Reviewerly/exHarmony huggingface dataset:
Combining Methods to Establish Potential Management Measures for Invasive Species Elodea nutallii in Lough Erne Northern Ireland

Lough Erne (Northern Ireland) is a heavily modified water body in a transnational catchment, straddling Northern Ireland (in the U.K.) and the Republic of Ireland. The lake has a long history of human modification from hydro-electrification, eutrophication and the introduction of non-native species. Most recently the proliferation of the non-native pond weed Elodea nutalli has adverse implications for recreational users of the lake. In order to establish management measures which might be acceptable to a range of lake, a number of methods, using a mixture of disciplines were combined. Fuzzy cognitive mapping exercises combined with formal goal identification surveys were conducted to establish consensus on the main environmental problems and conflicts. GIS was used to visualise potential management scenarios. Management scenarios were costed and presented to lake users to establish the preferred measures. The overall process promoted discussion and awareness of different uses and user perspectives to enable development of consensus.
Formative Assessment in a Blended Learning English Course

At present, formative assessment is widely used since it seems to significantly improve student achievement results. Most recently, it has become an inseparable part of the so-called blended learning (BL), which currently combines the use of mobile learning and face-to-face instruction. The implementation of formative assessment in a BL environment is increasing because it can enhance the learning quality. Moreover, research reveals that 87% of the formative assessment in BL is conducted automatically because it provides more flexible, timely and prompt feedback to students. The purpose of this article is to demonstrate how the formative assessment in a blended learning English course taught at the Faculty of Informatics and Management of the University of Hradec Kralove, Czech Republic, can help students to improve their English proficiency. The methods include a literature review of available sources found on the research topic in the world’s acknowledged databases such as Scopus and Web of Science, a method of analysis of students’ performance in a mobile app, and a method of analysis of students’ final test results in order to illustrate the effectiveness of the formative assessment and discuss its implementation in the English course. The findings of the statistical analysis and students’ performance in the mobile app reveal that the formative assessment has a positive impact both on students’ learning and teacher’s instruction.
A lightweight attribute-based authentication system with DAA

Abstract In cloud computing environment, while preventing users from illegally accessing resources, various cloud service systems must provide protection for authorized users of their personal sensitive information. Attribute-based authentication (ABA) makes it possible to solve the above problems. An efficient ABA system was put forward by extending the direct anonymous attestation (DAA) scheme of Brickell et al. During the construction of the new system, the technique of lightweight ciphertext policy attribute-based encryption was adopted, and the online computing task of users was optimized. Compared with other similar systems, the new system is characterized by the use of trusted computing technology to improve the level of privacy protection of users, and the computational complexity of users in the authentication stage is independent of the size of access policy.
Seamlessly Depth-Enhanced VR Display for 360 Wild Images

Due to the limited range of depth sensors, the depth estimators for wild images were trained by ordinal data. Without the need of re-train, we proposed an approach to create seamlessly ordinal depth for 360 wild images. The 360 depth was applied to VR display for improving the virtual perception.
Metrics for Personal Profiles of Social Network Users

This paper discusses the technical details of obtaining and processing data to determine a set of characteristics of texts from social networks, genre preferences in movies and music genres for students of Kazan Federal University who have different academic performance (successful, average, not-successful). The selection of such characteristics is carried out using machine learning methods (Word2Vec, tSNE). The data obtained is used in the development of a functional psychometric model of cognitive behavioral predictors of an individual’s activity within the framework of their educational activities. We also developed a web application for visualizing the obtained data using the Flask engine.
Iterative Trajectory Optimization for Real-Time Motion Planner of Autonomous Driving

For autonomous driving, motion planning and motion control are two significant modules. In this paper, an iterative trajectory optimization (ITO) method is proposed to improve the motion controller's tracking performance and increase the physical and operational feasibility of the motion planning trajectory. Based on the proposed scheme, a feedback connection is built from the motion controller to the motion planner. Different from traditional motion controllers, the motion controller in the proposed scheme is divided into two sub-modules, iterative motion simulator, and motion control operator. In the iterative motion simulator, the vehicle trajectory response is simulated while updating a trajectory offset iteratively. This offset will correct the vehicle response closer to the reference trajectory. After the iterative trajectory adjustment finishes, the simulated tracking error and trajectory offset are sent back to motion planner. The motion planner will first evaluate the simulated trajectory in terms of the response effectiveness, and then send the trajectory with the offset to the motion control operator to calculate the vehicle control maneuver. Comparing with tradition motion planning schemes, the proposed ITO approach can guarantee the trajectory's physical and operational feasibility, make the motion controller have better tracking performance, and have a predictive evaluation on the vehicle response. The simulation results demonstrate the effectiveness of the proposed method.
Sentiment Classification Using Feature Selection Techniques for Text Data Composed of Heterogeneous Sources

Aims: Feature Selection Techniques for Text Data Composed of Heterogeneous sources for sentiment classification. Objectives: The objective of work is to analyze the feature selection technique for text gathered from different sources to increase the accuracy of sentiment classification done on microblogs. Methods: Applied three feature selection techniques Bag-of-Word(BOW), TF-IDF, and word2vector to find the most suitable feature selection techniques for heterogeneous datasets. Results: TF-IDF outperforms outh of the three selected feature selection technique for sentiment classification with SVM classifier. Conclusion: Feature selection is an integral part of any data preprocessing task, and along with that, it is also important for the machine learning algorithms in achieving good accuracy in classification results. Hence it is essential to find out the best suitable approach for heterogeneous sources of data. The heterogeneous sources are rich sources of information and they also play an important role in developing a model for adaptable systems as well. So keeping that also in mind we have compared the three techniques for heterogeneous source data and found that TF-IDF is the most suitable one for all types of data whether it is balanced or imbalanced data, it is a single source or multiple source data. In all cases, TF-IDF approach is the most promising approach in generating the results for the classification of sentiments of users.
Knowledge-Aware LSTM for Machine Comprehension

Machine Comprehension (MC) of text is the problem to answer a query based on a given document. Although MC has been very popular recently, it still have some serious weaknesses which rely only on query-to-document interaction or its learning is just heavily dependent on the training data. To take advantage of external knowledge to improve neural networks for MC, we propose a novel knowledge enhanced recurrent neural model, called knowledge-aware LSTM (k-LSTM), an extension to basic LSTM cells, designed to exploit external knowledge bases (KBs) to improve neural networks for MC task. To incorporate KBs with contextual information effectively from the currently text, k-LSTM employs an compositional attention mechanism to adaptively decide whether to attend to KBs and which information from external knowledge is useful. Furthermore, we present our knowledge enhanced neural network, called Knowledge-guided DIM Reader (K-DIM Reader), which is a novel knowledge-aware compositional attention neural network architecture, employing the k-LSTM in our framework. By stringing external background knowledge together and imposing compositional attention interaction that regulate their interaction, K-DIM Reader effectively learns to perform reading comprehension processes that are directly inferred from the data in an end-to-end approach. We show our proposed models strength, robustness and interpretability on the challenging MC datasets, achieving significant improvements on SQuAD dataset [1] and obtaining new state-of-the-art results on both Cloze-style datasets, CBTest [2] and CNN news [3]. In particular, we further extend 6 popular end-to-end neural MC models using k-LSTM incorporating knowledge into models for improving MC, and evaluate their performance on both well-known MC datasets. We demonstrate that neural model with external knowledge improves performance on MC task.
Relationship-Aware Hard Negative Generation in Deep Metric Learning

Data relationships and the impact of synthetic loss have not been concerned by previous sample generation methods, which lead to bias in model training. To address above problem, in this paper, we propose a relationship-aware hard negative generation (RHNG) method. First, we build a global minimum spanning tree for all categories to measure the data distribution, which is used to constrain hard sample generation. Second, we construct a dynamic weight parameter which reflects the convergence of the model to guide the synthetic loss to train the model. Experimental results show that the proposed method outperforms the state-of-the-art methods in terms of retrieval and clustering tasks.
Multi-sensor weighted fusion algorithm based on improved TOPSIS

Abstract In the process of track fusion of multi-sensor target tracking, multi-sensor weighted fusion is used to overcome the shortcoming of incomplete single information source. In order to change the weight of each sensor participating in fusion in real time according to the merits and demerits of sensor information adaptively, a method of sensor weight calculation based on improved TOPSIS is proposed. Firstly, the local estimation and global prediction residuals of each sensor are composed into residuals matrix, from which the ideal solution is found, and then the relative closeness degree is calculated according to the grey correlation distance between the local estimation residuals and the ideal solution, so as to screen the sensors with high weight to participate in the fusion. The simulation results show that the tracking error of the algorithm after fusion is significantly reduced compared with the local estimation error of each sensor, and the tracking error of multiple sensors for the target can be realized.
Some Algorithms on Detection of Corner Points for Digital Images

Detecting corner points for the digital images is based on determining significant geometrical locations. Corner points lead and guide for providing significant clues for shape analysis and representation. They actually provide significant features of an object, which can be used in different phases of processing. In shape analysis problems, for example, a shape can be efficiently reformulated in a compact way and with sufficient accuracy if the corners are properly located. This chapter selects seven well referred algorithms from the literature to review, compare, and analyze empirically. It provides an overview of these selected algorithms so that users can easily pick an appropriate one for their specific applications and requirements.
The Role of Mentoring in the Teaching Practice Period

The need for a system of high-quality mentoring during students’ teaching practice, which takes place on the basis of educational institutions is substantiated. The views of various researchers on the concept of mentoring, as well as on the personality qualities that a teacher-mentor should have, are summarized. The experience of organizing teaching practice for the period 2018–2020 is analyzed. On the example of real situations, negative moments in the work of both teacher-mentor and the staff and administration of educational institutions were identified.
A Sentence-RCNN embedding model for Knowledge Graph Completion

Large-scale knowledge graphs are structured to represent real world facts, but they are far from completeness. A number of completion methods have been developed to fill missing facts. In this paper, a novel Sentence-RCNN embedding model is proposed for knowledge graph completion. This model represents knowledge facts as sentences, so that it can precisely capture long-term dependencies, local structure information and translational features simultaneously. In addition, we propose a new method to construct negative samples (CNS), which greatly reduces the number of false negative samples used in model training stage. The proposed model was validated by two challenging benchmark datasets without any extra information. Results showed Sentence-RCNN model had fairly good accuracy, robustness and convergence than the state-of-the-art embedding models. It improved MRR and H©N metrics by an average of over 11.8%, and obtained extra 5.4% improvement with CNS method.
Analysis of the Generator and Consistency of General Web Page Layout Structure Using Matching Algorithm Based on Set Difference

According to the website quality of the organization dimension and website usability, the consistency of the layout of the web page is how the general layout elements format is common to all pages. Consistency can make it easier for users in terms of navigation. In this study, researchers used a case study on the East Kalimantan High Prosecutors' website (http://www.kejati-kaltim.go.id/). The inequality location of the layout makes inconsistency. In this research, web page layout structure data was collected using a Chrome Extension called HTML Tree Generator by adding additional functions according to semantic elements, so that automatically downloads HTML elements in the Document Object Model (DOM). Then, the general layout structure generator only took the semantic elements from the most common layout as its input. Matching Algorithm used to solve the problem of inconsistency in general layout structure, by looking for differences of structure using set difference. Combination functions are used to determine the comparison of each page. Set difference produce “false” if there are one or more differences. Research on the consistency of web page layout structure from case studies yields a consistency score of 92.1%. This score indicates an inconsistency in the web page layout. However, after fixed inconsistent pages, the recalculation score can produce a consistent web page layout.
Classifying the Polarity of Online Media on the Indonesia Presidential Election 2019 Using Artificial Neural Network

The 2019 presidential election is one of the mandatory national agendas that is covered by all of the mainstream news media in Indonesia. The function of news media as an information provider reaps criticism because they are suspected of having polarity towards certain candidates. In this paper, the polarity of news media is analyzed by performing sentiment assessment towards every news regarding each candidate. Since manual sentiment analysis is costly and time-consuming, because of the large amount of data that needs to be processed, we adopt a machine learning method to automate the sentiment analysis process. This research employs Artificial Neural Network (ANN) to classify scraped news texts from online media and TF-IDF weighting method for feature extraction. We found that the observed online media kompas.com, liputan tan6.com, republika.co.id, and tempo.co do not have significant polarity toward one of the candidates. In addition to ANN, we also compared other methods to investigate the appropriate methods for our dataset. Our experiment shows that on average, ANN obtains the best accuracy at 84.57%, compares to Decision Tree C4.5 (83.34%), Naive Bayes (SO.42%), and SVM (79.04%).
Research on Protective Mining Method for Privacy Data in Network based on Apriori Algorithm

Aiming at the problems of high redundancy and unsafe security existing in the current research results of network data mining, this paper proposes a privacy data mining method for network based on Apriori algorithm. Redundant data are filtered by Bloom filter. Based on this, anti-interference filter and random number generator are designed to suppress noise in data. Random values are used to transform the original data, disguise the original data and ensure the security of data mining process. Combined with data redundancy analysis, filtering and camouflage, based on Apriori algorithm, the optimized TS Apriori algorithm is used to mine the camouflaged data sets. The association rule mining is divided into frequent itemset mining and association rule mining. The correlation parameter is introduced to filter association rules, and the false rules are eliminated. The high correlation rules are obtained to realize the privacy data protection mining for network. The experimental results show that the proposed method has low redundancy, strong security and high mining accuracy.
A method for false alarm recognition considering threshold

A new approach to false alarm recognition is proposed. The described method divides the state of a system into three types: normal, false-alarm, and faulty, and analyzes the overlapping relations of the distribution functions of different states to determine the optimal thresholds. After a brief introduction to support vector machine (SVM), the proposed strategy based on the results using thresholds is explained. The presented evolutionary approach is illustrated by a fault injection system. The accuracy of the technique is compared with the conventional and other intelligent methods, and the obtained results are discussed.
MA-TREX: Mutli-agent Trajectory-Ranked Reward Extrapolation via Inverse Reinforcement Learning

Trajectory-ranked reward extrapolation (T-REX) provides a general framework to infer users’ intentions from sub-optimal demonstrations. However, it becomes inflexible when encountering multi-agent scenarios, due to its high complexity caused by rational behaviors, e.g., cooperation and communication. In this paper, we propose a novel Multi-Agent Trajectory-ranked Reward EXtrapolation framework (MA-TREX), which adopts inverse reinforcement learning to infer demonstrators’ cooperative intention in the environment with high-dimensional state-action space. Specifically, to reduce the dependence on demonstrators, the MA-TREX uses self-generated demonstrations to iteratively extrapolate the reward function. Moreover, a knowledge transfer method is adopted in the iteration process, by which the self-generated data required subsequently is only one third of the initial demonstrations. Experimental results on several multi-agent collaborative tasks demonstrate that the MA-TREX can effectively surpass the demonstrators and obtain the same level reward as the ground truth quickly and stably.
Game Design and Implementation in Popular Science Education Based on Virtual Reality Maze

Abstract With the development of science and technology, VR technology has been widely used in various fields, and promoted the rapid development of various fields. It has become an important direction of market development in game design, and VR technology has many advantages in practical application. According to the research and development concept of popular science game and VR maze game, this paper designs the game by making use of what they have in common. Effective integration of knowledge popularization and electronic games, through the VR maze popular science games to open up new ways of knowledge dissemination.
Different Techniques of Image Inpainting

Image inpainting was generally done physically by artists to remove deformity from works of art and photos. To cover the area of target or missing data from a signal, utilizing encompassing details and restructure signal is the fundamental job of image inpainting algorithms. We have considered and audited numerous distinct algorithms available for image inpainting and clarified their methodology. This paper includes various works in the branch of image inpainting and will help beginners who want to work and develop the image inpainting techniques.
Solar interference test for flame detectors based on natural lighting

Abstract In order to evaluate the anti-interference performance of the flame detector applied to the environment with possible solar interference, the feasibility of standardized test method for solar interference of flame detectors is researched in this paper. According to the characteristics such as structure, use place and installation mode of flame detector, the radiation model and illuminance model of solar interference are constructed. Through statistical analysis of photo-climatic data of regional representative cities, the severity level of the solar interference that reflecting national lighting conditions is summarized and classified. On this basis, data analysis of natural lighting resources of 8 typical cities in China is carried out. Combined with natural lighting test schemes, the feasibility of the solar interference test for flame detectors is analyzed, and relevant conclusions are drawn.
Modeling and Prototyping a Modular, Low-Cost Collision Avoidance System for UAVs

Many challenges arise when attempting to use unmanned aerial vehicles (UAVs) in indoor environments, such as the lack of a GPS signal for use in navigation and the smaller margin of error in movements. Typically, those challenges are addressed by using a collision avoidance system. However, most commercially available collision avoidance systems are expensive, limited in suppliers, and are restricted to use on a specific platform. Additionally, some of the collision avoidance systems choose to forego obstacle detection in one or more directions, usually the upward direction. This work proposes that it is possible to develop a custom, low-cost collision avoidance system with modular capabilities, allowing it to be adapted to any UAV platform. The feasibility of the proposed system was determined by creating a single-direction prototype that was implemented on a small quadcopter and tested by flying the quadcopter towards a wall at slow speeds. To develop the system's control algorithm a model of a quadcopter was built. Using the model, a feedback control algorithm was developed and tested via simulation with the model. The feasibility of the proposed collision avoidance system is promising with the prototype able to prevent the quadcopter from colliding with a wall. However, further refinement in the methodology and techniques used to develop the system is needed to improve performance and reliability of the system, especially as obstacle detection is added in other directions of motion.