Reviewerly/exHarmony huggingface dataset:
Plagiarism Detection in Arabic Documents using word2vector and Arabic WordNet

Plagiarism detection has become a latest research area in Natural Language Processing Field. In today's with the huge available content of Arabic articles on the internet this make the text plagiarism is so easy and Spread widely in academic society, so to decrease and prevent this Harmful habit many algorithms has been developed to detect plagiarized texts in many famous languages and in Arabic language, in this article we have developed an algorithm to detect plagiarism in Arabic text, in this algorithm we have used word2vector technology which transforms texts to numeric vectors and it keep the syntax and the meaning of sentences, and we have used Arabic WordNet to overcome of the limitation of word2vector models, we have used Twitter Continuous Bag-of-words word2vector model which built from huge repository of Arabic tweets in different topics, and using of Arabic WordNet is to finds the numeric vectors of non- existing words in word2vector model so in this case we try to find numeric vectors for one synonym word of the non-existing words in the model
Unsupervised Meta Learning with Neural Style Transfer

Deep neural networks show great performance for image classification tasks when we have a large dataset to train on, but it does not perform well for the few-shot learning tasks. So, to eradicate this issue, we can use meta-learning, wherein we train our model to learn generalizable features over a multitude of tasks such that it can learn a new task using only a few training data samples. However, training a supervised meta-learning algorithm it requires a large repository of meta-training tasks with samples that are labelled. This makes the regime dependent on labelled samples which are not always largely available, and it fails to utilize large amounts of unlabelled data due to its inability to learn useful features from the unlabelled examples. In this paper, we propose an unsupervised meta-learning algorithm which is an improvement on the approach of UMTRA. Also, our approach is model agnostic as long as it is trained with gradient descent. To construct meta-training tasks for the meta-learning phase, the algorithm employs random sampling and augmentation while the validation set is constructed through data augmentation of training set using neural style transfer. We evaluated our algorithm on Omniglot & Mini-Imagenet dataset. Our approach outperforms various unsupervised meta-learning algorithms like CACTUs, UMTRA and LASIUM on the Omniglot dataset and gives comparable results on the Mini-Imagenet dataset.
Information Technology Courses and their Adequacy for Graduates of Digital Media Programs in Arab Universities

This paper focuses on studying the structure of the study plans for digital media programs and specializations in Arab universities and their learning outcomes, as well as the contents of IT courses and their adequacy for the graduate It poses its problematic question in two main points: Do the plans of digital media programs in Arab universities contain a sufficient number of IT and multimedia courses? And do these courses contain the skills required for the graduate in the use of IT and multimedia in his specialization? The study adopted a quantitative and qualitative content analysis approach with two categories of analysis the shape category; It focused on studying the structure of study plans for digital media programs and specializations, the ratio of IT courses to the rest of the courses, and the content category; It focused on analyzing the structure of the learning outcomes of the programs, the contents of IT courses, and their adequacy for the graduate of digital media programs. The study concluded: The study plans of digital media programs in Arab universities incorporated a large number of information technology and multimedia courses in their study plans. These focused on teaching students computer skills, multimedia, graphic design, and software skills for designing, directing and publishing media materials. A review of the literature pertaining to course descriptions, learning outcomes, course content, and weekly topics show that the courses have a strong focus on training students in the digital literacy skills necessary for the labor market in the digital environment, such as word processing, file management software, privacy settings in phones, creating text files and professional presentations, internet search, desktop publishing and digital graphic design, Digital photo editing and production, processing of audio and video, multimedia applications in the field of advertising and media marketing.
Demystifying the TensorFlow Eager Execution of Deep Learning Inference on a CPU-GPU Tandem

Machine Learning (ML) frameworks are tools that facilitate the development and deployment of ML models.These tools are major catalysts of the recent explosion in ML models and hardware accelerators thanks to their high programming abstraction.However, such an abstraction also obfuscates the runtime execution of the model and complicates the understanding and identification of performance bottlenecks.In this paper, we demystify how a modern ML framework manages code execution from a high-level programming language.We focus our work on the TensorFlow eager execution, which remains obscure to many users despite being the simplest mode of execution in TensorFlow.We describe in detail the process followed by the runtime to run code on a CPU-GPU tandem.We propose new metrics to analyze the framework's runtime performance overhead.We use our metrics to conduct in-depth analysis of the inference process of two Convolutional Neural Networks (CNNs) (LeNet-5 and ResNet-50) and a transformer (BERT) for different batch sizes.Our results show that GPU kernels execution need to be long enough to exploit thread parallelism, and effectively hide the runtime overhead of the ML framework.
Chameleon Swarm Algorithm Enhanced by Differential Evolution

The chameleon swarm algorithm (CSA) is a novel meta-heuristic algorithm for solving unconstrained optimization problems. CSA simulates the dynamic behavior of chameleon foraging method to design optimization mechanism, which performs excellent in function optimization. Differential evolution (DE) is an effective and classical stochastic direct search method based on evolutionary strategies, its operators have good convergence. In this paper, the excellent exploration ability of CSA and outstanding convergence speed of DE are taken into consideration. We ingeniously incorporate the differential evolution operator into CSA, and propose hybrid CSADE to solve 30 benchmark optimization functions. To testify the effectiveness of proposed CSADE, we utilize 29 benchmark functions on CEC 2017 to conduct experiments. Through the analysis of statistical results for many experiments, CSADE outperforms selected state-of-the-art algorithms.
FP-SLIC: A Fully-Pipelined FPGA Implementation of Superpixel Image Segmentation

A superpixel segment is a group of pixels that carry similar information. The Simple Linear Iterative Clustering (SLIC) is a well-known algorithm for generating superpixels that offers a good balance between accuracy and efficiency. Nevertheless, due to its high computational requirements, the algorithm does not meet the demands of real-time embedded applications in terms of speed and resources. This paper proposes a fully-pipelined FPGA architecture based on SLIC, dubbed FP-SLIC, that exhibits 1) a simplified and efficient algorithm of reduced computational complexity that facilitates algorithm development for FPGAs, 2) a fully pipelined FPGA design operating at 40MHz with a throughput of one pixel per cycle, and 3) a memory-efficient architecture that eliminates the requirement for external memory. FP-SLIC shows promising BSDS500 benchmark results, especially considering boundary recall for less than 1000 superpixels, where it performs better than related works, while, at the same time, accomplishing a throughput of 259 frames per second (fps).
Educational Tool For ABET Accreditation

One of the essential success factors of any modern education system is to self-assess the teaching process and continuously improve the quality. The new educational system adopts sophisticated assessment processes, designing and assessing the teaching curriculum and the different academic programs based on the learning outcome rather than the teaching input. This helps the educational management spot weaknesses, direct the teaching process toward the goals, and continuously improve the learning process. In addition, the schools use the documents produced by this process as evidence that the academic programs regularly meet specific criteria required by international accreditation institutions such as ABET. However, this process places an extra load on the instructors and the educational management for managing an effective assessment process. This paper proposes the Educational Course and Program Assessment System (ECPAS), a software system with novel features that simplifies managing the educational course and programs assessment process for both the instructor and the academic management. The proposed system will allow the educational administration to assess their courses and programs and provide a novel student level of assessment to improve the overall quality.
Evaluation of Early-exit Strategies in Low-cost FPGA-based Binarized Neural Networks

In this paper, we investigate the application of early-exit strategies to quantized neural networks with binarized weights, mapped to low-cost FPGA SoC devices. The increasing complexity of network models means that hardware reuse and heterogeneous execution are needed and this opens the opportunity to evaluate the prediction confidence level early on. We apply the early-exit strategy to a network model suitable for ImageNet classification that combines weights with floating-point and binary arithmetic precision. The experiments show an improvement in inferred speed of around 20% using an early-exit network, compared with using a single primary neural network, with a negligible accuracy drop of 1.56%.
IoT Enabled Security Management Approach for Intelligent Transportation System in Smart Cities

Using encryption and machine learning to improve security is being considered by researchers. A novel technique for securing an IoT-enabled intelligent transportation system has been proposed. The transaction has been classified using a machine learning model. A machine learning model was trained using a dataset of filtered and unfiltered transaction data. During simulation, the intelligent transportation system classified routine transactions and attacks. There are two types of data transit discussed here: regular and attack and failure. Accuracy, f1-score, recall, and precision have been shown to have significant impact on accuracy of results. Cryptography and machine learning might be used to improve the security of an intelligent transportation system.
Lightweight Tracking of Satellite Video Object Based on Saliency Enhancement Mechanism

Target tracking based on satellite platforms in remote sensing images plays a critical role in military and civilian fields. However, most of the traditional algorithms are still aimed at natural scenes and are difficult to be directly applied to complex satellite images with large fields of view and weak contrast. Therefore, the method of tracking satellite videos based on a multidimensional enhancement mechanism is proposed. For the problem that the complex background in satellite images, which makes the target difficult to be correctly captured and identified, the triplet attention module is introduced to enhance the significance of the target in an efficient way, thereby improving the performance of the tracking network; because of the large computational complexity of a deep convolution network, the network structure with the ghost feature is adopted, and some traditional convolution operations are replaced by simple linear operations, which improves the speed of the network. Finally, with the support of satellite remote sensing datasets, the effectiveness of this method is verified through qualitative and quantitative experiments.
Virtual Board: A Digital Writing Platform for Effective Teaching-Learning

With the advent of a global pandemic, the world changed drastically with a wave of new technologies. The teaching learning process also saw an overwhelming change, where both teachers and students communicated via online. To facilitate such interactions, several tools were developed. Many online video conferencing platforms were vividly used starting from Google Meet to Microsoft Teams, etc. A new approach demands usage of new techniques, software, hardware and often platforms. The specific task of writing in a teaching learning scenario is of eminent importance and it often requires using hardware extensions such as digital writing pads, stylus, etc. Such external hardware devices can be categorised based on how easy and effective they are for communication. Adopting the basic feature of air tapping in head-mounted displays such as Microsoft Hololens for augmented reality applications, here we devised a platform which enables a person to write on a virtual board. To be specific, we proposed a system called virtual board, that enables a person to write “in-the-air” using a pointing device and which automatically translates into text. This enhances the task of writing in a teaching learning scenario. To justify its usability, we performed a subjective test. Participants were asked to perform a similar task both on our proposed virtual board and also on a commonly used digital writing pad. A post-session questionnaire was provided to the participants based on the experiment. Results obtained after analysing the feedback in form of responses clearly showcase that the developed system is truly accepted in terms of the ease of learning and writing, and its effectiveness in a virtual teaching learning environment.
Effective Masked Language Modeling for Temporal Commonsense Reasoning

We propose an enhanced language model for temporal commonsense inference. Through a specially designed masked language modeling task, we equip our model with temporal commonsense knowledge. Specifically, we propose different masking strategies for temporal commonsense reasoning. We evaluate our model on a challenging temporal commonsense reasoning dataset. Our experiments show that the method that takes into account the structure of the target dataset, in addition to time-related features, improved the accuracy by approximately 4.8% compared to the standard fine-tuning approach. This confirms that tailoring a pre-trained language model to the domain of a target task is effective.
Explanation Generated for Sequential Recommendation based on Transformer model

Generating recommendation reasons for recommended items can play an essential role in personalization such as by summarizing users’ comments on their purchased items. However, existing methods only utilize general recommendations, ignoring the fact that items purchased by users are often related to their purchase history. To address this issue, we propose a multitask model referred to as Explanation Generated for Sequential Recommendation (EG4SRec), which is designed to generate recommendation reasons based on a Transformer model for sequential recommendations. First, we predicted and recommended items based on the time series information from the user’s purchase history. Additionally, we used the proposed method to generate recommendation reasons for a target user based on these features by assigning linguistic meaning to the user’s purchase history and the items they may be interested in buy. Moreover, we applied context prediction to generate features for recommendation reasons. The results of experiments conducted using the constructed review dataset, which includes approximately 1. 29M explanations from the Yelp dataset, show that the proposed approach is reasonably effective for sequential recommendations. The model achieved performance similar to that of an existing SOTA model in terms of the evaluation matrix and performed even better in some other terms.
The relationship between Gaussian process based c-regression models and kernel c-regression models

Kernel regression and Gaussian process regression are known methods for representing non-linear regression models. The c-regression models is a method for obtaining the cluster partition and regression model simultaneously. The kernel c-regression models is a typical method of extending the c-regression models to the non-linear. This paper proposes a c-regression models based on Gaussian process regression as an approach to non-linearisation that differs from kernel c-regression models. Next, the relationship between the proposed method and the kernel c-regression models is presented. It is then shown experimentally that the proposed method and kernel c-regression models yield the same results under the same parameters and initial values.
Multidimensional Data Clustering and Visualization Using Hybrid Artificial Neural Network

All data with higher dimensions than 3D require some form of transformation to reveal its potential hidden patterns to assist human perception in making better data-related decisions. The exponential growth of multidimensional unlabeled data generated each day makes unassisted human perception harder to harness the full potential of the collected data that would provide insights. In order to bridge the gap between human visual perception and the multidimensional nature of modern-day data, this paper proposed a unique way of multidimensional data clusters discovery through a hybrid Artificial Neural Network (ANN) algorithms of Self-Organizing Map and modified Adaptive Coordinate (SOM-mAC). A Series of experiments conducted on synthetic 3D Gaussians dataset and benchmarking datasets Iris, Wine, and Wisconsin Breast Cancer (WBC) demonstrated that the proposed SOM-mAC algorithm could identify, cluster, and label the dataset's underlying topologies and densities. The algorithm can also identify and isolate potential outliers associated with each cluster. As clustering can be done in 2D through single-passing of the dataset through trained network, this algorithm has the potential for faster clustering of high dimensional big data.
Extraction and Classification of Employment-Related Information from Web Data

On the web, there is large amount of information pertaining to various job-hunting activities, and automatically collecting this information would be efficient and convenient. In this study, information related to job hunting was extracted from massive web data and was classified. For extracting employment-related information, the performance of the following three methods was compared: a supervised machine learning method called a support vector machine (SVM), a rule-based method, and a baseline method. The rule-based method obtained superior performance with an F-measure of 0.7. To classify employment-related information, the following seven categories were used: “qualification information,” “vocational information,” “job seeker information,” “job offer information,” “no relationship,” “job hunting support information,” and “job change information.” The results of the comparison between the three methods revealed the rule-based method displayed superior performance, with an average F-measure of approximately 0.6. In addition, the rule-based method had an F-measure of approximately 0.8 for the categories “qualification information,” “job offer information,” and “job change information.”
CaW-NAS: Compression Aware Neural Architecture Search

With the ever-growing demand for deep learning (DL) at the edge, building small and efficient DL architectures has become a significant challenge. Optimization techniques such as quantization, pruning or hardware-aware neural architecture search (HW-NAS) have been proposed. In this paper, we present an efficient HW-NAS; Compression-Aware Neural Architecture search (CaW-NAS), that combines the search for the architecture and its quantization policy. While former works search over a fully quantized search space, we define our search space with quantized and non-quantized architectures. Our search strategy finds the best trade-off between accuracy and latency according to the target hardware. Experimental results on a mobile platform show that, our method allows to obtain more efficient networks in terms of accuracy, execution time and energy consumption when compared to the state of the art.
Deep Spatial Feature Transformation for Oriented Aerial Object Detection

Object detection in aerial images has received extensive attention in the field of computer vision. Different from natural images, the aerial objects are usually distributed in any direction. Therefore, the existing detector usually needs more parameters to encode the direction information, resulting in a large number of redundant calculations. In addition, because an ordinary convolution neural network (CNN) does not effectively model the direction change, a large amount of the rotated data is required for the aerial detector. To solve these problems, we propose a deep spatial feature transformation network (DSFT-Net), which includes a spatial feature extraction module and a feature selection module. Specifically, we add the rotation convolution kernel to the detector to extract the directional feature of the rotated target to accurately predict the direction of the model. Then, we build a dual pyramid to separate the features in the classification and regression tasks. Finally, the polarization function is proposed to construct the critical features that are suitable for their respective tasks, achieving feature selection and more refined detection. Experiments on public remote sensing benchmarks (e.g., DOTA, HRSC2016, and UCAS-AOD) have proved the effectiveness of our detector.
Analysis of the level of suitability of the average-based fuzzy time series method for predicting national salt production

Based on data obtained from the Ministry of Maritime Affairs and Fisheries reported by kompas.com, in 2021, Indonesia will import 3.07 million tons of salt. This is because the stock from local salt farmers is insufficient and does not meet the quality for industrial salt standards. Variables that affect the amount of salt production and quality in Indonesia are Nino 3.4, negative Indian Ocean Dipole (IOD). These two variables will affect the dry season that will occur in the form of wet or dry drought. If the dry season is wet, the national salt production will decrease which will result in crop failure. The Ministry of Maritime Affairs and Fisheries (KKP) has predicted national salt using several methods. However, the results of the salt prediction are still not able to approach the number of predictions of national salt accurately. Thus, in this study, the Average-Based Fuzzy Time Series method was used.