Reviewerly/exHarmony huggingface dataset:
3D numerical modeling of flow and sediment transport in rivers and open channels

Numerical model is a useful tool in studying the flow and sediment transport, change in river bed and is built on solving governing differential equations. Numerical model has many different levels and three-dimensional model is the highest level, allowing detailed simulation of flow and sediment transport process in 3D space. The paper presents a method calculating three - dimensional flow and sediment transport in the open channel. Water level and flow velocity are solved from three-dimensional equations with hydrostatic hypothesis. Concentration of suspended sediment, bottom sediment and bottom evolution is solved from transport equations. The governing differential equations in the "sigma" transform coordinate system are solved by finite volume method on unstructured grid of quadrilateral elements. Boundary condition of water level or flow will be imposed on open boundary. For suspended sediment concentrations in the injected phase, suspended sediment concentrations are applied and the outflow phase applies free drainage conditions. This method of calculation was tested with the problem of curved channel sediment transport which was studied experimentally by Odgaard and Bergs. Calculation results are quite consistent with the measured data. In order to test the practical applicability, this method is also tested with the problem of sediment transport in Cu lao Pho islet on Dong Nai river. To solve the matter of hydraulic boundary condition of this problem, the model of Cu lao Pho islet is integrated into the Sai Gon - Dong Nai river system model. Results of the calculation of the river bed evolution of the Cu lao Pho islet on the Dong Nai river also show that this calculation method gives results consistent with the rule and can be used in practical research.&#x0D;
Students’ English Structure and Written Competence

This research aims to investigate the students’ English competence, the highest percentage of students’ mistake and their difficulties in responding to the TOEFL structure and written expression sections. All of the 6th semester students of the English Education Study Program of Universitas Bengkulu who had passed all structure courses were involved in this research. The data were collected through a TOEFL test and an interview. The results show that majority of the students’ English competence was poor. It also reveals that the most difficult topic in grammar section was redundancy. There were two factors that affected the students’ performance in redundancy, ambiguity and the interference of first language. In contrast, gerund and infinitives were the easiest topic in grammar section. Simple rules and the most common expression were contributed to the students’ performance in gerund and infinitives. In responding to structure and written expression section there were four factors that influenced students’ difficulties, poor understanding of grammar, less focus, lack of practice and limited vocabulary.
Active Recommendation Method Based Mobile Network Using Book Management Application

This paper suggests a mobile-based book recommendation method utilizing a book management application.It will help smart phones to recognize Barcodes and QR codes and collect information on books through the open APIs.Such information is divided into that of books already purchased and that of the books the user would like to buy.This divided information is stored in an application, while the server measures the similarity among users in terms of their information stored in the database created.Then, if it is found that users with similar preferences do not have certain books, recommendations for the books will be made through push notification; the users receiving the recommendations get the book details directly from the server.To evaluate the performance of the proposed method, 300 users were selected as subjects and the success rate was 85%.Therefore, the proposed method will be useful in a mobile-based recommendation system.
Analysis of the effects of scrambling and diffusion of logistic chaotic map on image encryption

The image encryption algorithm based on chaotic system has been widely used at present. For this kind of algorithm, the encryption mechanism of "scrambling - diffusion" and multiple iterations are mainly adopted. In this paper, the common one-dimensional logistic chaotic mapping is taken as an example to analyze the influence of "scrambling - diffusion" and the number of iterations on the encryption effect. The simulation results show that, when only the chaotic system is used to complete the encryption operation, except for the key space, the contribution of "scrambling - diffusion" and single diffusion operation to the image encryption effect is basically the same, and the number of iterations has no significant effect on the encryption. When a "scrambling - diffusion" mechanism and multiple iterations are employed, the encryption effect may be enhanced by combining other cryptographic operations.
Modeling User Contextual Behavior Semantics with Geographical Influence for Point-Of-Interest Recommendation

Point-Of-Interest (POI) recommendation assists users to find their preferred places and helps businesses to attract potential customers.However, the data sparsity and the complexity of user check-in behavior pose a big challenge to POI recommender systems.To tackle this challenge, we propose a POI recommendation method named HeteGeoRankRec based on user contextual behavior semantics.First, to mine the fine-grained user behavioral features, we employ the meta path of Heterogeneous Information Network (HIN) to represent the complex semantic relationship among users and POIs and integrate the context constraints (such as time and weather) into the meta paths.Secondly, we propose a weighted matrix factorization model considering the influence of geographical distance to obtain semantic preference through the user-POI semantic correlativity matrixes generated by multiple meta paths.Finally, we introduce a ranking-based fusion method, which unifies the recommendation results of different meta paths as the final preference of users.Experiments on the real data collected from Foursquare show that HeteGeoRankRec has the better performance than the state-ofthe-art baselines.
Velocity Constrained Trajectory Generation for a Collinear Mecanum Wheeled Robot

While much research has been conducted into the generation of smooth trajectories for underactuated unstable aerial vehicles such as quadrotors, less attention has been paid to the application of the same techniques to ground based omnidirectional dynamically balancing robots. These systems have more control authority over their linear accelerations than aerial vehicles, meaning trajectory smoothness is less of a critical design parameter. However, when operating in indoor environments these systems must often adhere to relatively low velocity constraints, resulting in very conservative trajectories when enforced using existing trajectory optimisation methods. This paper makes two contributions; this gap is bridged by the extension of these existing methods to create a fast velocity constrained trajectory planner, with trajectory timing characteristics derived from the optimal minimum-time solution of a simplified acceleration and velocity constrained model. Next, a differentially flat model of an omnidirectional balancing robot utilizing a collinear Mecanum drive is derived, which is used to allow an experimental prototype of this configuration to smoothly follow these velocity constrained trajectories.
Cybercrime and the policing of politics in Tanzania

This chapter explores the policing of social media in Tanzania. It argues that understanding the impact of increased social media use on politics in Africa requires consideration of the ways in which policing and politics are connected. Policing of social media use and content can be seen as part of a long history of police involvement in politics in Tanzania and elsewhere in the region.
Double-Loop Wavefront Tilt Correction for Free-Space Quantum Key Distribution

Both free-space and optical fiber quantum key distribution (QKD) links will coexist in future quantum networks, requiring efficient coupling between both types of channels. However, wavefront distortions introduced by the atmospheric channel can severely affect this efficiency. Active mechanisms such as precise beam tracking or steering can correct for some of these wavefront distortions, considerably improving the signal-to-noise ratio of the received quantum signal in many scenarios. A tracking system that uses two, instead oftypically one, controlling loops for tilt correction, stabilizes the beam in the whole optical axis of the receiver relaxing the restrictions of the receiver's optical design, and reduces the area of beam fluctuations in the receiver's focal plane a 24% more than a single-loop configuration. The tracking system was characterized in a QKD system at a 300 meter-link in moderate to strong turbulent conditions (C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> - 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-14</sup> - 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-13</sup> m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2/3</sup> ) and an improved coupling efficiency of a factor of 2.1 and 1.6 was obtained for a 9.5 μm-core diameter standard telecommunications Single Mode Fiber (SMF) and a 25 μm-core diameter multimode fiber (MMF), respectively. This reduces the quantum bit error rate (QBER) caused by solar background photons in a 52 % for the former and 39% for the latter, enabling an increase in the secret key rate ofmore than one order ofmagnitude for SMF and a factor of five for MMF. These results are promising for enabling QKD free-space links and their interconnection to fiber optic infrastructure in realistic scenarios of communication networks of high turbulence regimes and daylight conditions.
Exploration and evaluation of efficient pre-processing and segmentation technique for breast cancer diagnosis based on mammograms

Breast cancer is the second leading cause of death for women everywhere in the world. Since the reason behind the disease remains unknown, early detection and diagnosis is the key challenge for breast cancer control. In this work, mammogram images are initially subject to pre-processing using Laplacian filter for enhancement of tumour regions, Gaussian mixture model, Gaussian kernel FCM, Otsu global thresholding and FCM technique are employed for segmentation. Further, the efficiency of segmentation techniques is analyzed by classifying the samples into benign, malignant and healthy using Gray Level Co-occurrence Matrix (GLCM) features. Linear discriminant analysis classifier is used a combination based on which efficiency used for classification of mammograms. Ensemble methods are evaluated. The efficiency has resulted in better accuracy with the ensemble-based method. The experimentation is conducted in the mini MIAS database of mammograms, and the efficiency of the linear discriminant analyzer is found to be 89.19% for GKFCM, 83.78% with Otsu and 78.38% with FCM method with GLCM features.
A Vision-based Unmanned Aircraft System for Autonomous Grasp &amp; Transport

The progress in sensor technologies, computer capabilities and artificial intelligence has endowed the unmanned aircraft system (UAS) with more autonomous abilities. Motivated by the 6th International Unmanned Aerial Vehicle Innovation Grand Prix (UAVGP), a UAS with high degree of autonomy was developed to perform the mission of building a simulated tower using prefabricated components. According to the requirement of the competition, the UAS was designed and implemented from the following four parts: 1) navigation and control, 2) recognition and location, 3) grasp and construction, and 4) task planning and scheduling. Different levels of autonomy have been given to the UAS based on these parts. The system hardware was developed on a quadrotor platform by integrating various components, including sensors, computers, power and grasp mechanism. Software which included precise navigation, mission planning, real-time perception and control was implemented and integrated with the developed UAS hardware. The performance in the test environment and actual competition showed that the UAS could perform the mission without human intervention with high autonomy and reliability. This paper addresses the major components and development process of the UAS and describes its application to the practical mission.
Applications of Extreme Learning Machines

Presents the introductory editorial for this issue of the publication.
User-guided Rendering of Audio Objects Using an Interactive Genetic Algorithm

Object-based audio allows for personalization of content, perhaps to improve accessibility or to increase quality of experience more generally.This paper describes the design and evaluation of an interactive audio renderer, which is used to optimize an audio mix based on the feedback of the listener.A panel of 14 trained participants were recruited to trial the system.The range of audio mixes produced using the proposed system was comparable to the range of mixes achieved using a traditional fader-based mixing interface.Evaluation using the System Usability Scale showed a low level of physical and mental burden, making this a suitable interface for users with impairments, such as to vision and/or mobility.
Runtime Analysis of Pigeon-Inspired Optimizer Based on Average Gain Model

The pigeon-inspired optimization (PIO) algorithm is a novel swarm intelligence optimizer inspired by the homing behaviors of pigeons. Although PIO has demonstrated effectiveness and superiority in numerous fields, there are few results about the theoretical foundation of PIO. This paper employs the average gain model to estimate the upper bound for the expected first hitting time of PIO in continuous optimization. The case study and experiment result indicate that our theoretical analysis is applicable to the general case where the population size and problem size are both larger than 1, which is close to the practical situation.
Central Actor Identification of Crime Group using Semantic Social Network Analysis

The Police as law enforcers who authorize in terms of social protection are expected to do both the prevention and investigation efforts also the settlement of criminal cases that occurred in the society. This research can help police to identify the main actor faster and leads to solving crime-cases. The use of overall centrality is very helpful in determining the main actors from other centrality measures. The purpose of this research is to identify the central actor of crimes done by several people. Semantic Social Network Analysis is used to perform central actor identification using five centrality measurements, such as degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, and overall centrality. As for the relationship between actors, this research used social relation such as friendship, colleague, family, date or lover, and acquaintances. The relationship between actors is measured by first four centrality measures then accumulated by overall centrality to determine the main actor. The result showed 80.39% accuracy from 102 criminal cases collected with at least 3 actors involved in each case.
Automatic Acrostic Couplet Generation with Three-Stage Neural Network Pipelines

As one of the quintessence of Chinese traditional culture, couplet compromises two syntactically symmetric clauses equal in length, namely, an antecedent and subsequent clause. Moreover, corresponding characters and phrases at the same position of the two clauses are paired with each other under certain constraints of semantic and/or syntactic relatedness. Automatic couplet generation is recognized as a challenging problem even in the Artificial Intelligence field. In this paper, we comprehensively study on automatic generation of acrostic couplet with the first characters defined by users. The complete couplet generation is mainly divided into three stages, that is, antecedent clause generation pipeline, subsequent clause generation pipeline and clause re-ranker. To realize semantic and/or syntactic relatedness between two clauses, attention-based Sequence-to-Sequence (S2S) neural network is employed. Moreover, to provide diverse couplet candidates for re-ranking, a cluster-based beam search approach is incorporated into the S2S network. Both BLEU metrics and human judgments have demonstrated the effectiveness of our proposed method. Eventually, a mini-program based on this generation system is developed and deployed on Wechat for real users.
ADVENT OF AUGMENTED REALITY EXPERIENCE IN RETAIL AND ONLINE SHOPPING AND ITS INFLUENCING SIGNIFICANCE IN FUTURE

Augmented Reality (AR) is a trending technology that augments or superimposes an image generated by a computer system virtually into the real world environment for the user’s viewpoint using a smart phone or other hand held devices. AR shows recent advancements in the shopping domain with various implementation trails and refinement. The simplicity and flexibility in online shopping where people stay in their own place and do shopping brought a great challenge to retail shopping environment today. Retail stores are now struggling a lot to bring in the customers and the foot traffic has been greatly reduced due to which online sales are boosting and retail sales are stalling. This necessitates to bring new technological innovations to offline shopping to attract people. With the use of AR, it is possible to merge digital component to physical products in the store to stimulate the engagement of the shopping experience with more fun and joy. On the other hand, in the online shopping, though user reviews and product showcase aids the customers to analyze the quality, look and feel of diverse products, the buyer still cannot see how exactly the product fits in a real environment or how it works. Here plays AR a vital role in online shopping where it uses animations and visualization techniques to offer more value to their shoppers virtually aiding to see exactly the look of the product in user environment. This paper explains the advancement of AR in both retail and online shopping of various product domains with an implementation model of ShopAR for Online shopping and AR significance in near future.
Exposure Fusion for Dynamic Scenes Combining Retinex Theory and Low-Rank Matrix Completion

Exposure fusion (EF) directly generates a low dynamic range (LDR) image with similar image quality to the high dynamic range (HDR) image by combining images of different exposures to overcome the limited dynamic range of common digital cameras. EF for dynamic scenes is challenging not only due to the difficulty in removing various ghost artifacts but also because of the requirement of preserving details, especially in the saturated regions. Recently, low-rank (LR) matrix completion (LRMC) has been shown to be efficient in separating the latent background from the sparse motion in the irradiance domain. However, the performance of LRMC models strongly relies on the estimated irradiance images. To address this problem, this paper proposes a novel EF method combining Retinex theory and the LRMC model. The proposed method consists of four steps. First, according to Retinex theory, an image is decomposed into illumination and reflection components. Second, the LRMC model is applied to the reflection component to generate the background reflection component and the sparse error. Third, the motion map is modeled as a Markov random field (MRF), integrating the sparse error with the ordering constraint across all the illumination components. Finally, all the illumination components are fused via a pyramid-based method, where the weight maps are defined based on the obtained motion map and illumination. The experimental results show that the proposed method outperforms the state-of-the-art methods particularly in preserving details in saturated regions.
Aspect-based sentiment analysis based on dynamic attention GRU

引言特定目标情感分类 (aspect-based sentiment analysis, ABSA) 是情感分析领域的基础任务 [1∼3]
Hybrid Handwriting Character Recognition with Transfer Deep Learning

Handwriting character recognition is useful and important in terms of allowing correct recognition and interpretation of all characters such as handwritten letters, numbers and figures. Deep convolutional neural networks have been used frequently for computer vision in recent years due to high performance in image processing, feature extraction and classification. A lot of sample data, processing power and time are needed to train CNNs. Transfer learning enables us to obtain specific CNNs for the classes we want by minimizing these needs In this work, firstly, different CNN models are trained with transfer learning by using NIST19 dataset with handwritten characters, and then a hybrid model is created by evaluating the results of each CNN and revealing the best value. As a result of the experiment carried out on the test data set, it is observed that a performance increase of 1.1% is achieved with the created model.
DESIGN AND IMPLEMENTATION OF AN ENGLISH LESSON BASED ON HANDWRITING RECOGNITION AND AUGMENTED REALITY IN PRIMARY SCHOOL

In recent years, the development of mobile technology and devices makes Artificial Intelligence (AI) and AugmentedReality (AR) available tools in classroom teaching and learning. AI and AR are used to improve the learning effect as wellas motivate
Towards an Adaptive Defuzzification: Using Numerical Choquet Integral

Fuzzy systems have been proven to be an effective tool for modeling and control in real applications. Fuzzy control is a well established area that is used in a large number of real systems. Fuzzy rule based systems are defined in terms of rules in which the concepts that define the rules (both in the antecedent and consequent) can be defined in terms of fuzzy sets. In applications, rules are fired and then a set of consequents need to be combined to make a final decision. This final decision is often computed by means of a defuzzification method. In this paper we discuss the defuzzification proces and propose the use of a Choquet integral for this process. In contrast with standard defuzzification methods which are based on mean operators (usually discrete), the Choquet integral permits us to have an output variable with values that have different importances and with interactions among the values themselves. To illustrate the approach, we use a numerical Choquet integral software for continuous functions that we have recently developed. We also position the application of the approach to handle the uncertainty associated to a mission-oriented Cyber-Physical System (CPS).