relationship of sibling nodes and use summation as aggregation function to model the relationship of parent-child nodes. It may cause nodes with less degrees, like column nodes in schema graph, to obtain little information. And the context information is important for natural language. To leverage more context information flow comprehensively, we propose novel cross flow graph neural networks in this paper. The information flows of parent-child and sibling nodes cross with history states between different layers. Besides, we use hierarchical encoding layer to obtain contextualized representation in tables. Experiments on the Spider show that our approach achieves substantial performance improvement comparing with previous GNN models and their variants.
Image Registration Method Based on Optimized SURF Algorithm

In order to solve the time consuming problem of image registration based on the traditional SURF algorithm, the image registration method based on the optimized SURF algorithm is proposed.Firstly, the image corner points are extracted by the Shi-Tomasi algorithm, then, the SURF algorithm is used to generate the corner point descriptors and the sparse principle algorithm is used to reduce the dimension of the corner point descriptors.Finally, the bidirectional matching algorithm is used to match.Through the experimental data analysis, the image registration method based on the optimized SURF algorithm is nearly the same in image registration accuracy in comparison with the traditional SIFT algorithm, the traditional SURF algorithm and the other four optimized algorithms, but the time consuming of image registration is decreased by 79.09%, 47.74%, 66.25%, 50.79%, 21.43% and 5.13%, respectively, verifying the instantaneity and effectiveness of the algorithm.
PV POWER PLANTS SITES SELECTION USING GIS-FAHP BASED APPROACH IN NORTH-WESTERN MOROCCO

Abstract. Energy plays a crucial role in the economy of any country. One of the most recent trends in global development is the transition to "green” growth. Morocco is keeping pace with its growth by promising to increase renewable energy capacity to 42% of total installed capacity by 2020 and to 52% by 2030. This study develops a framework that aims to determine the optimal areas for deploying photovoltaic (PV) installation in two stages. The first stage aims at excluding the undesirable areas such as forests, rivers and agricultural land. The second stage consists in defining the suitability of sites based on seven criteria; solar irradiation, land surface temperature, slopes, slope orientation, distance from power lines, distance from main roads, distance of urban area. In this study we are using fuzzy logic and fuzzy membership functions in order to create criteria layers in the environment of Geographic Information System (GIS) that allowing the integration of a Multi-Criterion Decision Analysis (MCDA) to identify the best sites to deploy PV solar power plants in north-western Morocco. Also, the Analytic Hierarchy Process (AHP) technique is used to determine weights for each one of the criteria. Results obtained from the spatial approach shows that the major portion of the studied area was judged inappropriate for solar farms installations. Also, it shows that only 5.11% (8500 Ha) of the territory demonstrate high suitability for PV solar installations located in the southern part of our studied area with a potential of electricity generation, from the areas with high suitability level, assuming a PV system efficiency of 6.48%; equivalent to 5.39 Twh/year with an installed capacity of 4.02 GW and an annual carbon emission reduction of 3 697.54 Kt-CO2/year. The resulting suitability map can be used as a decision support tool to help the public policy makers to integrate green energy into their policies.
Robust Image Watermarking in the Print-Cam Process

Nowadays with the popularity of smartphones, it is increasingly valuable to capture side information in print media through phone cameras. The print-cam process produces serious attacks during capturing images, especially geometric distortions. In this paper, we propose a robust image watermarking method for the print-cam process, which is also evaluated on some different smartphones. In the embedding process, the method uses watermarking based on Discrete Cosine Transform (DCT). We introduce Scale-Invariant Feature Transform (SIFT), the local feature point detection techniques, to restore the geometric distortions, and then the reverse process of embedding is carried out to extract the watermark. The results show that the proposed method is considerably robust in the print-cam process. Meanwhile, the method is suitable for a wide range of smartphones and acceptable for real-life applications such as ID card verification, anti-counterfeit traceability, and medicine authentication.
Grey Wolf Optimizer (GWO) Algorithm to Solve the Partitional Clustering Problem

The clustering which is an unsupervised classification method is very important for data processing applications.The main purpose of the clustering is to separate the data samples into different groups by using the similarity (or dissimilarity) between data samples.There are many conventional and heuristic algorithms which are used for the clustering problem.Nevertheless, in last years, it is seen that many new techniques are proposed and improved to solve the clustering problem.In this paper, grey wolf optimization (GWO) algorithm which is modelled according to the social behavior of grey wolves is applied to partition the data samples by searching the optimal center of the clusters.The clustering performance of the GWO is compared with the performances of the three clustering algorithms: k-means, kmedoids and fuzzy c-means algorithms.The experiments show that the GWO algorithm has generally better results than the other clustering algorithms and can be alternatively applied on the clustering problem.