Reviewerly/exHarmony huggingface dataset:
Towards Text Simplification in Spanish: A Brief Overview of Deep Learning Approaches for Text Simplification

Text simplification refers to the transformation of a specific source text into a target text aiming to increase understanding and readability for one or more specific audiences. This task demands large human efforts and specialized knowledge, which makes the usage of automated or semi-automated computational approaches appealing. The rise of deep learning as an unifying paradigm between seemingly different fields as image analysis, sound processing and natural language processing has considerably influenced the current state of the art approaches for automatic text simplification. Therefore, in this work, we focus on the study of deep learning based state of the art methods for automatic text simplification in the Spanish language. For this end, we first disentangle the different tasks which can be addressed in order to yield a simplified text in general. Later we review the latest deep learning-based approaches, along with the main datasets and performance metrics used in the field. We also describe approaches to deal with small datasets and technical words. Finally, we describe some lessons to build accurate automatic text simplification systems in Spanish, as in this language there is a noticeable shortage of work for text simplification.
Safe Rust Code Recommendation Based on Siamese Graph Neural Network

Rust is an emerging system programming language with attractive features in both memory safety and efficiency. However, its security is guaranteed with safe APIs only. Any usage of unsafe code is still possible to incur critical bugs. Since unsafe code offers much flexibility, such code is often unnecessarily used, especially by Rust beginners. To mitigate such risks, this paper proposes an intelligent approach to detect replaceable unsafe code and recommend its equivalent safe version to developers. To this end, we first collect a dataset of unsafe code snippets based on Rustdoc as our knowledge base, and then we employ the idea of siamese graph neural network to recognize similar code patterns for each unsafe code snippet written by developers and make recommendations accordingly. We have conducted an evaluation experiment with a dataset of real-world samples from GitHub, and results show that our approach can achieve an accuracy of 82.3%.
Systematic Review of the published Explainable Educational Recommendation Systems

The goal of this paper is to systematically review the available literature on the topic of explainable recommendation systems in education, especially when recommendation systems are integrated as a part of learning management systems (LMS). The focus years for analyzing available literature are the years between 2010 and 2022, period when online learning is expanding and online learning platforms are continuously being developed, which makes these years relevant for scoping this review. The topic of interest in this research are recommendation algorithms whose results can be explained and interpreted. The first part of the methodology used in the paper utilizes an NLP-powered toolkit that automates a big part of the review process by automatically analyzing articles indexed in the IEEE Xplore, PubMed, Springer, Elsevier and MDPI digital libraries. The toolkit relies on the PRISMA methodology for standardizing systematic reviews. First, a quantitative analysis of all available literature is performed, followed by a qualitative analysis of the few selected articles which indeed focus on the explainability when implementing recommendation systems in educational context. The relevant articles are analyzed in detail and compared on multiple indicators like the field of work, tools and techniques used, and how explainability is achieved. The results show that although the amount of available research is growing and new learning management systems are being developed at a fast pace in the last few years, the explainability of the machine learning techniques used in the recommendation systems is not a popular topic among the researchers and developers with research interest in educational context. The amount of the available literature for explainable recommendation systems in educational environment is scarce, but is expected to grow following the global trend of explainable artificial intelligence $(\mathrm{x}\mathrm{A}\mathrm{I})$ as key technique for practical implementation of advanced AI models.
fProSentiment Analysis on Mobile Phone Brands Reviews using Convolutional Neural Network (CNN)

Due to the rapid growth of online e-commerce, customers can now voice and express their reviews and thoughts on online products. Therefore, companies who market their product on the e-commerce website will receive thousands of reviews and feedback from their end-users directly on this platform. As the amount of textual data grows tremendously, developing sentiment analysis that automatically analyses text data becomes increasingly vital. It is because reading every review manually can be a tedious task and time-consuming. Analyzing the sentiment for all reviews can provide the companies with an overview of how positive or negative the customers are about their products. The convolutional neural network (CNN) has recently been used for text classification tasks and has achieved impressive results. Hence, this study proposes a CNN method for sentiment analysis to classify the reviews on mobile phone brands. The customer reviews dataset is obtained from the Amazon website. This study combined the Word2Vec-CNN model to predict the sentiment of mobile phone reviews effectively. Pre-trained Word2Vec model is utilized to generate word vectors in word embedding. CNN layers are used to extract better features for sentence categorization to identify the sentiment polarity of the reviews, whether positive or negative. The obtained results give us 88% accuracy and the developed application can also function well in analyzing the sentiment of customers’ reviews.
Facilitating Engineering Students’ Creativity And Teamwork Ethos Using Smart Learning Platforms During Covid-19

Covid-19 has brought some revolutionary changes in Higher Education with a shift from traditional face-to-face teaching and learning to online or hybrid-based delivery. There is a strong emphasis on the integration of technology and smart platforms to deliver an effective teaching and learning environment that can withstand the unpredictable challenges of the ongoing pandemic. But many researchers have noted a lack of social interaction and motivation among students in an online setting. Also, with limited face-to-face interactions, many students had issues with collaboration and other group-based activities. Now with the availability of pre-recorded lecture videos and course materials in several universities, there is a noticeable drop in student engagement. To retain and boost students’ motivation in the current complex environment, there is an added pressure among educators to create teaching content by utilizing smart and innovative teaching approaches that are efficient and effective. Smart learning platforms might offer the potential solutions to address some of the issues with the changing landscape of teaching and learning due to the pandemic. Such platforms are versatile and therefore, can work seamlessly across in-person and virtual teaching and learning environments. They can provide an interactive platform to facilitate active learning and the quality of teamwork experience among students. The objective of this paper is to explore the effectiveness of some of the smart teaching-learning platforms used in the MSc Engineering Management programme in the School of Physics, Engineering and Technology at the University of York. A survey was conducted among the cohort from this programme in the Summer of 2021 to review their engagement and experiences with these platforms. Can such smart platforms facilitate creativity and improve teamwork ethos among students? The paper will discuss the findings of this study and also highlight if such approaches can transform the educational setting post Covid-19.
IoT &amp; ML-Based Healthcare Monitoring System-Review

Intelligent medical management is one of several modern city and society management fields where the Internet of Things (IoT) is essential. Smart cities' current engagement between technology and the health care system is strengthened by the intelligent IoT's limitless networking capabilities for big data analysis in medicine. Allows for more practical methods for efficiently monitoring patients' health and providing medical services remotely online assessment of patients' health status by doctors, nurses, and other healthcare professionals. The abstract of the current study aims to provide a full examination of the function of IoT in medical management systems, analyse the available concerns, and address many of the open questions. It also aims to provide an up-to-date and comprehensive review of this field. Enabling technology and hints at a variety of uses There have been suggested research plans. The following are some examples of IoT applications from previous studies: wearable technology, monitoring technology, rehabilitation technology, telehealth, behaviour modification, smart city, and smart home. This comprehensive review identifies the crucial elements that make it possible to comprehend the healthcare possibilities and obstacles providers to put IoT applications into action. Lastly, anticipated COVID-19 effects on IoT uptake this review assessed in the field of healthcare.
Blockchain Technologies in e-Government Services: A Literature Review

The efficiency of blockchain technologies in managing transactions using distributed ledgers provides new age of government services. This enhances citizen-government transparency to establish public-sector trust by preventing fraud. However, using and adopting blockchain in e-Government context was insufficiently explored in previously published literatures. This paper systematically reviews relevant works to illustrate blockchain issues, challenges, while detecting the new directions for future research of using blockchain applications in e-Government. Discussed literatures explained that adopting blockchain applications to build e-Government services still has obvious lack in empirical evidence. Predominantly, major challenges facing blockchain adoption are summarized in scalability, flexibility and security aspects. organizationally, acceptability issues in blockchain and demanding new models of e-government are illustrated as major obstacles against the adoption. Furthermore, the lack in legislations and helping regulatory represents the major environmental obstacles against the adoption.
Indian Sign Language Translator

Communication is crucial in our day-to-day lives. It is the basis for all human interactions. It is how people pass on information to other people and receive information from them. A person with a speaking or hearing problem cannot communicate properly with others. A person who isdeaf or dumb usually uses Sign Language to communicate with others. Normal individuals who would not know Sign language, on the other hand, cannot communicate better with them. Indian Sign Language acts as a mode of communication for deaf and dumb people who constitute a hefty portion of the Indian population. Most people struggle to apprehend ISL gestures. As a result, communication gap is created between the hearing and speech impaired and those who do not understand ISL. Translation systems are required to help bridge the gap between the community of people withhearing and speechimpairment and the general population. This paper focuses on developing an end-to-end system that can recognize the spoken language and interpret the corresponding speech to animated sign language gestures and that is also capable of converting Indian Sign Language to speech.
Hand Gesture Recognition System in the Complex Background for Edge Computing Devices

Hand gesture recognition offers a wide range of contactless applications. Some applications include designing a sign language recognition system for communicating with people having disabilities, health-care, automobiles, security, etc. For deaf and hard-of-hearing people, sign language recognition is a game-changer and has been studied for years. Unfortunately, each study has its limitations and cannot be used commercially. Some inves-tigations have shown that detecting sign language is possible, but commercialization is prohibitively expensive. This paper investi-gates a robust system to implement hand gesture recognition in a complex background. To test the hand gesture recognition system design, we developed an American sign language recognition for the letters A-J in a complex environment. The MediaPipe Hands framework, used in the developed system, helps successfully detect the hand landmark positions. The machine learning techniques are built on top of the obtained hand landmark positions. The developed system achieves 98.1% accuracy in gesture recognition with an inference time of around 50 ms. Subsequently, the system is successfully ported to Raspberry Pi 4 and NVIDIA's Jetson AGX Xavier and tested.
Top-down Approach to Solving Speaker Diarization Errors in diaLogic System

Speaker diarization, which separates continuous speech signals into utterances associated to different speakers, is critical to any environment that supports team collaboration using models based on data extracted from speech. However, the occurring diarization errors are hard to reduce only through better processing. This paper proposes top-down error correction based on Bayesian prediction about the most likely author of an utterance. Experiments studied the effectiveness of the method.
Progressive Multi-scale Deraining Network

Removing rain streaks from the captured single rainy images plays a dominant role in many computer Vision (CV) applications. Since, many existing deraining methods ignores structural content, introduces artifacts and fails to remove heavy rain streaks completely. To address this issue, a novel PMSDNet was proposed. We introduced an Encoder-Decoder network (UNet) structure in the earlier stagesto extract multi-scale contextual information, whereasfinal stage operates with the Image Original Resolution Network (IORNet) and generates spatially derained outputs accurately. Next a “Feature Fusion Cross Stage” (FFCS) module was inserted between two Encoder-Decoders, Encoder-Decoder and IORNet modules and which makes feature refinements and then propagates to next stage for feature aggregation. At-last an Attention Module (AM) was introduced in between every two stages and generates attention map by suppressing the less informative features at current stage and allows only to propagate useful features to the next stage. Experimental results on synthetic datasets shows that the proposed network achieves state-of-the-art (SOTA) results.
Isolated Word Recognition based on Convolutional Recurrent Neural Network

Nowadays in the modern world, speech recognition has gained elevation with development of AI and many intelligent assistants. The ability of a machine or programme to recognize and react to the sounds made by human speech is known as speech recognition. So, this paper focuses on building Speech Command Recognition system of a single language that can identify predetermined keywords by employing a Convolutional Recurrent Neural Network model to reduce computational requirements. This model combines the capacity of RNN to learn the time dependent features of sequential data and the capability of CNN to learn local features. We compare the performance of convolutional neural network, convolutional recurrent neural network and pretrained network to recognize command words using the Speech Commands Dataset provided by Google's TensorFlow and Artificial Intelligence Yourself teams.
Fracture Extraction From Logging Image Using a Dual Encoder-Decoder Architecture With Swin Transformer

Imaging logging is a method of imaging the physical parameters of the borehole wall or the objects around the borehole according to the observation of the geophysical field in the borehole. Imaging logging data can determine the dip angle and structural characteristics of the formation and observe the geometry and development degree of fractures. The performance of existing target segmentation networks relies on large volumes of data. However, logging images are expensive to acquire, so how to effectively extract fractures from small samples of logging images is an urgent problem to be solved. Therefore, we developed a dual encoder-decoder structure using the Swin Transformer, which uses the self-attention mechanism of a hierarchical Vision Transformer with shifted window to model the remote context information. It can overcome the limitations of most convolutional neural network-based methods that cannot establish long-term dependencies and global contextual connections in convolutional operations. In addition, the shifted window mechanism substantially improves the computational efficiency of the model, and the hierarchical structure allows flexibility in modeling at different scales. At the same time, skip connections are established between adjacent layers of the structure, and the higher-level feature maps are stitched with the lower-level feature maps in channel dimensions, which can obtain more high-resolution detail information of fractures, and thus improve the segmentation accuracy. The experimental results show that the performance is better than the mainstream segmentation networks under small training sets of logging images. The effectiveness of our method reveals that it is practical in fracture extraction of logging images.
A novel approach for flow analysis in software-based networks using L-moments theory

The continuous increase in the number of devices connected to the Internet, together with the growth of applications and services, has made the tasks of network traffic analysis and classification essential in any environment. The deployment of 5G networks has prompted the research community to establish the pillars of Next-Generation Networks. These include intelligent systems, providing the network with intelligence in management and security tasks. In addition, these tasks require mechanisms capable of characterizing traffic in order to make network decisions. In this context, this paper proposes a novel methodology for processing network traffic using the L-moments theory and Machine Learning algorithms. This methodology is robust to outliers, requires few data to characterize the flows and subsequently fit the classification models. The results show that L-moments are particularly useful for processing network flows, and the classification algorithms obtain very high-quality results. Moreover, we show that the considered statistical tools also allow for a better understanding of the attack behaviour, leading the way to the improvement of the feature selection in similar problems.
A Position-Aware Word-Level and Clause-Level Attention Network for Emotion Cause Recognition

Emotion cause recognition is a vital task in natural language processing (NLP), which aims to identify the reason of emotion expressed in text. Both industry and academia have realized the importance of the relationship between emotion word and context. However, most existing methods usually ignore the fact that the position information is also crucial for detecting the emotion cause. When an emotion word occurs in a clause, its neighboring words and clauses should be given more attractive than others with a long distance. In this paper, we propose a novel framework Position-aware Word-level and Clause-level Attention (PWCA) Network based on bidirectional GRU. PWCA not only concentrates on the position information of emotion word, but also builds the relation between emotion clause and candidate clause by leveraging word-level and clause-level attention mechanism. The experimental results show that our model obviously outperforms other state-of-the-art methods. Through the visualization of attention over words, we validate our observation mentioned above.
Stochastic configuration networks with chaotic maps and hierarchical learning strategy

Stochastic configuration networks (SCNs) have universal approximation capability and fast modeling properties, which have been successfully employed in large-scale data analytics. Based on SCNs, Stochastic configuration networks with block increments (BSC) use the node block increments mechanism to improve training speed but increase the complexity of the model. This paper presents a parallel configuration method (PCM), develops an extension of the original BSC with chaos theory and proposes stochastic configuration networks with chaotic maps (SCNCM), and establishes a hierarchical learning strategy (HLS) to enhance the compactness and construction speed of the model. Firstly, PCM randomly assigns the input weights w and biases b of hidden layer nodes by using uniform and normal distributions. In PCM, an iterative learning algorithm is intended to generate the scope control set and improve configuration efficiency. Secondly, the paper presents two kinds of stochastic configuration networks with chaotic maps, which are SCNCM-I and SCNCM-II. SCNCM-I adjusts block size by using multiple error values and chaotic maps to improve the training speed. Based on SCNCM-I, SCNCM-II utilizes node removal mechanism to enhance the compactness. Finally, HLS integrates with SCNCM-I, SCNCM-II, and the Harris-hawks optimization algorithm (HHO). The purpose of training is to enhance the training speed and compactness for three algorithms. The experiments are conducted on four benchmark data sets and an industrial application shows its effectiveness.
Robust entanglement and steering in open Dicke models with individual atomic spontaneous emission and dephasing

In this paper, we study steady-state quantum entanglement and steering in an open Dicke model where cavity dissipation and individual atomic decoherence are taken into account. Specifically, we consider that each atom is coupled to independent dephasing and squeezed environments, which makes the widely-adopted Holstein-Primakoff approximation invalid. By discovering the features of quantum phase transition in the presence of the decohering environments, we mainly find that (i) in both normal and superradiant phases, the cavity dissipation and individual atomic decoherence can improve the entanglement and steering between the cavity field and atomic ensemble; (ii) the individual atomic spontaneous emission leads to the appearance of the steering between the cavity field and atomic ensemble but the steering in two directions cannot be simultaneously generated; (iii) the maximal achievable steering in normal phase is stronger than that in superradiant phase; (iv) the entanglement and steering between the cavity output field and the atomic ensemble are much stronger than that with the intracavity, and the steerings in two directions can be achieved even with the same parameters. Our findings reveal unique features of quantum correlations in the open Dicke model in the presence of individual atomic decoherence processes.