Reviewerly/exHarmony huggingface dataset:
Automatic stopword Identification Technique for Gujarati text

Natural Language Processing (NLP) is an Artificially Intelligent (AI) mechanism that allows computers to intelligently analyze, comprehend, and derive meaning from human language. In natural language text processing, common words like ‘a’, ‘the’, ‘is’, ‘an’, etc. are known as a stopwords. They are typically considered having no informative value. It is proved that one of the major benefits of removing stopword in NLP text-based processing is the reduction of the text in the corpus by 35 - 45%, without compromising on the efficiency of the target application performance. There are many stopword lists existing for Non-Indian languages like English, Arabic, French and German. Even for a few Indian languages like Hindi, Sanskrit and, Tamil substantial lists are available. But as of date very little research work is reported for one of the widely used Indian language namely Gujarati. As per our survey, for the Gujarati language, two major approaches have been suggested for stopword identification. The first approach is giving a static generic stopword list, and another approach is a Rule-based approach. The major drawback of these method is their inability to handle neologism. In this paper, we have suggested domain-specific, robust and dynamic stopword list identification mechanism developed for documents written in the Gujarati language. In our proposed approach, we take the top "N" words as seed words based on their frequency and later add other "M" similar context word which are identified by word embeddings. Further the effectiveness of removing these listed (N+M) stop words was checked by applying the stopword removal preprocessing phase in the Text Classification (TC) and Information Retrieval (IR) applications. In TC model, the feature vector reduces by approximately 16%, and on other hand, the accuracy of the TC model increased by nearly 3 %. The experiments also found, removal of the these stop words in IR application, increased the Mean Average Precision (MAP) of the system by nearly 31%. Thus, the overall time and space requirements were decreased without compromising on the end results of system.
Fast Ship Detection in Remote Sensing Images Based on Multi-Attention Mechanism

With the continuous development of deep learning in computer vision, object detection technology is constantly employed for processing remote sensing images. Especially, ship detection has become a significant and challenging task due to complex environmental factors (strong waves, clouds interference, etc.) and object issues (orientation, scale variety, density, etc.). Current detection methods pay more attention to the detection accuracy while ignoring the detection speed. In contrast with accuracy, detection speed is more important in some cases such as marine rescue and vessel tracking. Aiming at addressing these problems, we propose an enhanced YOLOv4(C-YOLOv4) which contains the feature fusion attention module (FAM) with a channel correlation loss(C-loss). C-loss is proposed to constrain the relations between object classes and channels while maintaining the intra-class and the inter-class separability. To evaluate the effectiveness of the proposed approach, comprehensive experiments are conducted on a public dataset HRSC2016. According to the experimental results, our proposed approach outperforms the baselines.
Pixel Gradient Based Zooming Method for Plenoptic Intra Prediction

Plenoptic 2.0 videos that record time-varying light fields by focused plenoptic cameras are prospective to immersive visual applications due to capturing dense sampled light fields with high spatial resolution in the rendered sub-apertures. In this paper, an intra prediction method is proposed for compressing multi-focus plenoptic 2.0 videos efficiently. Based on the estimation of zooming factor, novel gradient-feature-based zooming, adaptive-bilinear-interpolation-based tailoring and inverse-gradient-based boundary filtering are proposed and executed sequentially to generate accurate prediction candidates for weighted prediction working with adaptive skipping strategy. Experimental results demonstrate the superior performance of the proposed method relative to HEVC and state-of-the-art methods.
Science Education in Palestine

The aim of this chapter is to give the reader an overview of the real situation, opportunities, trends, and challenges related to science education in Palestine. The Palestinian educational system is struggling from the Israeli occupation and the procedures of the occupation procedures on the ground which negatively influence the development of the Palestinian infrastructure in different aspects of the Palestinian people including the educational system. The Palestinian organizations including the governmental ones and the local community try their best to mitigate the negative influence on education through many initiatives. This chapter was to introduce the actual situation of Science education in the Palestinian territories exploring the potential of emerging technology and utilizing it in the Palestinian educational system especially in science education in both higher education institutions and in K- 12. The Ministry of Education and non-profit organizations as well as the local community are supporting science education in public schools through improving the infrastructure of science labs, providing schools with scientific equipment and teachers’ training. The Palestinian policy is to encourage students to learn science through lunching different national competitiveness in science field and participating regional and international exams in science field. Along with these procedures, the Palestinian Ministry of Education encourages teachers to integrate technology in science education especially using new emerging technology such as Artificial Intelligence, Augmented Reality, and Virtual Reality. In addition, training teachers to design technological activities and interactive content.
Research on Quality Evaluation of Requirement Analysis Specification Based on Text Similarity Calculation

Aiming at the problem of automatic quality evaluation of requirement analysis specification, an evaluation method based on text similarity is proposed. This method uses the typical process of natural language processing. Firstly, the evaluation document is preprocessed to complete word segmentation and stop word processing; Then, word2vec vector is constructed to replace the original document to be evaluated; Finally, the quality of standard documents is evaluated. Simulation experiments show that, compared with manual evaluation, its efficiency can be greatly improved, but considering the personalized problem of requirement analysis document writing, its evaluation accuracy needs to be further improved.
Teaching Research on Automatic Evaluation System of Japanese Pronunciation Based on Artificial Intelligence

In recent years, with the development of computer technology and the rapid popularization of the Internet, language learning is combined with advanced information technology. Computer-assisted language learning has attracted people’s attention and has become an effective tool in modern language teaching. Compared with traditional classroom-style language teaching, CALL can assist teachers in imparting knowledge, and can also partially or completely replace teachers, building a good autonomous learning platform for learners, thereby eliminating the limitations of traditional language learning teaching methods. This paper focuses on the teaching of automatic evaluation system based on artificial intelligence in Japanese pronunciation, and has a general understanding of the automatic pronunciation evaluation process based on relevant literature data, and then analyzes the needs of the system, and then designs the system according to the needs. A new Japanese pronunciation system based on artificial intelligence evaluation is proposed. Finally, the system is evaluated. The evaluation results show that although the system still has shortcomings, in general, the number of people with better evaluation of system performance more than 34%, generally more than 41%.
Sentiment Classification of Chinese Railway Review Text Based on Multi-Feature Fusion Gated Recurrent Unit

Posting comments through online platforms has become the main channel for many users to express their opinions. Many Chinese private companies have rectified their marketing strategies by collecting online user comments and using them as a reference. However, since many Chinese state-owned enterprises are within the system, they can only rectify and reform according to the plan provided by the government but with little effect. China’s railway system is faced with such problems. Traditional sentiment classification methods may encounter some problems when processing short texts. Our research provides a Gated Recurrent Unit (GRU) sentiment classification method based on multi-feature fusion and sentiment classification of user reviews about China’s railway system. And through classification, we get 1244 comments with negative emotions and 919 non-emotional comments
Matching Applicants with Positions for Better Allocation of Employees in the Job Market

Nowadays most people who are looking for a job use the Internet, visiting websites like Linkedin or Indeed so they must face hundreds of recruitment companies and job ads. The process of applying for a job is time consuming especially in screening, preparing and attending tests and interviews. In addition, job applicants do not know which companies are most proper for them, this job-hunting strategy can easily lead to employment dissatisfaction or failure. therefore, it is more efficient to recommend a few most suitable jobs. Also, manual screening for a position is time consuming and expensive. Experienced recruiters may be able to speed up the process by noting patterns in the resumes. The aim is therefore also to identify these patterns so they can be implemented in the system. In this work we try to introduce a system that would help both recruiters and job applicants in the job-hunting process.
A Multi-Genre Urdu Broadcast Speech Recognition System

This paper reports the development of a multi-genre Urdu Broadcast (BC) corpus and a Large Vocabulary Continuous Speech Recognition (LVCSR) system. BC speech corpus of 98 hours from 453 speakers is collected and annotated. For acoustic modeling, Time-delay Neural Network (TDNN) is developed with prior Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) training and alignments. For the language model, 3-gram, 4-gram and Recurrent Neural Network (RNN) based models are developed on a text corpus of 188 million words. The developed models are tested on 4.3 hours of unseen BC multi-genre speech dataset and the best Word Error Rate (WER) 18.59&#x0025; is achieved using RNN based Language Model (LM). Moreover, a detailed word error analysis is carried out to compare the errors made by humans and the Automatic Speech Recognition (ASR) System. The results showed a similar behavior of word misrecognitions by both humans and ASR.
A Text Similarity Measurement Employs Semantic Dictionary-based Sentiment Analysis

In the age of big data, obtaining demanded information becomes more and more difficult due to continuous emergence of text information and the increasing number of repeated text data. It is even more urgent to use text similarity algorithms to solve the current dilemma. The classification method based on word features is often used as a method for calculating sentence similarity, but it is difficult to obtain accurate results without considering emotional factors. On the other hand, sentence-level similarity calculation and sentiment analysis are also difficult to guarantee to accurately measure the similarity between texts. This work not only considers text similarity based on word features, but also considers text similarity based on emotional tendency. Firstly, the word vector and the Chinese thesaurus are used to measure the text similarity at the word level, and then sentiment analysis is performed at the sentence and paragraph levels based on sentiment lexicon, and finally, according to the above two kinds of similarity to draw a conclusion whether the texts are similar. In the experiment and analysis, several mainstream similarity methods are tested and compared to verify the accuracy, stability and efficiency of the proposed method.
Graph-Based Prototypical Network for Few-Shot Learning

Few-shot learning (FSL) is a technique for learning new class concepts with a limited number of labeled samples, is a key step towards human-level intelligence. Among existing few-shot learning methods, prototypical network shows to be promising in solving the critical problem of overfitting. However, due to the simplicity of average operation in building the prototype representation for each class, the inter- and intra-class relationships among the samples in the support set are not fully exploited, resulting in deviation of the prototype representation from the true class distribution. In this paper, we propose graph-based prototypical network (GPN) model to overcome this problem. In GPN, a fully learnable message passing graph module is proposed to refine the feature embedding vector of each sample. The refined features are then fed into prototypical network to obtain the robust prototype representations of classes. According to experimental results, the proposed method achieves competitive classification accuracy against state-of-the-art ones.
A Performance Evaluation of Distributed Deep Learning Frameworks on CPU Clusters Using Image Classification Workloads

Over the recent years, deep learning is widely being used in a variety of different fields and applications. The constant growth of data used to train complex models, has opened research in the distributed learning. In this domain, two main architectures are used to train models in a distribution fashion, all-reduce and parameter server. Both support synchronous learning, while parameter server also supports asynchronous learning. These architectures are adopted by tech companies, which have developed multiple systems for this purpose. Among the most popular and widely used distributed deep learning systems are Google TensorFlow, Facebook PyTorch and Apache MXNet. In this paper, we quantify the performance gap between these systems and present a detailed analysis to discuss the parameters that affect their execution time. Overall, in synchronous learning setups, TensorFlow is slower compared to PyTorch by average 2.65X, while the latter lags MXNet by average 1.38X. Regarding asynchronous learning, MXNet is faster by average 3.22X in respect with TensorFlow.
A Classification Model of Legal Consulting Questions Based on Multi-Attention Prototypical Networks

Abstract Text classification is a research hotspot in the field of natural language processing. Existing text classification models based on supervised learning, especially deep learning models, have made great progress on public datasets. But most of these methods rely on a large amount of training data, and these datasets coverage is limited. In the legal intelligent question-answering system, accurate classification of legal consulting questions is a necessary prerequisite for the realization of intelligent question answering. However, due to lack of sufficient annotation data and the cost of labeling is high, which lead to the poor effect of traditional supervised learning methods under sparse labeling. In response to the above problems, we construct a few-shot legal consulting questions dataset, and propose a prototypical networks model based on multi-attention. For the same category of instances, this model first highlights the key features in the instances as much as possible through instance-dimension level attention. Then it realizes the classification of legal consulting questions by prototypical networks. Experimental results show that our model achieves state-of-the-art results compared with baseline models. The code and dataset are released on https://github.com/cjm0824/MAPN .
Multi-Agent Collaborative Framework for Automated Agriculture

The use of internet-connected devices, especially small multi-rotor Unmanned Aerial Vehicles (UAVs), in scientific data gathering and applications is quite widespread. But due to limited intervention capability, the UAVs alone fail to automate agricultural tasks completely. Thereby, we propose a centralized framework capable of handling a heterogeneous mixture of UAVs and UGVs to cater to the needs of automating agriculture efficiently. The framework’s core is a novel heuristic decision module that creates new tasks by visually analyzing the farm and solves a vehicle routing problem to allocate it to agents optimally. It is also equipped with supporting modules to monitor their operation and, in case of failures, help them recover autonomously based on the task and agent assessment. The framework is used in three significant agricultural applications, namely yield prediction and drought stress detection in a simulated environment using ROS and Gazebo, and 3D mapping of a real farm. These applications demonstrate the use of the multi-agent collaborative framework in identifying agricultural tasks on a farm and executing them.
Application of data from social networks for value-based management of city development programs

Modern urban space is a complex, relatively autonomous system, the internal integrity and stability of which is created through the interaction of residents, the urban environment and various management systems. The development of a large city, as a dynamic process, requires constant monitoring and expanding the possibilities of human and environmental communication to monitor the needs of residents, as well as rapid response and solution of social and economic problems. The paper considers the potential of using the data of socio-psychological diagnostics of the perception of urban space by users of social networks and the possibility of using this data for the formation of programs for the reorganization of the urban environment infrastructure. Significant correlations between the value-semantic characteristics of the users’ personality and the data of their profiles from social networks for predictive and analytical management of urban space are evaluated. This approach allows us to find the solution of one of the most interesting problems of socio-psychological knowledge - to identify the relationship between social identity, the information environment and the real environment of users in real time, which can create prerequisites for improving successful communication between a person and a city.
Sustainability-based Framework for Virtual Machines Migration Among Cloud Data Centers

Cloud computing services have achieved great popularity and wide adoption thanks to their flexible operation on demand. These services are hosted in Cloud Data Centers that integrate thousands of computers, storage and communication networks whose use entails a high consumption of electrical energy. Renewable energy-based cloud data centers are replacing traditional energy power grids with sustainable energy sources such as wind or photovoltaic energy, for instance. In this respect, in Modular Data Centers the workload could be transferred to different nodes among different cloud data centers geographically distributed based on their access to renewable energy to allow its processing in those locations where renewable energy is available. In this sense, in order to take advantage of this concept of sustainable processing, in addition to efficient load and resource management, this paper presents a framework based on Cloudsim for sustainability-based system for the migration of virtual machines among cloud data centers which can be used in order to simulate different strategies and to make conclusions on its comparison. In addition, to test the behavior the proposed framework, an approach for migrations among datacenters has been tested in several victual machines migration scenarios, so that it is clearly shown how the proposed framework improves previous capabilities of Cloudsim, and shows how follows the renewable policies take advantage of renewable energy in terms of energy consumption, while preserving the QoS in terms of execution time.
Design of Anatomy Multimedia E-learning Platform Based on ToolBook

The anatomy multimedia electronic learning (E-learning) platform is a platform for preparing and displaying courseware for lectures, lectures, and self-study by anatomy teachers. This platform not only solves the problem of self-adaptability and self-upgradability of courseware, but also enables front-line teachers to realize the free organization of teaching content and multimedia materials without writing a single line of program and get rid of development caused by non-programming difficult. The anatomy multimedia e-learning platform uses the ToolBook multimedia development tool as the development environment and uses the object-oriented OpenScript programming language to write programs. It is designed to open two independent “books” on the desktop at the same time based on the common characteristics of morphology courses. One “book” the text script used to input and display the courseware is a “teaching plan” and the other is a morphology “atlas”. The two books can be related to each other through hot words or can be viewed independently. Anatomy multimedia courseware helps teachers to actively control and use the E-learning teaching platform, helps to fully realize E-learning's teaching auxiliary functions, and mobilize teachers’ personalized teaching.
Enhancing Negation Scope Detection using Multitask Learning

Negation is a linguistic phenomenon that usually occurs in a text for denial or refute of some occasion. Detection of such negative assertions is an essential sub-task in various applications of information extraction and data mining. In this paper, we present a deep multitask learning (MTL) framework to enhance the performance of Negation Scope detection using part-of-speech (POS) tagging as an auxiliary task. We show how the relationship between these two tasks, which do not seem to be easily linked from a linguistic point of view, is mutually beneficial.
Towards Transparent Markers

For many years, 2D fiducial markers played one of the most important roles in Augmented Reality (AR) solutions. As AR evolved, this type of marker gave place to markerless alternatives, supported by the increase in processing power of desktop and mobile platforms. Besides that, 2D fiducial markers are still used nowadays. In this work, we proposed to extend their capability by adding transparency to them. This brings different limitations on the marker construction process but on the other hand it enables new uses not possible before. Markers can now be used to manipulate virtual objects being viewed from both sides and they can also be stacked together and still be recognized. We demonstrate such new features with two different real-time samples together with an analysis regarding the main limitations of the solution, providing some application scenarios that could directly benefit from transparent markers.