Miracle-dz/UniFAR-FTUs huggingface dataset:
User Generated Content and Engagement Analysis in Social Media case of Algerian Brands

Nowadays, online social media hugely inﬂu-ences individuals’ daily lives, companies, institutions, and governments. Analyzing the on-line social content related to the productivity of any company becomes crucial to manage and supervise its activities and future trends. We investigate the quality of social signals and content related to Algerian products and services to enhance their exploitation and deployment. Our investigation relies on the statistical analysis of social signals and the textual analysis of User-Generated Contents (Posts and Comments). The current work has been done on a sample of more than 50 brands gathering products and services on Facebook with 10 K posts and their related comments totaling around 100 K. We measure Users/Brand Engagement Rates (ER) considering reactions and content. We adopted a statistical analysis for the reaction-based measurement. We leveraged an LDA-based Topic Modeling Approach for content-based measurement. Our ﬁndings emphasize the signiﬁcance of the existing social signals and user-generated content in the Algerian context.
The Quality of Lexical Semantic Resources: A Survey

With the increase of the lexical-semantic resources built over time, lexicon content quality has gained significant attention from Natural Language Processing experts such as lexi-cographers and linguists. Estimating lexicon quality components like synset lemmas, synset gloss, or synset relations are challenging research problems for Natural Language Processing. Several lexicon content quality approaches have been proposed over years in order to enhance the work of many applications such as machine translation, information retrieval, word sense disambiguation, data integration, and others. In this research, a survey for evaluation the quality of lexical semantic resources is presented
ITAcotron 2: Transfering English Speech Synthesis Architectures and Speech Features to Italian

End-to-end deep learning models have pushed forward signiﬁcantly many tasks of Natural Language Processing (NLP). However, most of these models are trained for languages providing many resources (such as English), and their behaviour is hardly studied in other languages due to resource shortage. To cope with these situations, it is common practice to employ transfer learning . With this work, we wanted to explore the cross-language trans-ferability of a Text-to-Speech (TTS) architecture and the re-usability of the surrounding components that complete a speech synthesis pipeline. To do so, we ﬁne-tuned an English version of the Tacotron 2 TTS, with speaker conditioning, to Italian (hence ITAcotron 2 ). The human evaluation –carried on 70 subjects– showed that the language adaptation was indeed successful.
Identifying and Understanding Game-Framing in Online News: BERT and Fine-Grained Linguistic Features

News providers tend to add an entertaining and eye-catching spin to online news stories by framing politics around strategies and tactics as well as wins and losses. Thus, they shape the reader’s view on a particular subject, person, or event and influence their behavior regarding, for instance, voting respectively. To address this issue, we introduce the first attempt in computational linguistics to model computational frame classification. We offer a human-labeled dataset indicating the issue-game framing in news media using a comprehensive range of linguistic features. Moreover, we present an overview of potential linguistic indicators of issue and game frames. Furthermore, we attempt to provide meth-ods for analyzing, understanding, and flag-ging problems that deal with subjectivity with respect to framing by identifying and presenting the respective cues. We use BERT, a pre-trained word representation model, and fine-tune it with our dataset on the binary text classification task. It turns out that BERT-like approaches can be used to detect issue and game frames. However, studies utilizing more annotated training data should be conducted to investigate its universal effectiveness. We put forward some suggestions on the grammatical features that could be taken into consideration in the further development of similar language models.
Static Fuzzy Bag-of-Words: a Lightweight and Fast Sentence Embedding Algorithm

The introduction of embedding techniques has pushed forward signiﬁcantly the Natural Language Processing ﬁeld. Many of the proposed solutions have been presented for word-level encoding; anyhow, in the last years, new mechanisms to treat information at a higher level of aggregation, like at sentence-and document-level, have emerged. With this work, we address speciﬁcally the sentence embeddings problem, presenting the Static Fuzzy Bag-of-Word model. Our model is a reﬁnement of the Fuzzy Bag-of-Words approach, providing sentence embeddings with a ﬁxed dimension. SFBoW provides competitive performances in Semantic Textual Similarity benchmarks while requiring low computational resources.
A New Approach for Arabic Text Summarization

Due to the increasing number of online textual information, acquiring relevant information quickly has become a challenging task. Automatic text summarization (TS) offers a powerful solution for the quick exploitation of these resources. It consists of producing a short representation of an input text while preserving its relevant information and overall meaning. Automatic text summarization has seen a great attention for Indo-European languages. However, for Arabic, researches in this field have not yet attaint a notable progress. Most of the existing approaches in Arabic text summarization literature rely mainly on numerical techniques and neglect semantic and rhetorical relations connecting text units. This affects negatively the global coherence of the generated summary and its readability. In this paper, we attempt to overcome this limitation by proposing a new approach that combines a rhetorical analysis following the rhetorical structure theory (RST) and a statistical-based method. The proposed approach relies on exploiting rhetorical relations linking text units to generate a primary summary, which will be pass by a second phase where a statistical processing is applied in order to produce the final summary. Evaluation results on Essex Arabic Summaries Corpus (EASC) using ROUGE-N measures are very promising and prove the effectiveness of the proposed approach.
Arabic Named Entity Recognition Using Transformer-based-CRF Model

Named Entity Recognition is an essential component of Natural Language Progressing with countless practical applications. Several different directions of research are currently being pursued, exploring the possibilities in various ways. One particularly fruitful approach is the localization of multilingual deep learning tools based on the BERT architecture, with AraBERT and ARBERT/MARBERT serving as good examples. In this paper, we propose a simple but effective model for Arabic named entity recognition. The architecture of this model consists of three layers, as follows: a transformer-based language model layer, a fully connected layer, and the last layer is a conditional random field(CRF). Our proposed model shows promising results compared to the current state-of-art Arabic-NER models. For example, the F1-macro of the test data scores approximately 89.6% on the ANERCorp and 88.5% on the AQMAR datasets.
Propofol effects in rodent models of traumatic brain injury: a systematic review

Abstract Background Traumatic brain injury (TBI) causes high mortality and disability worldwide. Animal models have been developed to explore the complex processes in TBI. Propofol is used to manage head injuries during surgical intervention and mechanical ventilation in patients with TBI. Many studies have investigated the neuroprotective effect of propofol on TBI. However, other studies have shown neurotoxic effects. Objectives To review systematically the literature regarding the neuroprotective and neurotoxic effects of propofol in rodent models of TBI. Methods Data from rodents as models of TBI with propofol as one of the intervention agents, and/or comparing the neuroprotective effects of propofol with the other substances in rodent models of TBI, were obtained from PubMed, EBSCO Host, and ProQuest databases. The PRISMA 2020 statement recommendations were followed and research questions were developed based on PICOS guidelines. Data was extracted from the literature using a standardized Cochrane method. Results We analyzed data from 12 articles on physiological changes of experimental animals before and after trauma, the effects of propofol administration, and the observed neurotoxic effects. The effects of propofol administration were observed in terms of changes in traumatic lesion volume, the release of antioxidants and inflammatory factors, and the neurological function of rodent models of TBI. Conclusion Propofol has neuroprotective and neurotoxic effects via several mechanisms, and various doses have been used in research to determine its effects. The timing of administration, the dose administered, and the duration of administration contribute to determine the effect of propofol in rodent models of TBI. However, the doses that produce neuroprotective and neurotoxic effects are not yet clear and further research is needed to determine them.
Smart Solutions to Climate Change: RANKING THE OPPORTUNITIES

List of figures List of tables List of contributors Acknowledgements Introduction Bjorn Lomborg Part I. The Solutions: 1. Climate engineering Eric Bickel and Lee Lane 1.1 Alternative perspective Roger Pielke Jr 1.2 Alternative perspective Anne E. Smith 2. Carbon dioxide mitigation Richard S. J. Tol 2.1 Alternative perspective Onno Kuik 2.2 Alternative perspective Roberto Roson 3. Forestry carbon sequestration Brent Sohngen 3.1 Alternative perspective Sabine Fuss 4. Black carbon mitigation Robert E. Baron, W. David Montgomery and Sugandha D. Tuladhar 4.1 Alternative perspective Milind Kandlikar, Conor C. O. Reynolds and Andy P. Grieshop 5. Methane mitigation Claudia Kemfert and Wolf-Peter Schill 5.1 Alternative perspective David Anthoff 5.2 Alternative perspective Daniel Johansson and Fredrik Hedenus 6. Market and policy driven adaptation Francesco Bosello, Carlo Carraro and Enrica de Cian 6.1 Alternative perspective Samuel Fankhauser 6.2 Alternative perspective Frank Jotzo 7. Technology-led climate policy Isabel Galiana and Christopher Green 7.1 Alternative perspective Valentina Bosetti 7.2 Alternative perspective Gregory Nemet 8. Technology transfers Zili Yang 8.1 Alternative perspective David Popp Part II. Ranking the Opportunities: 9. Expert panel ranking Nancy L. Stokey, Vernon L. Smith, Thomas C. Schelling, Finn E. Kydland and Jagdish N. Bhagwati Conclusion Bjorn Lomborg Index.
Research of Automatic Speech Recognition of Asante-Twi Dialect For Translation

This paper presents a new way of building low-resourced dialect Automatic Speech Recognition (ASR) systems using a small database using the Asante-Twi dialect. Three different ASR systems with different features and methods have been tested and tried using the Kaldi toolkit. For the first and second Asante-Twi ASR systems, Mel Frequency Cepstral Coefficients (MFCC) feature extraction method was used with different context dependent parameters for each and the Perceptual Linear Prediction(PLP) feature extraction method was used for the third ASR system. To enhance the performance of the ASR systems, all feature extraction methods of the systems are improved using Cepstral Mean and Variance Normalization (CMVN) and Delta(&Dgr;) dynamic features. In addition, the acoustic model unit of each ASR system using the GMM-HMM pattern classifier algorithm has been improved by training two context dependent (triphone) models, one on top of the other, and both on top of context independent (monophone) models to deliver better performance. Word Error Rate(WER) is used as metrics for measuring the accuracy performance of the systems. As the correct parameter setting for triphone models were used, the second ASR system saw about 50% reduction in %WER for the first triphone model and about 10% reduction in %WER values for the second triphone model as compared to the first ASR system. Decoding results show that the second ASR system was the most efficient system of all the ASR systems in percent WER because it produced the lowest value of 5.15% WER obtained from context dependent triphone models. The third ASR system, using the same triphone parameters as the second ASR, was the worst performing of all three. Thus, MFFCs are found to be the most suitable feature extraction technique when using noise-free data with context-dependent acoustic models being the best method for GMM-HMM acoustic modeling on a limited amount of data.
Application Research of end to end behavior decision based on deep reinforcement learning

At present, the control method of driverless vehicle mainly adopts artificial rule making and behavior decision-making, which is difficult to adapt to the new scene. To solve this problem, this paper proposes an end-to-end behavior decision-making method, which uses deep reinforcement learning to interact with the environment and learn the decision model. The model has good generalization. The main reinforcement learning algorithms used in this paper are PPO, SAC, A2C, DDPG and TRPO algorithm, etc. This paper is based on the unity 3D virtual engine to build the scene of vehicle search target. The experimental results show that DDPG algorithm has better effect, the better generalization performance, the shortest time to find the target and the fastest training speed.
An Effective Structure in Single Image Super-resolution

Over the years, with the development of televisions, computers, mobile phones and other equipment, the requirements for clear images, or high-resolution images, have become higher and higher. However, the transmission of high-resolution images is limited by hardware, storage space, and bandwidth, and is not suitable for frequent information interaction. Therefore, if super-resolution reconstruction of low-resolution images can be achieved at the receiving end, the above-mentioned problems can be avoided. At present, the super-resolution technology is relatively mature and is widely used in aerospace, medical imaging, monitoring equipment and other fields. In this paper, a convolutional neural network is proposed, which will be explained in detail later. The network training uses DIV2K, and the test sets are Set5, Set14 and Urban100. Experimental results show that the network can clearly reconstruct low-resolution images into high-resolution images, and can retain the original information while enlarging. In this sample-structured document, neither the cross-linking of float elements and bibliography nor metadata/copyright information is available. The sample document is provided in "Draft" mode and to view it in the final layout format, applying the required template is essential with some standard steps.
JFD: Automatic Java Fuzz Driver Generation

Java is widely used in many areas. There have been a lot of works on improving the security of Java. In the industry, fuzzing is the most efficient software testing technique to discover real-world vulnerabilities and improve software security. Recent efforts are seen to make library fuzzing more automatically and have performed well in general library fuzzing. However, these tools cannot solve problems in Java library fuzzing appropriately. In this paper, we present JFD, an automatic Java fuzz driver generation system, which can generate fuzz drivers based on consumer programs, that utilize target library programs. Our approach consists of three parts: a static-analysis-based method to analyze call dependencies graphs of target Java library, a value-set-based method to analyze argument dependencies graphs of target Java library, and a method to synthesize fuzz drivers in the style of JQF. We then evaluate JFD on JVM native libraries. JFD can generate appropriate fuzz drivers.
A novel music genre classification algorithm based on Continuous Wavelet Transform and Convolution Neural Network

The automated genre categorization of musical audio signals plays a fundamental role in the application, spectral characteristics that have been averaged across a large number of audio frames are estimated by Continuous Wavelet Transform (CWT) and then converted to a gray-scale picture for training and classification. A Convolution Neural Networks (CNN) model is proposed in this paper and the results demonstrated that the classification accuracy of the proposed CWT+ CNN model is about 70% with the testing sample and 94% with the training sample. This may suggest good application potential for the music genre classification.
Analyses of Production Line Controlled with Kanban Methode

The challenges of choosing methods of material flow control are bigger due to the fact that they authorize the quantity of production in each processing stage so that the products get to the customer at the right moment and in the quantity wanted, while minimizing the costs and stocks of the production system. For this reason, the objective of the research performed in this paper is to determine the impact of Kanban control methods on the performance indicators, total cost and interoperation stock of a production system, given the fact that many system parameters are variable. The main conclusion drawn from the results is that when the input parameters vary within certain limits the control method with the best performance indicators for a production line can be established following the methodology used in this paper. Methodology is a useful tool in the hand of those who design production systems because it allows the choice of the control method which provides the best performances, at least from the viewpoint of two indicators: costs and stocks.
Design of a Novel Neural Network Compression Method for Tiny Machine Learning

Traditional IoT processing data is sent from local devices to the cloud for processing, which has disadvantages such as low privacy, high latency, and low energy efficiency. These drawbacks can be effectively remedied by deploying the model for processing on devices at the "edge" of the cloud. In order to realize the data being processed directly on the cloud "edge" devices, the original machine learning algorithms need to be improved. One of the important steps is neural network compression. In this paper, a neural network compression method for Tiny Machine Learning (TinyML) is proposed. The neural network is compressed by training a conventional neural network and then performing group convolution, pruning and asymmetric ternary quantization. In the next step, a model transformation is performed using TFLite to deploy it on embedded devices. With this novel neural network compression method, the size of the model can be greatly compressed with guaranteed accuracy. Consequently, the traditional machine learning is upgraded to TinyML, and finally a TinyML-based fall monitoring system for the elderly is built.
Target Warning System of Fusion IR & UV Data Based on Neural Network

Target warning is a research field in constant evolution due to the new technological advances. The principle of UV warning system and IR warning system restrict the application and development of target waning. Target warning system based on fusion data is a main development trend. This study focused on dual-color warning system of fusion IR data & UV data, aimed at complementing the advantages of the two target warning methods. At the same time, the neural network technology is introduced to complete the fusion and correction, and complete the calculation of the target position. The test results show that the dual-color warning system effectively improves the ability of the system, and the target warning angle resolution is better than 1°. Dual-color warning system based on neural network proved to achieve the purpose of increasing the detection probability and reducing the false warning rate.
Research and Implementation of Forest Fire Smoke Detection Based on ResNet Transfer Learning

Frequent forest fires damage the ecological environment and affect people's lives. Early monitoring of forest fire can significantly reduce the damage. Many methods have proposed by researchers for fire detection, but most of them are based on traditional image feature extraction. In the paper, we present a forest fire detection approach based on ResNet-18 transfer learning which can be used to train a model with a smaller dataset. First, image preprocessing methods were used to enlarge the dataset, which include random cropping and random erase. Secondly, the pre-trained ResNet-18 model is fine-tuned using the target train set. Experiments shows that the fine-tuned ResNet-18 model can effectively recognize the fire or smoke in the image, which got the accuracy of 94.33% on test set. Also, image preprocessing which contains random cropping and random erase can improve the performance of the fine-tuned ResNet-18 model.
An Improved Tightly-Coupled Monocular Visual-Inertial Location Algorithm

Both direct and optical flow visual odometers are based on a strong assumption that the gray scale is invariant. Because of this assumption, the system is sensitive to the luminosity change of the image. To solve this problem, this paper proposes monocular visual-inertial odometry exploiting both multilevel Oriented Fast and Rotated Brief (ORB) feature and tightly-coupled fusion strategy. The purpose of this method is to improve the speed and robustness of matching. Furthermore, it also can build a high-precision initialization map to ensure the successful initialization of the whole system and the smooth operation of the subsequent. This paper pre-integrates Inertial Measurement Unit (IMU) data and constructs constraints with visual reprojection to optimize the solution. The experiments evaluated on public datasets demonstrate the multilevel ORB feature and the IMU fusion make the algorithm more accurate than other excellent projects. Compared with OKVIS and VINS-Mono using the visual and inertial fusion, the algorithm also performs better in the accuracy of state estimation and system robustness.
Visual odometery for UAV navigation based on deep learning

Taking UAV as the application background, this paper studies the visual odometery for deep learning in UAV navigation. Firstly, the dataset FLYING is made through UAV aerial photography. Secondly, pretraining and testing the established G-LSTM VO and attention VO models through the KITTI dataset. Thirdly, by means of transfer learning, training and testing the pre trained model based on FLYING dataset. Finally, the model is tested. The performance of the model in UAV mission is analyzed from the aspects of trajectory, pose estimation accuracy and algorithm time-consuming. The experimental results show that the monocular visual odometery method based on depth neural network is effective in the field of UAV navigation.
Recurrent SinGAN: Towards Scale-Agnostic Single Image GANs

Learning a deep generative model on a single image has attracted considerable attention recently. In this paper, we present a single image generative model, named recurrent SinGAN, based on the recent proposed SinGAN [22]. The original SinGAN relies on a pyramid architecture of generators to learn the patch distribution from the pyramid of multiple image scales. We propose a recurrent generator to replace the pyramid of generators in SinGAN with a single recurrent generator. Our single recurrent generator can learn the patch distributions across multiple scales, yielding a scale-agnostic single image generative model. On the image synthesis task, our recurrent SinGAN performs on par with the original SinGAN, however our method reduces the training time by almost 60% and has 4.5x fewer parameters. Moreover, the results on various image manipulation tasks, including paint-to-image, image editing, harmonization, and image super-resolution, further verify the effectiveness of our proposed method.