Reviewerly/exHarmony huggingface dataset:
Attention-based Bi-LSTM-CRF Network for Emotion Cause Extraction in Texts

Emotion cause extraction is to identify the causes behind an emotion expressed in a document, a more challenging task for the fine-grained emotion analysis in natural language processing. Most existing methods regard the task as an independent clause classification problem, ignoring the relationships among multiple clauses in the same document. Moreover, the relative position of the candidate clause and emotion clause provide critical emotion cause clue. In the paper, an attention-based Bi-LSTM-CRF network is proposed to integrate the above information. In this network, a bi-directional long short-term memory is first used to capture both the contextual information and the latent semantic relations of emotion expression and candidate clause. Then, two attention mechanisms are designed to encode the mutual influence of the emotion expression and candidate clause, the relative position and candidate clause. Better-distributed representations are created with the former design. Finally, these representations are into the Condition Random Fields for labeling. The results experimented on a benchmark Chinese emotion cause dataset proved the effectiveness of our method by achieving the F score of 88.40%.
Keyword Extraction from Scientific Articles in Bahasa Indonesia using TextRank Algorithm

Some of text mining application usually uses the keyword extraction method to obtain the text features. Most of the text mining application implements keyword extraction in the pre-processing stage. The keyword extraction method is a necessary process to obtain the depiction of an article or document. In general, this research is divided into three major stages. First of all, the pre-processing stage. This stage prepares and cleans the scientific article from insignificant parts (such as header, footer, page number, author name, or author affiliation) before the keyword extraction stage. Second, the keyword extraction stage applies the TextRank algorithm to extract the keywords. Third, the post-processing stage to generate keyphrase based on the rank of the vertices and their adjacent. The results show that the number of assigned keywords plays a crucial role in determining the recall value. The recall value of the TextRank algorithm raises from 38.46% (5 keywords) to 55.26% (10 keywords) and 61.54% (15 keywords). It occurs to the keyword extraction from scientific articles with reference inclusion. A similar result occurs to the keyword extraction from scientific articles without references inclusion. Future research can consider eliminating the post-processing stage by including multiword expression candidates in the pre-processing stage to improve keyword extraction time.
Virtual simulation of Lathe Machining Training Based on Unity3D

Abstract In this paper, through the use of SolidWorks three-dimensional software for the lathe three-dimensional modeling, the lathe three-dimensional model can more intuitively express the structure of the lathe. Through the analysis of the reducer shaft, this paper developed the machining process, and then the solid modeling of the shaft, and finally through the Unity3D software for the reducer shaft machining simulation analysis.
Hierarchical Representations with Discriminative Meta-filters in Dual Path Network for Tracking

In visual tracking task, accuracy and robustness are critical issues for achieveing remarkable performance. In this paper, we propose a novel dual path network with discriminative meta-filters and hierachical representations to solve these issues. We first design geometrically sensitivity pathway (GESP) and geographical sensitivity pathway (GASP) as two subtasks for target classification and scale estimation. GASP mainly includes powerful discriminative meta-filters to find coarse location of target and GESP can refine region of interests online while adapt the appearance model to the target swiftly. Then, a dual path network is developed in a online and offline framework. Specifically, meta-filters are trained offline in order to gain meta-knowledge of similar tracking scenes. Finally, we present three suggestions on deigning modern tracker. Extensive experiments on VOT2018 datasets verify the superior performance of proposed method compared with other state-of-the-arts, achieving expected average overlap (EAO) of 0.467.
HEAT: Hyperbolic Embedding of Attributed Networks

Finding a low dimensional representation of hierarchical, structured data described by a network remains a challenging problem in the machine learning community. An emerging approach is embedding networks into hyperbolic space since it can naturally represent a network's hierarchical structure. However, existing hyperbolic embedding approaches cannot deal with attributed networks, where nodes are annotated with additional attributes. To overcome this, we propose HEAT (for Hyperbolic Embedding of Attributed Networks). HEAT first extracts training samples from the network that captures both topological and attribute node similarity and then learns a low-dimensional hyperboloid embedding using full Riemannian Stochastic Gradient Descent. We show that HEAT can outperform other network embedding algorithms on several common downstream tasks. As a general network embedding method, HEAT opens the door to hyperbolic manifold learning on a wide range of both attributed and unattributed networks.
Improved Attacks Against Key Reuse in Learning with Errors Key Exchange

Basic key exchange protocols built from the learning with errors (LWE) assumption are insecure if secret keys are reused in the face of active attackers. One example of this is Fluhrer's attack on the Ding, Xie, and Lin (DXL) LWE key exchange protocol, which exploits leakage from the signal function for error correction.
Automatic Docking Recognition and Location Algorithm of Port Oil Loading Arm Based on 3D Laser Point Cloud

In view of the traditional artificial traction oil arm docking low degree of automation, poor safety problem, this paper puts forward a kind of automatic docking visual recognition algorithm for port oil arm based on 3D laser point cloud data. First of all, RANSAC(Random Sample Consensus) algorithm is used for plane segmentation of scattered point cloud data scanned by lidar. For the partitioned plane, Euclidean clustering algorithm is used to obtain the target nozzle plane. Then extracting the feature of each orifice separately to get the centroid coordinates to realize the identification and positioning. The experimental results show that the algorithm can meet the needs of spot flange identification, accurate the positioning, meet the needs of automatic docking of oil loading arm and improve the docking efficiency.
Ethereum Blockchain Enabled Secure and Transparent E-Voting

The blockchain’s revolutionary concept is the underlying technology behind the popular examples such as Bitcoin and it now relies on the Web and online services. Nowadays, blockchain is famous for its use in cryptocurrencies, but many fintech activities and routine processes that were done offline can be done using blockchain. Smart contracts are abstract pieces of codes that need to be inserted into the network and enforced as planned in every phase of upgrading blockchains. With the population growing so fast across the globe, e-voting is an emerging online service-related issue. The smart contracts of blockchain enable to have a easy, safe, cheap, secure and transparent e-voting due to which blockchain is one of the top solutions for e-voting. Even in the many blockchains available in the world, Ethereum is one of the most consistent available blockchain and has widespread use because of which it is suitable for e-voting. An e-voting system must ensure that it is secure, as it should not allow duplicated votes and it should be able to protect attendants’ privacy being fully transparent too. In this paper, Ethereum wallets and Solidity language for smart contracts were used to make a sample small scale e-voting application. The blockchain was tested on local blockchain using ganache and ropsten test network. The Ethereum blockchain keeps the records of ballots and votes after an election is held. Users can use Ethereum wallets to directly submit theirs vote and those votes are handled with the consensus of each Ethereum node.
Identifying the public interest in COVID-19 contact tracing apps in Switzerland based on online search behavior

One major discussion point during the COVID-19 crisis is the use of digital contact tracing applications to reduce the spreading of the Corona virus among the population. Whereas this is important for public health from a pandemic point of view, this raised many discussions about privacy and other data mining concerns in the population.
E-learning: technologies, application and challenges

In the present article we examine the application of e-learning in the education system in the conditions of social isolation caused by the COVID-19 pandemic. The possibilities and challenges in the implementation of e-learning have been researched. Some of the most used platforms for e-learning are discussed. It has been made an analysis of these platforms, their advantages and disadvantages have been identified. Based on the results of the study, the challenges for the future development and successful use of e-learning are defined.
Prediction of Defects in Software Using Machine Learning Classifiers

The Software Defect Prediction (SDP) model engages in predicting defects and bugs in the software. This model will detect and predict bugs during early stages of the software development life cycle to improve the overall quality of software and reduce the cost also. In this paper, the author presents a model that will predict the bugs with the help of machine learning classifiers. For this model, the researcher has used the dataset NASA from the known repositories and used two supervised ML classifier algorithms such as linear supervision (LR) and Naive Bayes (NB) for detecting and predicting faults. This study describes how ML algorithms work effectively in SDP models. The results collected showed that the linear regression approach performs better and predicts the faults with accuracy.
Mobile Augmented Reality Application with Multi-Interaction for Learning Solutions on the Topic of Computer Network Devices (Effectiveness, Interface, and Experience Design)

Application of Mobile Augmented Reality (AR) as a learning solution in innovation facing the education era 5.0. for vocational students on the computer network device topic, we promote in this paper. The purpose of this study is to apply AR technology-based applications by maximizing all of its interactions in the learning process about computer network devices so that students can know and understand their functions, then conduct an assessment of User Interface (UI) and User Experience (UX). The detection approach uses a marker-based tracking method, by bringing up 3D objects, rotating and touching, information and tool functions in the form of text, and producing sounds as information clues. A total of 30 students from one of the vocational high schools have been involved to use this application. After being analyzed for the current situation the students were very enthusiastic in following the learning process, then in the analysis of student understanding results obtained an average value of N-Gain was 67.41 which was proven that AR application could improve learning effectiveness, the results of the UI assessment with a value of 98.70% have a very strongly agree predicate to the application interface has been met, and the results of the assessment of the experience of users with a value of 98.94% have a very strongly agree predicate to the experience of using the application has been fulfilled.
GuyDance

A product line is an approach for systematically managing configuration options of customizable systems, usually by means of features. Products are generated by utilizing configurations consisting of selected features. Product-line evolution can lead to unintended changes to product behavior. We illustrate that updating configurations after product-line evolution requires decisions of both, domain engineers responsible for product-line evolution as well as application engineers responsible for configurations. The challenge is that domain and application engineers might not be able to talk to each other. We propose a formal foundation and a methodology that enables domain engineers to guide application engineers through configuration evolution by sharing knowledge on product-line evolution and by defining configuration update operations. As an effect, we enable knowledge transfer between those engineers without the need to talk to each other. We evaluate our method by providing formal proofs that show product behavior of configurations can be preserved for typical evolution scenarios.
Research on Power Load Forecast Based on Ceemdan Optimization Algorithm

Abstract With the continuous development of the power industry, power data has become more complex. However, it is difficult for common shallow neural networks to fully extract the original load data features, thus greatly limiting the load prediction accuracy. Therefore, given the advantages of CEEMDAN, a prediction model based on CEEMDAN is proposed in the paper, the advantages of which are verified by simulation analysis of measured power data. Experiments prove that the algorithm proposed in the paper has higher prediction accuracy, which effectively overcomes the EMD modal aliasing problem, and the decomposition process is more complete, so that the prediction accuracy of the subsequent prediction model is improved.
Time Series Clustering with Deep Reservoir Computing

This paper proposes a method for clustering of time series, based upon the ability of deep Reservoir Computing networks to grasp the dynamical structure of the series that is presented as input. A standard clustering algorithm, such as k-means, is applied to the network states, rather than the input series themselves. Clustering is thus embedded into the network dynamical evolution, since a clustering result is obtained at every time step, which in turn serves as initialisation at the next step. We empirically assess the performance of deep reservoir systems in time series clustering on benchmark datasets, considering the influence of crucial hyper-parameters. Experimentation with the proposed model shows enhanced clustering quality, measured by the silhouette coefficient, when compared to both static clustering of data, and dynamic clustering with a shallow network.
Twin and Multiple Black Holes Algorithm for Feature Selection

Over the years, nature-inspired meta-heuristic algorithms have been proposed to solve complex computational problems. Evolutionary algorithms are simple to implement and can be adapted to any kind of optimization algorithms because of their flexibility. Finding best subset of attribute in order to collect interpretable results is an important step in classification and regression tasks. Several stochastic and heuristic algorithms with a suitable classifier have been employed in the past for attribute selection with considerable success. In this work we have proposed a novel modification to the recently proposed Black hole (BH) meta-heuristic swarm optimization algorithm and employed it along with Support Vector Machines (SVM) for simultaneous attribute selection and classification. This attribute selection algorithm can be implemented in different domains. Experiments demonstration on publicly available benchmark datasets shows the algorithm compares favorably with existing algorithms.
The Research on Covert Communication Model Based on Blockchain: A Case Study of Ethereum’s Whisper Protocol

Covert communication is an extension of the traditional encryption technology, which hides information through carriers such as text and images. Traditional covert communication relies on a third-party central node, which is easy to be detected and attacked. As a new type of decentralized technology, blockchain has been widely developed in recent years. Its anti-interference, anti-tampering and other characteristics can effectively alleviate the problems faced by traditional covert communications. Combining with blockchain technology to design an effective and feasible hidden communication model and method becomes the focus of research. To this end, this paper designs a new covert communication model suitable for Ethereum based on the whisper protocol. First, we introduce the covert communication model under the blockchain, as well as the whisper protocol in Ethereum. Then, based on the characteristics and structure of the whisper protocol, a new type of covert communication method was designed. Finally, the anti-jamming, anti-tampering and anti-detection of the model are proved, and its information embedding and transmission efficiency are also evaluated by comparing with the time-based covert communication.
Path Planning for Mobile Robots Based on Improved RRT Algorithm

Aiming at the shortcomings of the basic RRT algorithm, such as the random search ability is relatively strong, and the calculation result is usually not the shortest path, etc., an improved RRT path optimization algorithm is proposed, which uses the ant colony algorithm to obtain the basic RRT algorithm. The path is iterated and optimized, and eventually converges to a shortest path, improving the efficiency of planning the shortest path in advance, and conducting in-depth research on this algorithm. A large number of simulation results show that the improved algorithm has certain feasibility and effectiveness. In multiple iterations, it can eventually converge to a path with a better distance.
Using data mining techniques to predict user’s behavior and create recommender systems in the libraries and information centers

Purpose This study aims to analyze and predict a user’s behavior and create recommender systems in libraries and information centers, using data mining techniques. Design/methodology/approach The present study is an analytical survey study of cross-sectional type. The required data for this study were collected from the transactions of the users of libraries and information centers in Hamadan University of Medical Sciences. Using data mining techniques, the existing patterns were investigated, and users’ loan transactions were analyzed. Findings The findings showed that the association rules with the degree of confidence above 0.50 were able to determine user access patterns. Furthermore, among the decision tree algorithms, the C.05 predicted the loan period, referrals and users’ delay with the highest accuracy (i.e. 90.1). The other findings on feedforward neural network with R = 0.99 showed that the predicted results of neural network computation were very close to the real situation and had a proper estimation of user’s delay prediction. Finally, the clustering technique with the k -means algorithm predicted users’ behavior model regarding their loyalty. Practical implications The results of this study can lead to providing effective services and improve the quality of interaction between librarians and users and provide a good opportunity for managers to align supply of information resources with the real needs of users. Originality/value The results of the study showed that various data mining techniques are applicable with high efficiency and accuracy in analyzing library and information centers data and can be used to predict a user’s behavior and create recommendation systems.
Learning adversarial policy in multiple scenes environment via multi-agent reinforcement learning

Learning adversarial policy aims to learn behavioural strategies for agents with different goals, is one of the most significant tasks in multi-agent systems. Multi-agent reinforcement learning (MARL), as a state-of-the-art learning-based model, employs centralised or decentralised control methods to learn behavioural strategies by interacting with environments. It suffers from instability and slowness in the training process. Considering that parallel simulation or computation is an effective way to improve training performance, we propose a novel MARL method called Multiple scenes multi-agent proximal Policy Optimisation (MPO) in this paper. In MPO, we first simulate multiple parallel scenes in the training environment. Multiple policies control different agents in the same scene, and each policy also controls several identical agents from multiple scenes. Then, we expand proximal policy optimisation (PPO) with an improved actor-critic network, ensuring the stability of training in multi-agent tasks. The actor network only uses local information for decision making, and the critic network uses global information for training. Finally, effective training trajectories are computed with two criteria from multiple parallel scenes rather than single to accelerate the learning process. We evaluate our approach in two simulated 3D environments, one of which is Unity's official open-source soccer game, and the other is unmanned surface vehicles (USVs) built by Unity. Experiments demonstrate that MPO converges more stable and faster than benchmark methods in model training, and demonstrates excellent adversarial policy compared with benchmark models.
User Interface Design for Solar Panel Monitoring System on Android Smartphones Using User-Centered Design Method

In 1970, the world overcame the special energy crisis of petroleum. However, since then many people have used solar energy as an alternative energy source. As an alternative source, the use of solar power systems can easily spread throughout the world because of its low maintenance and ease of deployment. But the use of solar cells that have been integrated with the user's will is not widely available, for example, the use of solar cells that are integrated with smartphones. In connection with this, it will be very easy if the process of monitoring or monitoring the use of solar panel power and the application of smart home technology is done with a computerized system, for example by using an Android-based application. When interacting with the Solar Panel Monitoring Application on Android Smartphones the user must get the same comfort by his experience using other systems. This writing aims to design a Solar Panel Monitoring Application system on android smartphones using the User-Centered Design (UCD) method. The user as the center of the design system development process is called the User-Centered Design (UCD) design philosophy. From the 8 parameters of usability goals and user experience, the average percentage stage is 81%. It means that the design of the system is good.
Macro‐pixel‐wise CNN‐based filtering for quality enhancement of light field images

The Letter introduces a novel filtering method based on convolutional neural networks (CNNs) for quality enhancement of light field (LF) images captured by a plenoptic camera and compressed using high-efficiency video coding (HEVC). The method takes advantage of the macro-pixel (MP) structure specific to the LF images and proposes a novel MP-wise filtering approach based on a novel deep neural network architecture. The proposed CNN-based method achieves an outstanding performance when HEVC is employed without its in-loop filters. Experimental results show high luminance-peak signal-to-noise ratio (Y-PSNR) gains and average Y-Bjøntegaard delta (BD)-rate savings of over HEVC on a large data set.