Reviewerly/exHarmony huggingface dataset:
Feature Selections for the Classification of Webpages to Detect Phishing Attacks: A Survey

In recent years, due to the increased number of Internet-connected devices, almost all the real-world interactions are transferred to the cyberworld. Therefore, most of the commerce (especially in the e-commerce format) are executed over webpages. The anonymous and uncontrollable structure of Internet, enables the malicious use of this cyber environment for a relatively new crime format, named as e-crime, which mainly aims some illegal financial gain by cheating the standard end-users. Phishing attacks are one of the most preferred fraudulent technique which is used for getting some confidential information (like user-id, password, credit card information, etc.) of the end-users. Therefore, security admins of the networks try to decrease the number of victims is their companies. One principal protection mechanism is the use of blacklists to detect the phishing webpages. However, it has a significant deficiency in not protection about new page attacks. Most of the security admins use some learning systems which are trained by a pre-collected a-dataset by extracting some features from the URL and content of the web pages. The performance of the used system directly related with the features used for the classification. In this work, we aimed to analyze the previously used features in the classification of the web pages by making a comparative analysis about the literature. With this study, it is aimed to produce a general survey resource for the researchers, which aim to work on the classification of webpages or the security of the networks.
Emotion Recognition for Vietnamese Social Media Text

Emotion recognition or emotion prediction is a higher approach or a special case of sentiment analysis. In this task, the result is not produced in terms of either polarity: positive or negative or in the form of rating (from 1 to 5) but of a more detailed level of analysis in which the results are depicted in more expressions like sadness, enjoyment, anger, disgust, fear and surprise. Emotion recognition plays a critical role in measuring brand value of a product by recognizing specific emotions of customers’ comments. In this study, we have achieved two targets. First and foremost, we built a standard Vietnamese Social Media Emotion Corpus (UIT-VSMEC) with exactly 6,927 emotion-annotated sentences, contributing to emotion recognition research in Vietnamese which is a low-resource language in natural language processing (NLP). Secondly, we assessed and measured machine learning and deep neural network models on our UIT-VSMEC corpus. As a result, the CNN model achieved the highest performance with the weighted F1-score of 59.74%. Our corpus is available at our research website ( https://sites.google.com/uit.edu.vn/uit-nlp/corpora-projects ).
An accuracy improving method for composite grating phase measuring profilometry

An accuracy improving method for composite grating phase measuring profilometry (CGPMP) based on mixed filtering window is proposed. When CGPMP is used to carry out three-dimensional (3D) measurement, the phase-shifting deformed patterns can be demodulated effectively from the captured composite deformed pattern by proper filters. So the selection of the filtering window is very important. By analyzing the characteristics of noises and their different effects on the spectrum components along different orientations, searching for the best filtering window of the spatial spectrum along the two mutually orthogonal directions, a mixed filtering window to improve measuring accuracy is established. Digital simulating results show that the designedly mixed filtering window has better noise suppression than rectangular, triangular, Blackman and Hanning filtering windows. The experimental results show that the measuring error using the mixed filtering window equalizing noise and spectrum leakage is the smallest, and the root mean square error (RMSE) is reduced by 9.64%, and the measuring accuracy is improved effectively.
A deep learning approach to ship detection using satellite imagery

Abstract Automatic ship detection on remote sensing images is one of the important modules in the maritime surveillance system. Its main task is to detect possible pirate threats as early as possible. Thus, the detection system must be accurate enough as it plays a vital role in national security. Therefore, this paper proposes a deep learning approach to detect the presence of a ship in the harbour areas. DenseNet architecture has been selected as the core convolutional neural network-based classifier, where various finetuning has been done to find the optimal setup. The three hyperparameters that have been fine-tuned are optimizer selection, batch size, and learning rate. The experimental results show a success rate of over 99.75% when Adam optimizer is selected with a learning rate of 0.0001. The test was done on the Kaggle Ships dataset with 4,200 images. This algorithm can be further fine-tuned by considering other types of convolutional neural network architecture to increase detection accuracy.
Hybrid Henry Gas Solubility Optimization Algorithm Based on the Harris Hawk Optimization

Henry Gas Solubility Optimization Algorithm (HGSO) is a physical-based algorithm based on Henry's Law and simulates the process that the solubility of gas in liquid changes with temperature.The search strategy of the HGSO is very simple, which results in the algorithm having poor exploitation ability and unable to find a more accurate optimal solution.The Harris Hawk optimization algorithm (HHO) has strong exploitation because of its diverse exploitation strategies.In order to make up for the shortcomings of HGSO algorithm, this article proposes an improved Henry gas solubility optimization algorithm (HHO-HGSO) based on the Harris Hawk optimization.During the iteration, taking the escaping energy function of the HHO algorithm as an indicator and determining a threshold, when the escaping energy function value is greater than the threshold, the algorithm conducts the search strategy of the HGSO algorithm, when less than the threshold, executes four exploitation strategies of Harris Hawk.In order to verify the performance of the proposed algorithm, HHO-HGSO is used to optimize CEC2005 and CEC2017 benchmark test functions and solve 4 real engineering design problems.Marine predators algorithm (MPA), Whale optimization algorithm (WOA), Lightning search algorithm (LSA), Water cycle algorithm (WCA), HGSO, and HHO were used to conduct comparative experiments.The simulation results show that the proposed HHO-HGSO algorithm has strong optimization ability.The core code of the paper has been uploaded in https://github.com/xwaiyy123/HHO-HGSO.
Twitter Sentiment Analysis

In this era of growing social media users, Twitter has significantly large number of daily users who post their opinions in the form of tweets. This paper presents an idea of extracting sentiments out of the tweet and an approach towards classifying a tweet into positive, negative or neutral. This approach can be in many ways useful to any organization, who gets mentioned or tagged in a tweet. Generally the tweets being unstructured in format, first of all the tweet needs to be converted into the structured format. In this paper, tweets are resolved using pre-processing phase and access of tweets has been accomplished via libraries using Twitter API. The datasets need to be trained using algorithms in a way, such that, it becomes capable of testing the tweets and it releases the required sentiments out of the feeded tweets.
Seeing the unseen: user experience and technology acceptance in Augmented Reality science literacy

Augmented Reality (AR) appears to have great potential in education and training. Although this technology has become mainstream through the use of handheld mobile devices, consumer-grade AR glasses are beginning to become available. These glasses could unlock the potential of AR by increasing freedom of movement, performance and safety. This preliminary study explores user experience and acceptance of state-of-the-art AR glasses. A simple activity was created to visualize electromagnetic radiation for science literacy purposes and then evaluated by university students (N=154). The results showed moderate spatial presence, low simulator sickness and high levels of acceptance and satisfaction.
Fast, Small, and Area-Time Efficient Architectures for Key-Exchange on Curve25519

This paper demonstrates fast and compact implementations of Elliptic Curve Cryptography (ECC) for efficient key agreement over Curve25519. Curve25519 has been recently adopted as a key exchange method for several applications and included in the National Institute of Standards and Technology (NIST) recommendations for public key cryptography. This paper presents three different performance level designs including lightweight, area-time efficient, and high-performance architectures. Lightweight hardware implementations are used for several Internet of Things (IoT) applications due to their resources being at premium. Our lightweight architecture utilizes 90% less resources compared to the best previous work while it is still more optimized in term of A middot; T (area×time). For efficient implementation from either time or utilized resources, our area-time efficient architecture can establish almost 7,000 key sessions per second which is 64% faster than the previous works. The area-time efficient architecture uses well scheduled interleaved multiplication combined with a reduction algorithm. Additionally, we offer a fast architecture for high performance applications based on the 4-level Karatsuba method and Carry-Compact Addition (CCA). Our high-performance architecture also outperforms previous work in terms of A middot; T. The results show 9% and 29% improvement in A middot; T and A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sub> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">middot;</sub> T (DSP_count×time), respectively. All architectures are variable-base-point implemented on the Xilinx Zynq-7020 FPGA family where performance and implementation metrics are reported and compared. Finally, various side-channel attack countermeasures are embedded in the proposed architectures.
A New Feature Selection Scheme for Emotion Recognition from Text

This paper presents a new scheme for term selection in the field of emotion recognition from text. The proposed framework is based on utilizing moderately frequent terms during term selection. More specifically, all terms are evaluated by considering their relevance scores, based on the idea that moderately frequent terms may carry valuable information for discrimination as well. The proposed feature selection scheme performs better than conventional filter-based feature selection measures Chi-Square and Gini-Text in numerous cases. The bag-of-words approach is used to construct the vectors for document representation where each selected term is assigned the weight 1 if it exists or assigned the weight 0 if it does not exist in the document. The proposed scheme includes the terms that are not selected by Chi-Square and Gini-Text. Experiments conducted on a benchmark dataset show that moderately frequent terms boost the representation power of the term subsets as noticeable improvements are observed in terms of Accuracies.
LaScaDa: A Novel Scalable Topology for Data Center Network

The growth of cloud-based services is mainly supported by the core networking infrastructures of largescale data centers, while the scalability of these services is influenced by the performance and dependability characteristics of data centers.Hence, the data center network must be agile and reconfigurable in order to respond quickly to the everchanging application demands and service requirements.The network must also be able to interconnect the big number of nodes, and provide an efficient and fault-tolerant routing service to upper-layer applications.In response to these challenges, the research community began exploring novel interconnect topologies, namely: Flecube, DCell, Ficonn, HyperFlaNet and BCube.However, these topologies either scale too fast (grows exponentially in size), or too slow, and therefore suffer from performance bottlenecks.In this paper, we propose a novel data center topology called LaScaDa (Layered Scalable Data Center) as a new solution for building scalable and cost-effective data center networking infrastructures.The proposed topology organizes nodes in clusters of similar structure, then interconnect these clusters in a well-crafted pattern and system of coordinates for nodes to reduce the number of redundant connections between clusters, while maximizing connectivity.LaScaDa forwards packets between nodes using a new hierarchical rowbased routing algorithm.The algorithm constructs the route to the source based on the modular difference between the source and destination coordinates.Furthermore, the proposed topology interconnects a large number of nodes using a small node degree.This strategy increases the number of directly connected clusters and avoids redundant connections.As a result, we get a good quality of nodes in terms of average path length (APL), bisection bandwidth, and aggregated bottleneck throughput.Experimental results show that LaScaDa has better performance than DCell, BCube, and HyperBcube in terms of scalability, while providing a good quality of service.
Analog Compression and Communication for Federated Learning over Wireless MAC

In this paper, we consider federated learning in wireless edge networks. Transmitting stochastic gradients (SG) or deep model's parameters over a limited-bandwidth wireless channel can incur large training latency and excessive power consumption. Hence, data compressing is often used to reduce the communication overhead. However, efficient communication requires the compression algorithm to satisfy the constraints imposed by the communication medium and take advantage of its characteristics, such as over-the-air computations inherent in wireless multiple-access channels (MAC), unreliable transmission and idle nodes in the edge network, limited transmission power, and preserving the privacy of data. To achieve these goals, we propose a novel framework based on Random Linear Coding (RLC) and develop efficient power management and channel usage techniques to manage the trade-offs between power consumption, communication bit-rate and convergence rate of federated learning over wireless MAC. We show that the proposed encoding/decoding results in an unbiased compression of SG, hence guaranteeing the convergence of the training algorithm without requiring error-feedback. Finally, through simulations, we show the superior performance of the proposed method over other existing techniques.
Multiscale Omnibearing Attention Networks for Person Re-Identification

The past few years in the fields of Person Re-Identification (RE-ID) have seen attention mechanism receives enormous interest as it has superior performance in obtaining discriminative feature representations. However, a wide range of state-of-the-art RE-ID attention models only focus on one-dimensional attention design method, e.g. spatial attention and channels attention, hence the produced attention maps are neither detailed enough nor discriminative enough to capture complicated interactions of visual parts. Developing multi-scale attention mechanism for RE-ID, an under-studied approach, becomes a practicable method to overcome this deficiency. Toward this goal, we propose a Multiscale Omnibearing Attention Networks (MOAN) for RE-ID which is capable of utilizing the complex fusion information acquired from the multiscale attention mechanism with features being more representative. Specifically, MOAN takes full advantage of multi-sized convolution filters to obtain discriminative holistic and local feature maps, and adaptively conducts feature information augmentation by introducing an Omnibearing Attention (OA) module. Through the OA module, spatial attention and channel attention are integrated together in a unique way where they work in a complementary way. To sum up, MOAN not only inherits the merit of two kinds of attention mechanism but also performs well in extracting comprehensive feature information. Furthermore, taking into account the robustness of model performance, we formulate a Random Drop (RD) Function to facilitate training MOAN and further increase the diversity of training model for adaptation. Furthermore, to achieve end-to-end training, we utilize trainable parameters to take place of initial fixed parameters, and the model performance is experimentally promoted. Extensive experiments have been carried out on the four mainstream RE-ID datasets. As the result shows, our method with re-ranking achieves rank-1 accuracy of 92.29% on CUHK03-NP, 97.45% on Market-1501, 93.81% on DukeMTMC-reID and 81.53% on MSMT17-V2, outperforming the state-of-the-art methods and confirming the effectiveness of our method.
Detecting hate speech against politicians in Arabic community on social media

Purpose This paper aims to propose an approach for hate speech detection against politicians in Arabic community on social media (e.g. Youtube). In the literature, similar works have been presented for other languages such as English. However, to the best of the authors’ knowledge, not much work has been conducted in the Arabic language. Design/methodology/approach This approach uses both classical algorithms of classification and deep learning algorithms. For the classical algorithms, the authors use Gaussian NB (GNB), Logistic Regression (LR), Random Forest (RF), SGD Classifier (SGD) and Linear SVC (LSVC). For the deep learning classification, four different algorithms (convolutional neural network (CNN), multilayer perceptron (MLP), long- or short-term memory (LSTM) and bi-directional long- or short-term memory (Bi-LSTM) are applied. For extracting features, the authors use both Word2vec and FastText with their two implementations, namely, Skip Gram (SG) and Continuous Bag of Word (CBOW). Findings Simulation results demonstrate the best performance of LSVC, BiLSTM and MLP achieving an accuracy up to 91%, when it is associated to SG model. The results are also shown that the classification that has been done on balanced corpus are more accurate than those done on unbalanced corpus. Originality/value The principal originality of this paper is to construct a new hate speech corpus (Arabic_fr_en) which was annotated by three different annotators. This corpus contains the three languages used by Arabic people being Arabic, French and English. For Arabic, the corpus contains both script Arabic and Arabizi (i.e. Arabic words written with Latin letters). Another originality is to rely on both shallow and deep leaning classification by using different model for extraction features such as Word2vec and FastText with their two implementation SG and CBOW.
Feedback Analysis in Outcome Base Education Using Machine Learning

In outcome base education, we need to utilize students' feedback as one of the key inputs to determine the effectiveness of teaching and learning activities of a university. Analyzing only rating scores does not reveal the true sentiment of students. Textual comments are more reflected true sentiment of the students. This paper is to implement a feedback analysis system based on rating score and textual comments. We used two types of dataset which include rating scores and textual comments, respectively. K-means clustering algorithm is used to cluster rating scores. Classification models are built with various classification algorithms using labeled dataset that got from clustering step. For textual comment analysis, we used Naïve Bayes classifier to train a model and classify test dataset into negative and positive sentiments using 10-fold cross validation. We also tested the model to classify the unlabeled textual comments. According to experimental results, we found that Support Vector Machine gives the best precision result for rating score classification. For textual comment analysis, Naïve Bayes classifier gives optimum result.
A Survey on Cloud Computing Simulation and Modeling

Cloud Computing (CC) has attracted a massive amount of research and investment in the previous decade. The economical model proposed by this technology is a viable solution for consumers as well as being a profitable one for the provider. However, deploying real world cloud experiments to test new policies/algorithms is time consuming and very expensive, especially for large scenarios. As a result, the research community has opted to test their contributions is CC simulators. Although the models proposed by these simulators are not exhaustive, each one is made to address a specific process. Alternatively, others tools are made to provide a platform and the necessary building blocks to model any desired sub-component (application/network model, energy consumption, scheduling and Virtual Machine provisioning). In this paper, a detailed survey about the existing CC simulators is made discussing features, software architecture as well as the ingenuity behind these frameworks.
Pan-Cancer Classification Based on Self-Normalizing Neural Networks and Feature Selection

Cancer is a one of the severest diseases and cancer classification plays an important role in cancer diagnosis and treatment. Some different cancers even have similar molecular features such as DNA copy number variant. Pan-cancer classification is still non-trivial at molecular level. Herein, we propose a computational method to classify cancer types by using the self-normalizing neural network (SNN) for analyzing pan-cancer copy number variation data. Since the dimension of the copy number variation features is high, the Monte Carlo feature selection method was used to rank these features. Then a classifier was built by SNN and feature selection method to select features. Three thousand six hundred ninety-four features were chosen for the prediction model, which yields the accuracy value is 0.798 and macro F1 is 0.789. We compared our model to random forest method. Results show the accuracy and macro F1 obtained by our classifier are higher than those obtained by random forest classifier, indicating the good predictive power of our method in distinguishing four different cancer types. This method is also extendable to pan-cancer classification for other molecular features.
Resource Management and Fairness for Federated Learning over Wireless Edge Networks

Federated Learning has the potential to break the barrier of AI adoption at the edge through better data privacy and reduced client to server communication cost. However, the heterogeneity among the clients' compute capabilities, communication rates, the amount and quality of data can affect the training performance in terms of overall accuracy, model fairness and convergence time. We develop compute-communication and data importance aware resource management schemes to optimize the above metrics and evaluate the training performance on benchmark datasets. We observe that the proposed algorithms strikes a balance between model performance and total training time by achieving 4x - 10x reduction in convergence time without loss of test performance. Further, our algorithms also show superior fairness performance measured by variance and worst case 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> percentile accuracy/loss on benchmark datasets.