Reviewerly/exHarmony huggingface dataset:
Twitter Sentiment Analysis for Reviewing Tourist Destinations in Saudi Arabia using Apache Spark and Machine Learning Algorithms

The appearance of big data has created new challenges for data analysis teams especially dealing with unstructured data in text form.Many applications increasingly include a large amount of this type of data.Example of such data is data collected from Twitter.Adequate use of Machine Learning (ML), big data tools and social media platforms can solve several problems.The aim of this research is to apply sentiment analysis using Arabic tweets of tourism in Saudi Arabia and determine the most visited places.Ara Senti corpus was used as the labelled data to perform machine learning for sentiment analysis to deal with the Arabic morphology.The three-classes classification (Positive, Negative, or Neutral) was performed using Decision Tree, Random Forest, Logistic Regression and Naïve Bayes.The results showed that the highest performance achieved was 86% using Logistic Regression with Term Frequency-Inverse Document Frequency (TF-IDF) representation and Naïve Bayes with Bag-of-Words model compared with both random forest and decision tree.The trainable classifier was applied to predict classes on collected data from Twitter for reviewing Kingdom of Saudi Arabia (KSA) destinations to finally present a rating of the most visited places on KSA.There are five most visited places in Saudi Arabia (Riyadh, Alula, Hail, Taif and Tabuk).
Semantic Understandings for Aerial Images via Multigrained Feature Grouping

Aerial images play a key role in remote sensing as they can provide high-quality surface object information for continuous communication services. With advances in UAV-aided data collection technologies, the volume of aerial images has been greatly promoted. To this end, semantic understandings for these images can significantly improve the quality of service for smart devices. Recently, the multilabel aerial image classification (MAIC) task has been widely researched in academics and applied in industries. However, existing MAIC methods suffer from suboptimal performance as objects are located in different sizes and scales. To address these issues, we propose a novel multigrained semantic grouping model for aerial image learning, named MSGM. First, image features presented by the backbone are sent to spatial pyramid convolutional layers which extract the instances in a parallel manner. Then, three grouping mechanisms are designed to integrate the instances from the pyramid framework. In addition, MSGM builds a concept graph to represent the label relationship. MSGM resorts to the graph convolutional network to learn the concept graph directly. We extensively evaluate MSGM on two benchmark aerial image datasets, the commonly used UCM dataset, and the high-resolution DFC15 dataset. Quantitative and qualitative results support the effectiveness of the proposed MSGM.
NAVIGATING TEACHING DURING PANDEMIC: THE USE OF DISCUSSION FORUM IN BUSINESS ENGLISH WRITING CLASS

To response the spread of Covid-19, almost all offline education institutes are shut down and e-learning is conducted almost abruptly, including in a Business English writing class for university students. The discussion forum in the LMS ‘Moodle’ is used a lot to help the discussion of this class alive. To widen the knowledge of the use of discussion forum, especially during an emergency time like this Covid-19 pandemic, this qualitative descriptive qualitative research aims to explore how discussion forum is used in the LMS ‘Moodle’ for university students in e-learning during a pandemic, especially concerning optimization of the questions, the arrangement of the topics of discussion, and the assignments. By analyzing the patterns of how the discussion forum is used and interviewing the students, this research found that what determines the student's response to the discussion in the forum is the question words and the key words in the discussion sentences in the forum. These two things will determine students’ answers in terms of the key words used by students, the length and shortness of student answers, and the type and depth of student answers, which in turn show how students interact in the discussion forum.
Nonconvex Nonlocal Tucker Decomposition for 3D Medical Image Super-Resolution

Limited by hardware conditions, imaging devices, transmission efficiency, and other factors, high-resolution (HR) images cannot be obtained directly in clinical settings. It is expected to obtain HR images from low-resolution (LR) images for more detailed information. In this article, we propose a novel super-resolution model for single 3D medical images. In our model, nonlocal low-rank tensor Tucker decomposition is applied to exploit the nonlocal self-similarity prior knowledge of data. Different from the existing methods that use a convex optimization for tensor Tucker decomposition, we use a tensor folded-concave penalty to approximate a nonlocal low-rank tensor. Weighted 3D total variation (TV) is used to maintain the local smoothness across different dimensions. Extensive experiments show that our method outperforms some state-of-the-art (SOTA) methods on different kinds of medical images, including MRI data of the brain and prostate and CT data of the abdominal and dental.
Learning Functional Distributional Semantics with Visual Data

Functional Distributional Semantics is a recently proposed framework for learning distributional semantics that provides linguistic interpretability.It models the meaning of a word as a binary classifier rather than a numerical vector.In this work, we propose a method to train a Functional Distributional Semantics model with grounded visual data.We train it on the Visual Genome dataset, which is closer to the kind of data encountered in human language acquisition than a large text corpus.On four external evaluation datasets, our model outperforms previous work on learning semantics from Visual Genome. 1
KALA: Knowledge-Augmented Language Model Adaptation

Pre-trained language models (PLMs) have achieved remarkable success on various natural language understanding tasks. Simple fine-tuning of PLMs, on the other hand, might be suboptimal for domain-specific tasks because they cannot possibly cover knowledge from all domains. While adaptive pre-training of PLMs can help them obtain domain-specific knowledge, it requires a large training cost. Moreover, adaptive pre-training can harm the PLM’s performance on the downstream task by causing catastrophic forgetting of its general knowledge. To overcome such limitations of adaptive pre-training for PLM adaption, we propose a novel domain adaption framework for PLMs coined as Knowledge-Augmented Language model Adaptation (KALA), which modulates the intermediate hidden representations of PLMs with domain knowledge, consisting of entities and their relational facts. We validate the performance of our KALA on question answering and named entity recognition tasks on multiple datasets across various domains. The results show that, despite being computationally efficient, our KALA largely outperforms adaptive pre-training.
Bridging the Gap between Language Models and Cross-Lingual Sequence Labeling

Large-scale cross-lingual pre-trained language models (xPLMs) have shown effectiveness in cross-lingual sequence labeling tasks (xSL), such as cross-lingual machine reading comprehension (xMRC) by transferring knowledge from a high-resource language to low-resource languages.Despite the great success, we draw an empirical observation that there is a training objective gap between pre-training and finetuning stages: e.g., mask language modeling objective requires local understanding of the masked token and the span-extraction objective requires global understanding and reasoning of the input passage/paragraph and question, leading to the discrepancy between pretraining and xMRC.In this paper, we first design a pre-training task tailored for xSL named Cross-lingual Language Informative Span Masking (CLISM) to eliminate the objective gap in a self-supervised manner.Second, we present ContrAstive-Consistency Regularization (CACR), which utilizes contrastive learning to encourage the consistency between representations of input parallel sequences via unsupervised cross-lingual instance-wise training signals during pre-training.By these means, our methods not only bridge the gap between pretrain-finetune, but also enhance PLMs to better capture the alignment between different languages.Extensive experiments prove that our method achieves clearly superior results on multiple xSL benchmarks with limited pretraining data.Our methods also surpass the previous state-of-the-art methods by a large margin in few-shot data settings, where only a few hundred training examples are available.
Evaluation of Video Coding for Machines without Ground Truth

In the emerging field of video coding for machines, video datasets with pristine video quality and high-quality annotations are required for a comprehensive evaluation. However, existing video datasets with detailed annotations are severely limited in size and video quality. Thus, current methods have to either evaluate their codecs on still images or on already compressed data. To mitigate this problem, we propose an evaluation method based on pseudo ground-truth data from the field of semantic segmentation to the evaluation of video coding for machines. Through extensive evaluation, this paper shows that the proposed ground-truth-agnostic evaluation method results in an acceptable absolute measurement error below 0.7 percentage points on the Bjøntegaard Delta Rate compared to using the true ground truth for mid-range bitrates. We evaluate on the three tasks of semantic segmentation, instance segmentation, and object detection. Lastly, we utilize the ground-truth-agnostic method to measure the coding performances of the VVC compared against HEVC on the Cityscapes sequences. This reveals that the coding position has a significant influence on the task performance.
Local-Global Feature Aggregation for Light Field Image Super-Resolution

Deep convolutional neural networks (CNNs) have been widely explored in light field (LF) image super-resolution (SR) to achieve remarkable progress. However, most of the existing CNNs-based methods ignore the similarity of local neighbor views in the 4D LF data. Besides, due to the limitations of CNNs, these methods can’t fully model the global spatial properties of the whole LF images. In this paper, we propose a network with Local-Global Feature Aggregation (LF-LGFA) to handle these problems for LF image SR. Specifically, the Local Aggregation Module is designed to incorporate the local angular information by utilizing the similarity of the local neighbor views’ features in LF images. Moreover, the Global Aggregation Module is designed to capture long-range spatial information via row-wise and column-wise self-attention. Extensive experimental results on five public LF datasets demonstrate that our method achieves comparable results against state-of-the-art techniques.
LightPose: A Lightweight and Efficient Model with Transformer for Human Pose Estimation

The prediction of keypoints by generating high-resolution heatmaps has become a popular solution in human pose estimation. While this kind of method requires up-sampling or deconvolution operations, which would bring a great challenge to the acceleration of model inference. If performing keypoint prediction on low-resolution heatmaps, the performance is unsatisfied due to serious quantization errors. To solve this contradiction, we propose to perform joint training of the heatmap and center offset on low-resolution heatmaps to reduce quantization errors, which could achieve the comparable performance to the high-resolution heatmap and reduce the computational complexity. In addition, we utilize transformer to enhance the representation ability of low-resolution features, instead of increasing the network layers or the convolution kernel size. The transformer could bring the significant improvement of the performance with little computation cost. Combining the above two modules, we design a new lightweight pose estimation model, named LightPose. Experimental results have shown that, compared with HRNet, our method could achieve the state-of-the-art performance on COCO and MPII datasets with a massive reduction of the parameters by 86% and GFLOPs by 67%.
Hierarchical Deep Learning Model with Inertial and Physiological Sensors Fusion for Wearable-Based Human Activity Recognition

This paper presents a human activity recognition (HAR) system with wearable devices. While various approaches have been suggested for HAR, most of them focus on either 1) the inertial sensors to capture the physical movement or 2) subject-dependent evaluations that are less practical to real world cases. To this end, our work integrates sensing in-puts from physiological sensors to compensate the limitation of inertial sensors in capturing the human activities with less physical movements. Physiological sensors can capture physiological responses reflecting human behaviors in executing daily activities. To simulate a realistic application, three different evaluation scenarios are considered, namely All-access, Cross-subject and Cross-activity. Lastly, we propose a Hierarchical Deep Learning (HDL) model, which improves the accuracy and stability of HAR, compared to conventional models. Our proposed HDL with fusion of inertial and physiological sensing inputs achieves 97.16%, 92.23%, 90.18% average accuracy in All-access, Cross-subject, Cross-activity scenarios, which confirms the effectiveness of our approach.
EPiDA: An Easy Plug-in Data Augmentation Framework for High Performance Text Classification

Recent works have empirically shown the effectiveness of data augmentation (DA) for NLP tasks, especially for those suffering from data scarcity.Intuitively, given the size of generated data, their diversity and quality are crucial to the performance of targeted tasks.However, to the best of our knowledge, most existing methods consider only either the diversity or the quality of augmented data, thus cannot fully tap the potential of DA for NLP.In this paper, we present an easy and plug-in data augmentation framework EPiDA to support effective text classification.EPiDA employs two mechanisms: relative entropy maximization (REM) and conditional entropy minimization (CEM) to control data generation, where REM is designed to enhance the diversity of augmented data while CEM is exploited to ensure their semantic consistency.EPiDA can support efficient and continuous data generation for effective classifier training.Extensive experiments show that EP-iDA outperforms existing SOTA methods in most cases, though not using any agent network or pre-trained generation network, and it works well with various DA algorithms and classification models.Code is available at https: //github.com/zhaominyiz/EPiDA.
RankNEAT

Stochastic gradient descent (SGD) is a premium optimization method for training neural networks, especially for learning objectively defined labels such as image objects and events. When a neural network is instead faced with subjectively defined labels--such as human demonstrations or annotations--SGD may struggle to explore the deceptive and noisy loss landscapes caused by the inherent bias and subjectivity of humans. While neural networks are often trained via preference learning algorithms in an effort to eliminate such data noise, the de facto training methods rely on gradient descent. Motivated by the lack of empirical studies on the impact of evolutionary search to the training of preference learners, we introduce the RankNEAT algorithm which learns to rank through neuroevolution of augmenting topologies. We test the hypothesis that RankNEAT outperforms traditional gradient-based preference learning within the affective computing domain, in particular predicting annotated player arousal from the game footage of three dissimilar games. RankNEAT yields superior performances compared to the gradient-based preference learner (RankNet) in the majority of experiments since its architecture optimization capacity acts as an efficient feature selection mechanism, thereby, eliminating overfitting. Results suggest that RankNEAT is a viable and highly efficient evolutionary alternative to preference learning.
Phone-Informed Refinement of Synthesized Mel Spectrogram for Data Augmentation in Speech Recognition

While recent end-to-end automatic speech recognition (ASR) models achieve high performance, we need to prepare an abundant amount of training data, which is a barrier to apply them to a specific domain. To mitigate the lack of training data, text-to-speech (TTS) systems have been utilized to leverage text-only data to efficiently generate paired data for training the ASR model. The widely-used procedure first generates a Mel spectrogram from text data, then converts it into a waveform, and converts it again to a Mel spectrogram. The vocoder is often used to alleviate the difference between real and synthesized speech, but it requires a huge amount of run-time. In this work, we propose a phone-informed post-processing network that refines Mel spectrograms without using the vocoder. The proposed network consumes not only Mel spectrograms but also text information to use phone sequence information for refinement. Experimental evaluations demonstrate that the proposed network achieves better WERs than the vocoder network in an English domain adaptation task (LibriSpeech to TED-LIUM 2; read speech to spontaneous speech) in a much smaller amount of data generation time. It is also shown the use of phone information is critical for the improvement. We also confirm the effect of the proposed model in a Japanese domain adaptation task (CSJ-SPS to CSJ-APS; everyday topic to academic topic).
Towards Comparison and Real Time Implementation of Path Planning Methods for 2R Planar Manipulator with Obstacles Avoidance

The main requirement of parametric path planning techniques in robot manipulators is to create continuous, smooth, and easy-to-modify path such as to move the end-effector from start point to destination point. To meet these requirements, the rational Bezier and NURBS algorithms have been proposed for path planning of 2R manipulator in environment with known and static obstacles. In this study, a comparison in terms of path length and time consumption of algorithm has been conducted to show the superior of one method to another. Based on numerical simulation, it has been shown that the rational Bezier algorithm generates shorter path and takes less time to complete the path planning task as compared to NURBS method. In addition, this study presented the design of real-time set-up based on Arduino UNO microcontroller and micro-stepping actuators. It has been shown that the experimental results could successfully verify the numerical results for both proposed path planning methods for different configuration of obstacles.
Design of a Robust System Architecture for Tracking Vehicle on Highway Based on Monocular Camera

Multi-Target tracking is a central aspect of modeling the environment of autonomous vehicles. A mono camera is a necessary component in the autonomous driving system. One of the biggest advantages of the mono camera is it can give out the type of vehicle and cameras are the only sensors able to interpret 2D information such as road signs or lane markings. Besides this, it has the advantage of estimating the lateral velocity of the moving object. The mono camera is now being used by companies all over the world to build autonomous vehicles. In the expressway scenario, the forward-looking camera can generate a raw picture to extract information from and finally achieve tracking multiple vehicles at the same time. A multi-object tracking system, which is composed of a convolution neural network module, depth estimation module, kinematic state estimation module, data association module, and track management module, is needed. This paper applies the YOLO detection algorithm combined with the depth estimation algorithm, Extend Kalman Filter, and Nearest Neighbor algorithm with a gating trick to build the tracking system. Finally, the tracking system is tested on the vehicle equipped with a forward mono camera, and the results show that the lateral and longitudinal position and velocity can satisfy the need for Adaptive Cruise Control (ACC), Navigation On Pilot (NOP), Auto Emergency Braking (AEB), and other applications.
Trends and prospects for IT education in Industry 4.0

The fourth industrial revolution (Industry 4.0) has brought about changes in various aspects of human life. One of them is the education system. The article explores the impact of Industry 4.0 on the field of education. In this regard, the main characteristics of Industry 4.0, driven by artificial intelligence and digital physical structures are explored using distributed computing, big data, portable devices, the Internet of Things (IoT), virtual reality (VR), augmented reality (AR), etc. Moreover, the article analyzes the main technologies of Education 4.0, which play an important role in supporting Industry 4.0 and have a significant impact on the change in IT education. The article shows the main engineering competencies of Industry 4.0 specialists to be knowledge, skills and abilities necessary to successfully complete production tasks. Future engineers need to improve their professional, social, methodological and personal competencies. They need an interdisciplinary understanding of systems, manufacturing processes, automation technology and information technology. The article uses methods of comparative analysis, generalization and systematic approach to the peculiarities of using Industry 4.0 technologies in the field of e-education. The results obtained are expected to be used by specialists, managers and teachers to improve the educational performance of IT specialists in higher education institutions.
Secured Electronic Voting System Using RSA Key Encapsulation Mechanism

A secured process whereas enumerating and casting of votes with the electronic aids with aim of improving the performance of electoral system is called E-voting (Electronic voting). Present e-voting mechanisms are faced with challenges such as confidentiality, integrity, verifiability, transparency, non-repudiation, and authenticity. In e-voting system, blockchain cryptography exists to protect voter’s records. Encryption as well as decryption prevents forgery and other electoral malpractices by confirming voter’s coherence and confidentiality. Rivest, Shamir, Adleman (RSA) is a crooked algorithm with trapdoor functionality which makes it difficult to factor a huge prime whole number as the product of multiplication into its component primes. RSA – Key Encapsulation Mechanism describes a hybrid encryption that makes use of unrelated keys for decryption and encryption along with a key derivation function. In this paper, a secured voting system using RSA Key Encapsulation Mechanism with two layers-Symmetric and Public key Layers was presented. RSA consist of three functions such as key generation, encryption, and decryption. In RSA-KEM, KDF3 built upon SHA-256, induction function is mainly for key generation. AES key wrap (Advanced-Encryption-Standard) provides key-wrapping functionality.