Reviewerly/exHarmony huggingface dataset:
Fuzzy Inference-Propelled Sentence Ranking for Extractive Summary Generation

Natural language serves as an impeccable tool for the appropriate representation of knowledge among individuals. Owing to the varying representation of the same knowledge base and the perpetual growth of the World Wide Web, the need to uncover an effective method to condense available textual data without significantly dampening the implied information is paramount. In an attempt to solve the need for effectively condensing textual data, the paper proposes a system which is capable of mimicking the human brain's approach to process Natural Language Fuzzy Logic. The system is subjected to both intrinsic and extrinsic evaluation and the results are compared against two other text summarizers - Auto summarize Tool and SweSum using the CNN Corpus Dataset. The Relevance Prediction Measure, F1 Score and Recall results suggest the applicability of Fuzzy Reasoning in text summarization and through evaluation, it can be inferred that proposed system has successfully tried to mimic the process of summary generation by the human brain.
Memory Efficient Adversarial Attacks on Graphs: A Winning Solution for KDD Cup 2020

Graph structures are ubiquitous in many domains ranging from user-item relationships to paper citation networks and human mobility networks. Although Graph Neural Networks (GNNs) improve the accuracy of network analysis, an adversarial example may lead to misclassification. Most existing studies pertaining to adversarial attacks on graphs, however, have been conducted on small datasets. Because the complexity of GNNs is proportional to the number of nodes, the existing adversarial methods require an extremely large amount of memory in the case large-scale graphs. In this paper, we propose a memory efficient method for adversarial attacks on large-scale graphs that utilizes the characteristic of GNNs that aggregates only local nodes in a large-scale graph. We validate the efficiency of the proposed method in evaluations including an offline evaluation and an online evaluation at KDD Cup 2020 using a large-scale (almost 700,000 nodes) citation network. The offline evaluation results show that the proposed method reduces the estimation accuracy by 0.4%-7.2%. The method was awarded 4th place in the KDD Cup 2020 competition as an online evaluation.
Solar Chatbot and Energy Prediction using different Machine Learning Algorithms

Global warming and energy crux has motivated the use of renewable sources like solar energy. Solar power production is intensely sporadic as well as highly reliant on weather set parameters. Therefore, to utilize this energy source it becomes paramount to understand it’s fundamentals as well as the quintessential factors like weather, monetary requirements etc. necessary for its flawless implementation. This paper presents the application of different machine learning models to predict the short-range solar energy production in the city of Mumbai and projects the most accurate algorithm. Additionally, a client interested in investing in the use of solar energy requires a financial estimate of solar project alongside dynamical retrieval of queries related to this domain. Therefore, this paper also proposes to automate this process of interaction through a chat bot and a solar energy budget estimator which facilitates an evaluation of the remunerations required for solar panels installation.
A Path Planning Method for Video Camera Equipped UAVs Monitoring a Ground Area

Unmanned Aerial Vehicles (UAVs) have become the necessary tools for a wide range of activities including but not limited to real-time monitoring, surveillance, border patrolling, search and rescue, civilian, scientific and military missions, etc. Their advantage is unprecedented and irreplaceable especially in environments dangerous to humans, for example in radiation or pollution exposed areas. A method for occlusion-aware UAV path planning is presented in this paper, which ensures every point on the target ground area can be seen at least once in a complete surveillance circle. Besides, the geometrically complex environments with occlusions are considered in our research. Compared with many existing methods, we decompose this problem into a waypoint determination problem and an instance of the traveling salesman problem.
Blockchain’s ecosystems of innovation

Innovation in the blockchain technology is the process of bringing blockchain-based products and services to the market. As discussed in the previous chapters, blockchain is associated with a broad range of innovations, from specific products such as Bitcoin to platforms for the development of decentralized applications such as Ethereum. The ecosystem of innovation related to blockchain technologies sets the rate and direction of innovation and competence building on the blockchain technology. The central elements in the ecosystems of innovation are the main actors in the innovation process, their interlinkages through which knowledge and resources flow to enable innovation, and the institutions in place that set the rules, regulations and expected behaviour associated with blockchain innovation.
Cognitive Universal Educational Actions of Primary Schoolchildren: Characteristics, Methods and Techniques of Formation

The article discusses the actual problem of the formation of cognitive universal educational actions in younger schoolchildren in the context of&#x0D; the implementation of the changing requirements of the Federal State Educational Standard of Primary General Education. The approaches&#x0D; to the definition of concepts, groups and main characteristics of cognitive universal educational actions that students must master at the first&#x0D; stage of school education are analyzed. The effective methods and techniques for the formation of this type of educational actions in younger&#x0D; schoolchildren are presented.
Numerical Controllers

Numerical controllers (NC) offer far more flexibility than the sequentialControllersequential control controlsSequential control discussed in Chap. 10 . For a workpieceWorkpiece, the type and sequence of production steps are defined in an NC programNC program that contains the control information in the form of alphanumeric characters. So, from a global viewpoint, numerical controllers are part of the family of sequential controls, whereby the individual control steps are specified by NC linesNC line (as alphanumeric characters).
The manifold embedded selective pseudo-labeling algorithm and transfer learning of small sample dataset

Special scene classification and identification tasks are not easily fulfilled to obtain samples, which results in a shortage of samples. The focus of current researches lies in how to use source domain data (or auxiliary domain data) to build domain adaption transfer learning models and to improve the classification accuracy and performance of small sample machine learning in these special and difficult scenes. In this paper, a model of deep convolution and Grassmann manifold embedded selective pseudo-labeling algorithm (DC-GMESPL) is proposed to enable transfer learning classifications among multiple small sample datasets. Firstly, DC-GMESPL algorithm uses satellite remote sensing image sample data as the source domain to extract the smoke features simultaneously from both the source domain and the target domain based on the Resnet50 deep transfer network. This is done for such special scene of the target domain as the lack of local sample data for forest fire smoke video images. Secondly, DC-GMESPL algorithm makes the source domain feature distribution aligned with the target domain feature distribution. The distance between the source domain and the target domain feature distribution is minimized by removing the correlation between the source domain features and re-correlation with the target domain. And then the target domain data is pseudo-labeled by selective pseudo-labeling algorithm in Grassmann manifold space. Finally, a trainable model is constructed to complete the transfer classification between small sample datasets. The model of this paper is evaluated by transfer learning between satellite remote sensing image and video image datasets. Experiments show that DC-GMESPL transfer accuracy is higher than DC-CMEDA, Easy TL, CMMS and SPL respectively. Compared with our former DC-CMEDA, the transfer accuracy of our new DC-GMESPL algorithm has been further improved. The transfer accuracy of DC-GMESPL from satellite remote sensing image to video image has been improved by 0.50%, the transfer accuracy from video image to satellite remote sensing image has been improved by 8.50% and then, the performance has been greatly improved.
Metacognitive processes and qualities as determinants of general management styles

The article presents theoretical and empirical materials that reveal the specifics of the main parameters of the metacognitive sphere of personality as determinants of general management styles. It is shown that one of the most constructive means of such disclosure is to identify the relationship between management styles as one of the central constructs of management psychology, on the one hand, and the main metacognitive parameters (procedural, strategic and other characteristics), on the other. The regularities of the influence of metacognitive factors on the main general management styles are established. It is shown that they are characterized by different values of the indexes of integration (coherence), differentiation (divergence) and the index of general organization of the metacognitive sphere. The provisions are formulated according to which general management styles have a structural, rather than analytical determination in relation to those metacognitive factors on the basis of which they are formed.
Acoustic IoT Framework to Detect Missing Person in Mountainous Areas

For a search of a missing person in mountainous areas, a lot of time and human resources are required. We propose an Internet of Things (IoT) framework utilizing acoustic sensors to help to detect the missing person. The proposed IoT framework consists of five parts: smart acoustic sensors, drones/cartridges control system for dropping the sensors, the main control server, an analysis server for getting the acoustic information, and a web server to support the user interface. This framework enables rapid response to situations and golden time acquisition and provides continuous on-site detection services. We implement the proposed framework and demonstrate actual operation.
Developing Online English Training for Indonesian Female Home Workers

Community development has been in the spotlight worldwide as many countries attempt to promote equality and sustainability within social and economic aspects. Indonesia has also taken part in this effort; the government works with practitioners and educators to develop Indonesian communities. Indonesian Female Homeworker Network, the so-called Jaringan Perempuan Pekerja Rumahan Indonesia (JPPRI), is one of the communities striving for improvement to face the globalization challenges. Based on the need analysis result, members of JPPRI lacked English communication skills to grow and expand their businesses globally. Responding to the fact, the researchers designed a virtual English training program through WhatsApp to empower the community accordingly. This study employed 5 phases of the ADDIE model comprising analysis, design, development, implementation, and evaluation. The finding revealed that the participants’ English competencies showed a significant improvement after two cycles of the instructional design. Additionally, WhatsApp as the major teaching-learning aid did not contribute to the success of the training, but the teaching strategies and the ADDIE model.
ECO-DST: An Efficient Cross-lingual Dialogue State Tracking Framework

Data efficiency is a critical challenge for cross-lingual task-oriented dialogue state tracking (DST) due to high cost of collecting large amount of task-related labeled training set for specific language. Therefore, we focus on adapting high-performance source language DST to target language by using only bilingual dictionary, without accessing labeled target data. We propose a novel data efficient cross-lingual DST framework (ECO-DST), which consists of cross-lingual encoder and language independent decoder. To support cross-lingual zero-shot adaptation, we leverage two advanced methods in encoder: 1) pre-trained cross-lingual model XLM-RoBERTa (XLM-R), 2) dynamic local phrase code-switching data augmentation for cross-lingual representation alignment. We evaluate the proposed method on The Ninth Dialogue System Technology Challenge (DSTC9) cross-lingual tasks. For target language DST, we compare our proposed framework with submitted systems in DSTC9, our model achieves state-of-the-art result on CrossWOZ dataset and promising result on MultiWOZ 2.1 dataset. Meanwhile on source language DST, the same model keeps competitive performance compared with original source DST model.
ClusterChain: Decentralized and Trustworthy Clustering Over Blockchain

Artificial Intelligence (AI) algorithms follow probabilistic models instead of deterministic ones, therefore the results produced by the AI algorithms may fail occasionally. This represents a tremendous obstacle for large scale deployment of AI. Also, The centralized AI can lead to the possibility of data tempering and fraud. Moreover, the reliability and integrity of data sources are not guaranteed. This may lead to AI decision outcomes that can be highly erroneous and risky. In ordrer to address these issues, in this paper, we explore the integration benefit of AI and Blockchain technology and contribute to the construction of a reliable and immutable infrastructure for a trustworthy decentralized AI based on the Blockchain called ClusterChain. In ClusterChain, we implement a new decentralized consensus mechanism based on a clustering of data, in which multiple nodes in competition, run locally a clustering model and syncronise the best obtained solution which is saved in blocks resistant to the modification and could be traced back.
Modified Lanczos Algorithm for L2,1 norm Regularization Extreme Learning Machine

It is known that L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</inf> norm regularization extreme learning machine (L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</inf> -RELM) has strong robustness and good generalization performance in most cases. Though fitting problems had been solved, L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</inf> -RELM also exists high computational complexity. To solve this problem, a modified Lanczos algorithm is proposed for L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</inf> -RELM through iteration to compute output weights, and low computational complexity and fast convergence rate is achieved. Experimental results on artificial datasets and real benchmark datasets show that the proposed algorithm obtains better robustness and higher learning speed than ELM, RELM, Lan-ELM in most cases.
Research on the Basketball Goal Recognition Method Based on Improved MobileNet

Moving target detection is involved in many engineering projects, but it is difficult because of the strong time-varying speed and uncertain path. Goal recognition is the key technology of the basketball goal automatic test. Also, accurate and timely judgment of basketball goals has important practical value. Therefore, a basketball goal recognition method based on an improved lightweight deep learning network model (L-MobileNet) is proposed. First of all, the basket detection is carried out by the Hough circle transform algorithm. Then, in order to further improve the detection speed of basketball goals, based on the lightweight network MobileNet, an improved lightweight network (L-MobileNet) is proposed. First of all, for deeply separable convolution, channel compression and block convolution reduce the parameters and computational complexity of the module. At the same time, because block convolution will hinder the information exchange between characteristic channels, an improved channel shuffling method, IShuffle, is introduced. Then, combined with the residual structure to improve the generalization ability of the network, the RLDWS module is constructed. Finally, a more lightweight network L-MobileNet is constructed by using the RLDWS module. The experimental results show that the proposed method can effectively realize the judgment of basketball goals, and the judgment accuracy is improved by 8.35%. At the same time, the amount of parameters and computation is only 29.7% and 53.2% of the original, and it also has certain advantages compared with other lightweight networks.
Low-rank Subspace Consistency Clustering

The Clustering algorithm realizes data partition by similarity relation among data, and similarity calculation often depends on practical feature expression. Traditional shallow feature representation learning models cannot capture the deep semantic concepts between data and often fail to meet the current requirements of big data applications due to high computational complexity. In recent years, the depth subspace clustering algorithm has solved the above problems to some extent. However, in the process of feature expression learning, the existing deep clustering algorithms overemphasize the data reconstruction ability of the algorithm but ignore the rich potential structure information. Therefore, a new representation learning method is proposed in this paper, which adopts the consistency of subspace and uses the similarity between data samples as the supervision to guide the representation learning so that the subspace structure after the original data space transformation is more stable and the feature expression is more robust. A large number of experiments on public data sets have proved the good performance of this method in clustering.
Obstacle Avoidance and Target Tracking by Two Wheeled Differential Drive Mobile Robot Using ANFIS in Static and Dynamic Environment

This paper proposes an idea for navigation and target acquiring for an autonomous robot in static as well dynamic environment using a tuned and trained Sugeno-type fuzzy inference system. This term for such system is Adaptive Neuro Fuzzy Inference System (ANFIS). ANFIS incorporates benefits from both fuzzy logic controller and neural networks and therefore is a robust tool to solve the stated problem. The input/output training data used to tune the system parameters is intuitively chosen and can be considered expert knowledge. Simulation results are achieved using in-build MATLAB functions. The architecture of the entire system consists of four ANFIS controllers. Two of them are used to control the wheel velocities of left and right wheel of the robot in order to get to the target defined by the user. The remaining two ANFIS controllers are utilized to successfully avoid obstacles encountered on the way to target.
Monitoring of the hydrosphere by a group of robots

The proposed article is based on mathematical models studying and development, by setting up computational experiments, of a miniature unmanned underwater vehicles (MUUV) The development and studying of systems for automated monitoring of water quality allows for the prompt real-time assessment of the environmental situation at various points in the monitoring area. The research objectives are developing mathematical models describing the process of MUUV control, as well as to develop a mathematical model of the MUUV system. The development of a MUUV group motion control is carried out to organize the sampling process. A mathematical model which describes perturbed motion is proposed and a method for the synthesis of the parameters of regulator of the control system based on the decomposition of the equations of motion of the MUUV and transient process is developed. Mathematical models of the force factors acting on the MUUV have been developed in view of external pulse disturbances. Mathematical models of control factors have been developed which ensure the motion of the MUUV with a variable thrust vector.
Tibetan News Text Classification Based on Multi-features Fusion

At present, the main research method of Tibetan text classification research is to use Tibetan words and Tibetan syllables as the basic representation features of the entire text, and then use Deep Neural Network models such as CNN and LSTM to complete the text classification research. Under normal circumstances, Tibetan news texts are composed of the headlines and the content. The news headlines summarize the news content in a short text sequence. That is to say, relative to the content of a news text, the headlines have more important weight information. Due to the short text sequence length of the headlines, there will be sparse features. This paper proposes to use both syllables and words features as the basic representation objects of headlines, so that the convolutional neural network model can extract more comprehensive semantic features of the headlines. For the task of Tibetan news texts classification, the experimental method in this paper is to simultaneously express the basic characteristics of the headlines and the content of the Tibetan news text at the words and syllables. Then after feature vectorization, the CNN network is used to complete the text classification. The research results show that the classification method proposed in this paper has achieved good classification results on the test set of experimental corpus.
Graph Structural Attention and Increased Global Attention for Image Captioning

Attention mechanism plays a significant role in the current encoder-decoder framework of image captioning. Nevertheless, many attention mechanisms only fuse textual feature and image feature once, failing to adequately integrate the feature between context and image. Furthermore, many image captioning networks based on scene graphs only consider the node information but ignore the structure, which is insufficient in grasping the spatial object relationship. To address the above problems, we propose structural attention and increased global attention. Two attentions select critical image features from image detail and global image. The increased global attention, focusing on global image features, enhances integration between text and image via fusing detailed image features into global attention. To better describe the relationship among image objects, our network allows for both the node information by content attention and the structure information by structural attention. Structural attention computes the similarity between the structure information of scene graph and local attention, building the image objects relationship differing from content attention. We evaluate the performance of our image captioning network in MS COCO and Visual Genome datasets. The results of the experiments show that our method achieves superior performance compared with the existing methods.
Blockchain for Distributed Consistency: A Cliquebased Framework for Multi-agent Systems

Multi-agent systems (MAS) promote developments in various applications such as rescue, tracking, and health care. Through the cooperation of multiple agents, the system can emerge beyond the ability of individual intelligence. However, considering the flexibility and mobility of agents, accountability and trusted interactions among agents have become mandatory aspects. One of the challenges in developing multi-agent systems is the design of appropriate coordination strategies. In this paper, we leverage the features of Ethereum architecture and propose a novel Clique-based distributed multi-agent system. The advantages of our system include secure, self-governing, and tamper-proof properties. We implement our system framework and test custom smart contracts based on a private blockchain in Ethereum. The experimental results show that the resource consumption of our proposed scheme is reasonable and feasible.