Reviewerly/exHarmony huggingface dataset:
Phishing Email Detection Using Bi-GRU-CNN Model

Phishing attacks are the most frequently used method for attackers to obtain sensitive information from victims or infect their networks, as the number of phishing attacks continues to grow rapidly due to their simplicity and low cost of distribution, as well as the appearance of phishing-as-a-service. Thus, phishing email detection is a critical issue that requires immediate attention, where we have focused on resolving the phishing email detection problem using only email bodies. The current study proposed and trained a model using Bi-GRU and two dimensional CNN, in which words are represented using pre-trained GloVe word embeddings. The experimental results show that our model has achieved 98.44% precision, which shows the effectiveness of our model.
Twitter’s Hate Speech Multi-label Classification Using Bidirectional Long Short-term Memory (BiLSTM) Method

Since social media is one of the most likable products of techngetpeople get easier to express their opinions. Anyone be able to tell their opinion freely there. Unfortunately, its convenience has also become a boomerang for us, the easier every opinion conveyed the easier hate speech is expressed. This matter become the dark side of social media. Hate speech face us with a lot of dangers, such as violence, social conflict, even homicide. Therefore, preventing all of those dangers that might be occur because of hate speech is one of the prior things we need to do. This research was done as an attempt to take care of the dangers that could be done by hate speech. The attempt we tried to do is using multi-label text classification to predict hate speech with the Bidirectional Long Short-term Memory (BiLSTM) method. This multi-label text classification labelled every tweet in the dataset crawled from Twitter with 12 labels about hate speech. From this experiment, we obtained the best hyperparameter value that could achieve great performance with 82.31% accuracy, 83.41% precision, 87.28% recall, and 85.30% F1-score.
Ontology of XSS Vulnerabilities and its Detection using XENOTIX Framework

Web technologies are framed for the purpose of catering the need of ubiquitousness. There is no doubt that web applications are providing number of advantages to the masses, but everything comes with certain vulnerabilities. Exploitation of these vulnerabilities can change the game completely by providing fatal results, instead of giving fruitful results. Cross site scripting attack is also a result of mishandling of vulnerabilities located in web applications. In this paper, XENOTIX framework from OWASP has been used for the detection of cross site scripting attack and practices to curb XSS are discussed.
Decentralized Blockchain Technology: Towards a Trusted and Transparent Beneficial Ownership Regime

This chapter offers an insight into emerging blockchain technology and explores how it might support a trusted and transparent beneficial ownership regime. The international anti-money laundering community has advocated for central registers of beneficial owners as a measure to strengthen corporate transparency. A lack of corporate transparency is problematic because it helps to conceal illicit wealth by frustrating authorities’ efforts to identify the real beneficial owner behind corporate vehicles used to launder money and evade taxes. However, there are moral, legal, and practical challenges in implementing central registers of beneficial owners. The chapter considers the application of blockchain in decentralized and distributed digital ledger systems that are without a trusted third party. This chapter concludes with an examination of blockchain’s role in maintaining an accurate, verifiable, and transparent record of beneficial ownership information in support of global AML efforts.
CCTV as an Element of the Quality Management System of the Learning Process in Education Institutions

The article summarizes the acquired experience of using a video surveillance system in managing the quality of the educational process in education institution, considers the directions of using a video surveillance system to ensure the quality of the educational process in an educational institution, gives certain recommendations for the implementation and operation of such systems, describes the system of visual monitoring of the quality of classes based on a video surveillance system, in particular, the user interface of the system, database for accumulating monitoring results, and block for processing monitoring results. The quality of classes in the visual monitoring system is evaluated based on the results of rapid analysis of the class for 17 indicators, which are grouped into three blocks: class organization; assessment of the teacher's activities; students’ work in the classroom. The conditions under which CCTV will be an efficient element of the quality management system of the educational process are determined. The block diagram of the visual monitoring system for the quality of the educational process and the form of the interface of this system are considered, and further expansion of functions of the visual monitoring system using complex, aspect and other types of class analysis is proposed.
An Arabic Egyptian Dialect COVID-19 Twitter Dataset (ArECTD)

Citizens are increasingly expressing their ideas and feelings on social media platforms such as Twitter. During the coronavirus crisis, numerous emotions are exposed, including sadness, anger, fear, sympathy, surprise, etc. The Arabic Egyptian Dialect COVID-19 Twitter Dataset (ArECTD), comprised of 78K tweets, was collected in the period from the 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> of January 2020 till the 30 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> of May 2021 focusing on the Egyptian dialect. It was annotated using a combination of manual and a semi-supervised self-learning technique. The tweets of ArECTD were categorized into 10 emotions (sarcasm, sadness, anger, fear, sympathy, joy, hope, surprise, love, and none). Emotion analysis of this dataset could help decision makers understand and respond to the public reactions during the pandemic.
Artificial Intelligence in Online Stores’ Processes

The global e-commerce market has been growing rapidly, even more so due to the COVID–19 pandemic which caused a number of stationery stores to close down. At the same time, Artificial Intelligence (AI) has also gained prominence. In this article, we review and describe the practical applications of AI which is helping e-commerce businesses. In particular, we have concentrated on AI in some key areas of e-commerces such as online marketing, operations in fulfillment centers, barcode identification, and autonomous delivery methods. The findings and conclusions in this article would be useful for researchers as well as businesses to get an overview of the landscape of AI applications in e-commerces.
CrypTop12: A Dataset For Cryptocurrency Price Movement Prediction From Tweets And Historical Prices

Cryptocurrencies are gaining popularity day by day, and their analysis is a fascinating and demanding research topic. The average daily trading volume of Bitcoin was ${\$}67$ billion in May 2021. A peculiar feature of cryptocurrencies is that they are not generally issued by a central authority, making them insusceptible to any governmental impedance. Cryptocurrency rates are closely related to news and influenced by tweets. However, no available dataset can analyze the crypto market adequately. We present CrypTop12, a benchmark dataset for Cryptocurrency Price Movement Prediction based on tweets and historical prices. We collect over 576K tweets related to the top 12 cryptocurrencies, spanning over 1255 days and refine them to filter the tweets that are most relevant to price fluctuations. We also demonstrate use-cases by providing adapted baseline methods and a quantitative results analysis on our dataset.
Federated Patient Similarity Network for Data-Driven Diagnosis of COVID-19 Patients

Sensitive patient data is generated from a variety of sources and then transferred to a cloud for processing. Therefore, it is exposed to security and privacy and may lead to an increase in communication costs. Edge computing will ease computing pressure through distributed computational capabilities while improving security and privacy. In this paper, we propose a Federated PSN (FPSN) model where the model is moved directly to the edge to minimize computation and communication costs. PSN has been applied as a successful approach in categorizing and diagnosing patients based on similarities against some clinical and non-clinical features. Our proposed model distributes processing at each edge node, then fuses the constructed PSN matrices at the cloud premises, which significantly reduce the model’s training and inference time and ensures quick model updates with the local client/nodes. In this paper, we propose: (i) an algorithm to evaluate patient’s data similarity at the edge; and (ii) an algorithm to implement the federated similarity network fusion at the Cloud. We conducted a set of experiments to evaluate our FPSN model against other machine learning algorithms using a COVID-19 dataset. The results obtained prove that the FPSN model accuracy is higher than the distributed PSNs at various edges and higher than the accuracies of other classification models.
Design of college student information sharing system based on blockchain

College Students information sharing is of great significance for college students' employment and enterprise recruitment, and it can improve the recruitment efficiency. As blockchain technology has been gradually applied to the sharing system in many fields, this paper presents the design of college student information system based on blockchain, which mainly including system architecture and main process design, as well as key technologies such as consensus algorithm and smart contract deployment. The system has good security and traceability, and provides a new idea for college students' information sharing.
An Improved Emotion-based Analysis of Arabic Twitter Data using Deep Learning

Nowadays everyone is using social media like Twitter, Instagram, Facebook and other social media platforms. Thoughts and feelings about everything could be expressed on these social media platforms. Sentiment and emotion analysis are important tools for analyzing people’s opinions. The lack of using deep learning models in Arabic emotion analysis and the complex structure of the Arabic language encouraged us to explore different word embedding and deep learning models to improve the Arabic emotion analysis accuracy. A combination of Arabic text preprocessing techniques were tested with multiple word embedding, machine learning and deep learning models to categorize the emotion of Arabic tweets into 8 emotions. The AraBERT deep learning model achieved the best accuracy of 75.8% and outperformed other machine learning classifiers in the field of emotion analysis.
Towards Privacy-Preserving Online Medical Monitoring with Reverse Skyline Query

With the flourish of Wireless Body Area Network (WBAN), the online medical monitoring system has attracted extensive attention. Meanwhile, due to the limited resources, the hospital tends to outsource the medical services to the cloud and requires the patients' data to be encrypted before uploading. It is bound to raise a challenge in data availability, e.g., the reverse skyline query that is widely used in monitoring systems. In this paper, we propose a privacy-preserving online medical monitoring system, in which the cloud can answer the reverse skyline query over encrypted data and return the monitored data of high-risk patients to a doctor. To achieve this goal, we first design four secure protocols that can ensure the security of operands while minimizing the communication costs between two cloud servers. Based on these privacy-preserving protocols, we propose two privacy-preserving reverse skyline query schemes that can be used in the monitoring system. Security analysis shows that our proposed scheme is indeed privacy-preserving, and performance evaluations also demonstrate the efficiency of our scheme in terms of computation and communication.
Machine Learning Approaches-Driven for Mortality Prediction for Patients Undergoing Craniotomy in ICU

We aimed to predict the mortality of patients with craniotomy in ICU by using predictive models to extract the high-risk factors leading to the death of patients from a retrospective a study.Five machine-learning (ML) algorithms were applied for training on mortality predictive models with the data from a surgical intensive care unit (ICU) database of the Fujian Provincial Hospital in China. The accuracy, precision, recall, f1 score and the area under the receiver operator characteristic curve (AUC) were used to evaluate the performance of different models, and the calibration of the model was evaluated by brier score.We demonstrated that eXtreme Gradient Boosting (XGBoost) was more suitable for the task, demonstrating a AUC of 0.84. We analyzed the feature importance with the Local Interpretable Model-agnostic Explanations (LIME) analysis and further identified the high-risk factors of mortality in ICU through this study.This study established the mortality predictive model of patients who had undergone craniotomy in ICU. Identification of the factors that had great influence on mortality has the potential to provide auxiliary decision support for clinical medical staff on their practices.
Direct Analytics of Generalized Deduplication Compressed IoT Data

Given the ever increasing volume of data generated by the Internet of Things, data compression plays an essential role in reducing the cost of data transmission and storage. However, it also introduces a barrier, namely decompression, between users and the data-driven insights they require. We propose methods for direct analytics of compressed data based on the Generalized Deduplication compression algorithm. When applied to data clustering, the accuracy of the best performing method differs by merely 1-5% when compared to analytics performed upon the uncompressed data. However, it runs four times faster, accesses only 14% as much data and requires significantly less storage since the data is always compressed. These results show that it is possible to simultaneously reap the benefits of compression and accurate, high-speed analytics in many applications.
Feedback Collection and Sentiment Analysis on the Product Reviews

In the recent era, technology and digitalization have completely changed the way of making business and making the decision. This has pushed the companies to transform to keep up with the intense competition. Decision-making is a vital part of all sorts of businesses. A good company is characterized by its ability to make decisions. To make the right decisions, there are several tools that a company uses. Some of the tools are related to research analysis and studies on the analysis. Research-based result analysis will play a major role in helping the company or any business organization to make decisions in favor of the company's product sales. In this paper, the proposed system is to design a Web application to collect feedback for a mobile company. Primarily sends the feedback link to various customers, and then the feedback responses are collected for further analyses. Sentiment analysis is performed using a machine learning technique, namely CART (Classification and Regression Tree). The decision making power gives the accurate result and provide valuable inputs to the companies regarding the areas where the company can improve the product.
Construction of co-occurrence relationship between pedestrians based on smart face capture cameras’ data

With the continuous advancement of face recognition technology and the large-scale and intensive deployment of smart face capture cameras, this paper conducts in-depth mining of its data, explores the co-occurrence relationship between pedestrians, and provides clues when there is a need to obtain someone’s real social relationship. After experimental comparison and demonstration, this paper uses Chinese Whispers clustering algorithm to identify pedestrians nodes, and then use Faiss to accelerate its construction of adjacent edges, thereby speeding up the initialization step of the graph, and solving the low problem of clustering efficiency. On this basis, co-occurrence frequency and the concept of confidence in Apriori algorithm are used to mine the co-occurrence relationship between pedestrians, and build a pedestrian co-occurrence relationship graph.
SAGES: Scalable Attributed Graph Embedding with Sampling for Unsupervised Learning

Unsupervised graph embedding method generates node embeddings to preserve structural and content features in a graph without human labeling. However, most unsupervised graph representation learning methods suffer issues like poor scalability or limited utilization of content/structural relationships, especially on attributed graphs. In this paper, we propose SAGES, a graph sampling based autoencoder framework, which can alleviate these issues. Specifically, we propose a graph sampler considering both structural and content features, in which nodes with greater influence on each other have more chances to be sampled in the same subgraph. In addition, an unbiased Graph Autoencoder (GAE) with structure-level, content-level, and community-level reconstruction loss is built from the properly sampled subgraph each iteration. The time and space complexity analysis is carried out to show the scalability of SAGES. We conducted experiments on three medium-size attributed graphs and three large attributed graphs. Experimental results illustrate that SAGES achieves the competitive performance in unsupervised attributed graph learning on various downstream tasks including node classification, link prediction, and node clustering.
Bidirectional Backpropagation for High-Capacity Blocking Networks

The new bidirectional backpropagation algorithm helps blocking networks learn and recall large numbers of image patterns. Bidirectional backpropagation exploits backward-pass learning that ordinary unidirectional backpropagation ignores. The backward pass reveals a hidden regressor in classifiers since the input neurons are identity units. Blocking networks allow deep classifiers to learn and accurately recognize more patterns than the older classifiers that use softmax neurons at the output classification layer. Blocking networks use logistic neurons at the output layer of a block. They use random bipolar coding from the vertices of a hypercube rather than from the vertices of the simplex embedded in it as with l-in-K encoding. Bidirectional deep sweeps improved classification accuracy on the CIFAR-100 image data base and did so at little extra computational cost.
Robust LWIR-based Eye Center Detection through Thermal to Visible Image Synthesis

This paper proposes a novel approach to automatically detect the eye centers in challenging thermal (Long-Wave Infrared; 8 – 14 µm) face images so they can be geometrically normalized, which is an important part of face recognition systems. Developing a new LWIR based eye center detection model would require many thermal images, which is not possible due to the unavailability of publicly available large scale LWIR face datasets. An alternative solution would be to use a pre-trained, visible band based facial landmark detection model and test it on LWIR face images. In the latter case, the challenges are the significant differences between the visible and thermal face images. In this paper, we focus on addressing this research gap by proposing a solution that is based on the following approach. First, we synthesize visible from thermal face images. Then, we exploit the existing robust visible band facial landmark detection models to detect eye centers in the synthesized visible band face images. While we empirically test different image synthesis models, we determine that StarGAN2 (an image-to-image translation generative adversarial network model that learns a mapping between the different visual domains) yields the highest eye center detection accuracy when compared to the other state-of-the-art models. Thus, we train a StarGAN2 model to be able to synthesize good quality visible band images from their thermal band counterparts. Next, we use an efficient visible band based facial landmark detection model to detect the eye centers in the synthesized visible band face images. Finally, we map these coordinates to the original LWIR face images, which are used for geometric normalization and, finally run a set of face recognition experiments. Compared to the baseline model, our approach increases the eye detection accuracy by 14%, 50%, and 30% when the normalized error (e) is set to be ≤ 0.05, ≤ 0.10, and ≤ 0.25 respectively compared to the baseline model. Our approach yields up to a 15 % and 7% increase in face recognition accuracy when using advanced deep-learning based matchers, namely Facenet and VGGFace respectively, and by 30% when using other conventional face matching techniques such as LBP-LTP matchers.
Electronic News Sentiment Analysis Application to New Normal Policy During The Covid-19 Pandemic Using Fasttext And Machine Learning

The new phase in handling COVID-19 in Indonesia, called New Normal, gives various public perspectives regarding this policy. This study aims to analyze public sentiment towards the New Normal policy through an electronic news comment column. This study uses text data in the form of comments were collected from electronic news media sites, namely www.detik.com and www.kompas.com, and taken from the comments column on Instagram social media, namely the @detikcom account. Also, use FastText method to extract features by converting data into vector values and using three classification methods, Naive Bayes (NB), Support Vector Machine (SVM), and Multilayer Perceptron (MLP). This study conducted a hyperparameter test to obtain the most optimal model. Testing the hyperparameters from FastText produces an optimal model with dimensions of 250, window size 8, epoch 1.000, and a learning rate of 0,0025. Hyperparameter testing was also carried out on the SVM and MLP classifiers. Hyperparameter testing of the SVM and MLP classifiers produces the most optimal model with the SVM method using the RBF kernel, C of 1.000, gamma of 10. In contrast, the MLP method uses the relu activation function, hidden size layer (250,250), adam optimizer, alpha 0,0001, and adaptive learning rate. The classification model was evaluated using K-fold cross-validation to produce an average f1score. The result is for the NB method 72,25% f1score, for the SVM method 92,21% f1score, and for the MLP method 90,75% f1score.
Solving hard-exploration problems with counting and replay approach

The reinforcement learning agent has been very successful in many Atari 2600 games. However, while applied to a more complex and challenging environment, it is crucial to avoid falling into the local optimum, especially when the games contain many traps, ample action space, challenging scenarios, and sporadic successful episodes. In this case, using the intrinsic motivation method can easily fall into the local optimum. If the domain knowledge is excessively used, it is not applicable when encountering different game designs. Therefore, to enhance the agent’s ability to explore and avoid catastrophic forgetting due to the fades of intrinsic motivation, a Trajectory Evaluation Module is developed and integrated with ideas from the Count-Based Exploration and Trajectory Replay method. Moreover, our approach is integrated very well with the Self Imitation Learning method and works effectively for hard-exploration video games. Our policy is also evaluated with two video games: Super Mario Bros and Sonic the Hedgehog. The experiment results show that our Trajectory Evaluation Module can help the agent pass through various obstacles and scenarios, and successfully break through all levels of Super Mario Bros.