Reviewerly/exHarmony huggingface dataset:
Reevaluating Synthesizing Sentiment Analysis on COVID-19 Fake News Detection using Spark Dataframe

Some research uses the random forest model and sentiment analysis to detect COVID-19 fake news. However, there is still a research opportunity to apply the method to Indonesian Tweets and reevaluate the feature's performance. Our research aims to reevaluate synthesizing the sentiment analysis feature on detecting COVID-19 fake news on Indonesian Tweets by using the Spark Dataframe. We divide the stages of machine learning development into several steps, including collecting data using Tweepy and then applying sentiment polarity scores using Apache Spark. We apply random forest to classify fake news using the Spark MLlib. Further, we use model evaluation calculation through the level of Accuracy, Recall, Precision, and F1. The results show that applying the sentiment polarity calculation to our Tweet dataset labels 148 Tweets with positive sentiments, 118 Tweets with negative sentiments, and 99 Tweets with neutral sentiments. The Pearson correlation coefficient (PCC) feature score of Sentiment equals 0.056 and ranks fifth in the top feature correlation scores list. According to the experimental findings, the random forest model produces Accuracy = 0.787 for both models with sentiment analysis and without sentiment analysis. Which indicates that sentiment analysis provides no significance in the prediction model.
Human activity recognition with self-attention

In this paper, a self-attention based neural network architecture to address human activity recognition is proposed. The dataset used was collected using smartphone. The contribution of this paper is using a multi-layer multi-head self-attention neural network architecture for human activity recognition and compared to two strong baseline architectures, which are convolutional neural network (CNN) and long-short term network (LSTM). The dropout rate, positional encoding and scaling factor are also been investigated to find the best model. The results show that proposed model achieves a test accuracy of 91.75%, which is a comparable result when compared to both the baseline models.
GMLM-CNN: A Hybrid Solution to SWIR-VIS Face Verification with Limited Imagery

Cross-spectral face verification between short-wave infrared (SWIR) and visible light (VIS) face images poses a challenge, which is motivated by various real-world applications such as surveillance at night time or in harsh environments. This paper proposes a hybrid solution that takes advantage of both traditional feature engineering and modern deep learning techniques to overcome the issue of limited imagery as encountered in the SWIR band. Firstly, the paper revisits the theory of measurement levels. Then, two new operators are introduced which act at the nominal and interval levels of measurement and are named the Nominal Measurement Descriptor (NMD) and the Interval Measurement Descriptor (IMD), respectively. A composite operator Gabor Multiple-Level Measurement (GMLM) is further proposed which fuses multiple levels of measurement. Finally, the fused features of GMLM are passed through a succinct and efficient neural network based on PCA. The network selects informative features and also performs the recognition task. The overall framework is named GMLM-CNN. It is compared to both traditional hand-crafted operators as well as recent deep learning-based models that are state-of-the-art, in terms of cross-spectral verification performance. Experiments are conducted on a dataset which comprises frontal VIS and SWIR faces acquired at varying standoffs. Experimental results demonstrate that, in the presence of limited data, the proposed hybrid method GMLM-CNN outperforms all the other methods.
Internet Video Delivery Improved by Super-Resolution with GAN

In recent years, image and video super-resolution have gained attention outside the computer vision community due to the outstanding results produced by applying deep-learning models to solve the super-resolution problem. These models have been used to improve the quality of videos and images. In the last decade, video-streaming applications have also become popular. Consequently, they have generated traffic with an increasing quantity of data in network infrastructures, which continues to grow, e.g., global video traffic is forecast to increase from 75% in 2017 to 82% in 2022. In this paper, we leverage the power of deep-learning-based super-resolution methods and implement a model for video super-resolution, which we call VSRGAN+. We train our model with a dataset proposed to teach systems for high-level visual comprehension tasks. We also test it on a large-scale JND-based coded video quality dataset containing 220 video clips with four different resolutions. Additionally, we propose a cloud video-delivery framework that uses video super-resolution. According to our findings, the VSRGAN+ model can reconstruct videos without perceptual distinction of the ground truth. Using this model with added compression can decrease the quantity of data delivered to surrogate servers in a cloud video-delivery framework. The traffic decrease reaches 98.42% in total.
Real-time wavefront correction using diffractive optical networks

Real-time wavefront correction is a challenging problem to present for conventional adaptive optics systems. Here, we present an all-optical system to realize real-time wavefront correction. Using deep learning, the system, which contains only multiple transmissive diffractive layers, is trained to realize high-quality imaging for unknown, random, distorted wavefronts. Once physically fabricated, this passive optical system is physically positioned between the imaging lens and the image plane to all-optically correct unknown, new wavefronts whose wavefront errors are within the training range. Simulated experiments showed that the system designed for the on-axis field of view increases the average imaging Strehl Ratio from 0.32 to 0.94, and the other system intended for multiple fields of view increases the resolvable probability of binary stars from 30.5% to 69.5%. Results suggested that DAOS performed well when performing wavefront correction at the speed of light. The solution of real-time wavefront correction can be applied to other wavelengths and has great application potential in astronomical observation, laser communication, and other fields.
PerHeFed: A general framework of personalized federated learning for heterogeneous convolutional neural networks

Abstract In conventional federated learning, each device is restricted to train a network model of the same structure. This greatly hinders the application of federated learning where the data and devices are quite heterogeneous because of their different hardware equipment and communication networks. At the same time, existing studies have shown that transmitting all of the model parameters not only has heavy communication costs, but also increases risk of privacy leakage. We propose a general framework for personalized federated learning (PerHeFed), which enables the devices to design their local model structures autonomously and share sub-models without structural restrictions. In PerHeFed, a simple-but-effective mapping relation and a novel personalized sub-model aggregation method are proposed for heterogeneous sub-models to be aggregated. By dividing the aggregations into two primitive types (i.e., inter-layer and intra-layer), PerHeFed is applicable to any combination of heterogeneous convolutional neural networks, and we believe that this can satisfy the personalized requirements of heterogeneous models. Experiments show that, compared to the state-of-the-art method (e.g., FLOP), in non-IID data sets our method compress ≈ 50 % of the shared sub-model parameters with only a 4.38% drop in accuracy on SVHN dataset and on CIFAR-10, PerHeFed even achieves a 0.3% improvement in accuracy. To the best of our knowledge, our work is the first general personalized federated learning framework for heterogeneous convolutional networks, even cross different networks, addressing model structure unity in conventional federated learning.
STUDENT PERCEPTIONS OF AI-POWERED WRITING TOOLS: TOWARDS INDIVIDUALIZED TEACHING STRATEGIES

Due to the advances of artificial intelligence (AI) and natural language processing, new kinds of Internet-based writing tools have emerged.Among other things, these AI-powered writing tools can be used by students for text translation, to improve spelling or for rewriting and summarizing texts.On the one hand, they can provide detailed recommendations for the adaptation of text elements within seconds.On the other hand, they also produce inconsistencies and errors, that students might not be aware of.How to deal with these tools in an educational context is a difficult question.Since writing tools are usually used unsupervised and without further instructions, students may need guidance from the teacher in interacting with those tools, to prevent the risk of misapplication.To better understand this underlying issue, the paper at hand uses survey data of 365 freshmen students to describe and analyze student perceptions of AI-powered writing tools.Regarding AI-powered writing tools, different student types were identified by using the k-means clustering method.The results suggest that students have different attitudes towards AI-powered writing tools.Some students may use them in an unreflective, non-sceptical way, which can lead to (un)voluntary plagiarism.Other students may not use writing tools at all, partly because they are sceptic, but also because they may lack meaningful learning strategies in general (course repeaters).Depending on the different student types, individualized teaching strategies might be helpful to promote or urge caution in the use of these tools.
Intelligence sharing in big data forensics

With the high prevalence of digital crimes, forensic investigators rely on traditional desktop tools to conduct investigations. Most of these tools are device-specific and majority of them are desktop-based therefore they suffer from limited storage and fail to process big data. These tools also lack the analytical ability to link evidence between cases or share information between cases. Therefore, inter-links can exist between cases without being detected. The poor ability to detect links between cases may result in investigators: taking a long time to complete investigations and failing to establish organised crimes. In this paper, we propose a novel solution that can cross-link evidence between cases. Our solution is not desktop-based, nor is it restricted by the evidence source. Using real-world data for evaluation, we demonstrate that our solution is capable of uncovering evidence common between cases that could otherwise be missed.
Neo4j: Graph Database

Most of the previous databases you’ve been exposed to throughout this book store information in a similar structure. A diagram or tool is necessary to help you understand how they organize information. But what happens when another type of organizing data is available—one that makes representation and access more easily digested?
The Effect of Technology-Oriented Differentiated Instruction on Motivation to learn Science

Integrating technology into teaching approaches has become one of the most effective strategies in teaching development.Therefore, this study aimed to investigate the impact of technology-oriented Differentiated Instruction on thirdgrade students' motivation to learn science.In order to achieve the goal of the study, the quasi-experimental approach was used, the motivation scale for science learning was prepared, consisting of (29) items, distributed on four dimensions, which are: the academic dimension, the social dimension, self-organized learning, the challenge and curiosity preference, and an instructional plan was prepared according to the Technology-oriented Differentiated Instruction strategy.The study sample was chosen by the intentional method, as the number of the sample reached (58) students of the third grade at the Al-Hussein Bin Talal University Applied School in Ma'an Governorate, in the first semester of the academic year 2020/2021.Male and female students, who studied using the technology-oriented Differentiated Instruction approach, and the other was a control, consisting of (28) male and female students, who also studied using the regular method.The results of the study showed the effectiveness of technology-oriented differentiated instruction in motivating students to learn science with a significant difference compared to traditional instruction.
Triplet Loss-based Convolutional Neural Network for Static Sign Language Recognition

Sign language (SL) is a non-verbal visual language used as a primary communication tool by deaf or hearing-impaired community. Owing to availability of large number of SLs with wide varieties, a great effort is required for public majority to master in interpreting them which is not feasible. Despite the recent advances in developing automatic sign language recognition (SLR) systems, their performance undergoes tremendous degradation when low resolution images with large intra-class and slight inter-class variations are employed. To deal with these issues, a novel end-to-end Convolutional Neural Network (CNN) is proposed to extract the features from the low resolution input images. This feature extractor is trained based on the semi-hard triplet loss function so that the images belonging to the same class are placed close to one another in a lower dimensional embedding space while the distance between the samples from separate classes is maximized. In addition to the efficient loss function, proper selection of the filter and kernel sizes, activation functions, and regularization methods in the proposed CNN leads to effective feature vectors from the small-sized images while the number of the parameters is reduced. The embedded features with a fixed small vector length are utilized to train a Support Vector Machine (SVM) classifier for final recognition. Experimental results on two datasets from two SLs of American (MNIST) and Arabic (ArSL2018) with an accuracy of 100% and 97.54%, respectively, demonstrate that the proposed model outperforms the existing approaches without any need for increasing the quantity of the dataset with augmentation which proves its feasibility.
Artificial intelligence based marketing: a bibliometric analysis

The growth in the online trend in business has brought a big revolutionary wave of artificial intelligence in the field of marketing to not only gain a competitive advantage, but also to predict and convert the potential consumers into the ultimate consumers. This paper presents a detailed analysis on the publications of the Scopus database on the previous work done in the area of 'artificial intelligence marketing' between the year 2000-2021 (accepted/published till January 2021) and then a bibliometric analysis using Vos Viewer was conducted on 110 papers to review the research trends in artificial intelligence-based marketing with special focus on India. The purpose of this study is to review the research work done on the growth of research conducted on artificial intelligence marketing in India and to analyse the future line of research in this area.
Channel Estimation for Massive MIMO: An Information Geometry Approach

In this paper, we investigate the channel estimation for massive multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Using the sampled steering vectors in the space and frequency domain, we first establish a space-frequency (SF) beam based statistical channel model. The accuracy of the channel model can be guaranteed with sufficient sampling steering vectors. With the channel model, the channel estimation is formulated as obtaining the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$a \ posteriori$</tex-math></inline-formula> information of the beam domain channel. We solve this problem by calculating an approximation of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$a\ posteriori$</tex-math></inline-formula> distribution's marginals within the information geometry framework. Specifically, by viewing the set of Gaussian distributions and the set of the marginals as a manifold and its <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$e$</tex-math></inline-formula> -flat submanifold, we turn the calculation of the marginals into an iterative projection process between submanifolds with different constraints. We derive the information geometry approach (IGA) for channel estimation by calculating the solutions of projections. We prove that the mean of the approximate marginals at the equilibrium of IGA is equal to that of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$a \ posteriori$</tex-math></inline-formula> distribution. Simulations demonstrate that the proposed IGA can accurately estimate the beam domain channel within limited iterations.
Id-Free Person Similarity Learning

Learning a unified person detection and re-identification model is a key component of modern trackers. However, training such models usually relies on the availability of training images / videos that are manually labeled with both person boxes and their identities. In this work, we explore training such a model by only using person box annotations, thus removing the necessity of manually labeling a training dataset with additional person identity annotation as these are expensive to collect. To this end, we present a contrastive learning framework to learn person similarity without using manually labeled identity annotations. First, we apply image-level augmentation to images on public person detection datasets, based on which we learn a strong model for general person detection as well as for short-term person re-identification. To learn a model capable of longerterm re-identification, we leverage the natural appearance evolution of each person in videos to serve as instance-level appearance augmentation in our contrastive loss formulation. Without access to the target dataset or person identity annotation, our model achieves competitive results compared to existing fully-supervised state-of-the-art methods on both person search and person tracking tasks. Our model also shows promising results for saving the annotation cost that is needed to achieve a certain level of performance on the person search task.
Revisiting Personal Maturity Development

The article considers the phenomenon of personal maturity in its dynamic aspect. Conceptualization of personal maturity, its criteria are not well structured so far, which makes it difficult to conduct research on the subject matter, to establish a holistic picture of personal maturity development at all ontogenesis stages. The author states that personal maturity is not an age parameter; it can develop throughout the life span of a person. The author analyzed the concepts “development”, “transformation”, “advancement”. Taking into account internal and external factors in relation to an individual, the rate and quality of its development, processes of formation development, and consequently their outcomes relating to a certain period will be exclusively individual. The author emphasizes the necessity to search for mechanisms of actualization of personal maturity and constructing the model for their empirical study.
FastHebb: Scaling Hebbian Training of Deep Neural Networks to ImageNet Level

Learning algorithms for Deep Neural Networks are typically based on supervised end-to-end Stochastic Gradient Descent (SGD) training with error backpropagation (backprop). Backprop algorithms require a large number of labelled training samples to achieve high performance. However, in many realistic applications, even if there is plenty of image samples, very few of them are labelled, and semi-supervised sample-efficient training strategies have to be used. Hebbian learning represents a possible approach towards sample efficient training; however, in current solutions, it does not scale well to large datasets. In this paper, we present FastHebb, an efficient and scalable solution for Hebbian learning which achieves higher efficiency by 1) merging together update computation and aggregation over a batch of inputs, and 2) leveraging efficient matrix multiplication algorithms on GPU. We validate our approach on different computer vision benchmarks, in a semi-supervised learning scenario. FastHebb outperforms previous solutions by up to 50 times in terms of training speed, and notably, for the first time, we are able to bring Hebbian algorithms to ImageNet scale.
Evaluation of Pre-Trained CNN Models for Geographic Fake Image Detection

Thanks to the remarkable advances in generative adversarial networks (GANs), it is becoming increasingly easy to generate/manipulate images. The existing works have mainly focused on deepfake in face images and videos. However, we are currently witnessing the emergence of fake satellite images, which can be misleading or even threatening to national security. Consequently, there is an urgent need to develop detection methods capable of distinguishing between real and fake satellite images. To advance the field, in this paper, we explore the suitability of several convolutional neural network (CNN) architectures for fake satellite image detection. Specifically, we benchmark four CNN models by conducting extensive experiments to evaluate their performance and robustness against various image distortions. This work allows the establishment of new baselines and may be useful for the development of CNN-based methods for fake satellite image detection.
“Writing is challenging”: factors contributing to undergraduate students’ difficulties in writing English essays

Essay writing is one of writing skills that must be mastered by students especially college ones because they often get an assignment to write an essay. However, many students find it difficult when they are assigned to compose an essay. This research aims to investigate the difficulties in English essay writing and its factors. Framed in a narrative inquiry, 33 students and one lecturer volunteered to participate. Data were collected through open-ended questionnaires and semi-structured interviews. The data were analyzed using thematic analysis by Braun and Clarke. Study findings reveal that students find difficulties in grammar, vocabulary, cohesion and coherence, and developing topic ideas. Factors that contribute to students' difficulties are both internal factors (psychological factors and linguistic factors) and external factors (peer support and advisor support). Several ways that students do to overcome the difficulties in writing essays were motivating themselves, studying and reading a lot, and writing practice. In addition, they were also asked their friends and their lecturer to get more explanation. To make a good essay, students need to improve their grammar and punctuation ability. They, likewise, need to increase their logical thinking ability to create coherent and cohesive essays.