Reviewerly/exHarmony huggingface dataset:
The Use of Blended Learning Approach in EFL Education

The 21ST century education is a revolution that encounters the use of ICT (Information Communication Technology) as a component tool of education aligned with the contexts of teaching and learning. It is widely used in every surface of education particularly in teaching and learning EFL (English as a Foreign Language). When it comes to EFL education, traditional language teaching methodologies are intensively substituted with the new technological-integrated approaches. Currently, blended learning is used as an applicable approach and EFL education is surrounded by its features. It has really restructured the educational context and has paved the way for effective EFL practices. In fact, blended learning cooperates EFL educators a variety of interactive language activities while it is double component, integration of face-to-face education and technology-generated approaches. Therefore, this article investigates the use of blended learning approach in EFL education and undertakes an in-depth review of literatures, practices, and authentic enhancements of blended learning in the context of EFL education. The study enriches creative ideas toward the use of blended learning approaches in EFL classrooms. It indicates that blended learning approach provides a flexible language learning platform, endorse academic achievements of the learners, and influences EFL education.
Recent Trends in Deep Learning Based Abstractive Text Summarization

With the rapid growth of cyberspace and the appearance of knowledge exploration era, good text summarization method is vital to reduce the large data. Text summarization is the mechanism of extracting the important information which gives us an overall abstract or summary of the entire document and also reduces the size of the document. It is open problem in Natural Language Processing (NLP) and a difficult work for humans to understand and generate an abstract manually while it have need of a accurate analysis of the document. Text Summarization has become an important and timely tool for assisting and interpreting text information. It is generally distinguished into: Extractive and Abstractive. The first method directly chooses and outputs the relevant sentences in the original document; on the other hand, the latter rewrites the original document into summary using NLP techniques. From these two methods, abstractive text summarization is laborious task to realize as it needs correct understanding and sentence amalgamation. This paper gives a brief survey of the distinct attempts undertaken in the field of abstractive summarization. It collectively summarizes the numerous technologies, difficulties and problem of abstractive summarization.
Virtual Reality

During the 2019 EDUCAUSE Annual Conference, a large proportion of programs were about virtual reality. This article discusses how virtual reality could be used in libraries and how some institutions are creating VR content.
Image Segmentation of Brain MR Images Using Otsu’s based Hybrid WCMFO Algorithm

In this study, a novel hybrid Water Cycle Moth-Flame Optimization (WCMFO) algorithm is proposed for multilevel thresholding brain image segmentation in Magnetic Resonance (MR) image slices. WCMFO constitutes a hybrid between the two techniques, comprising the water cycle and moth-flame optimization algorithms. The optimal thresholds are obtained by maximizing the between class variance (Otsu’s function) of the image. To test the performance of threshold searching process, the proposed algorithm has been evaluated on standard benchmark of ten axial T<sub>2</sub>-weighted brain MR images for image segmentation. The experimental outcomes infer that it produces better optimal threshold values at a greater and quicker convergence rate. In contrast to other state-of-the-art methods, namely Adaptive Wind Driven Optimization (AWDO), Adaptive Bacterial Foraging (ABF) and Particle Swarm Optimization (PSO), the proposed algorithm has been found to be better at producing the best objective function, Peak Signal-to-Noise Ratio (PSNR), Standard Deviation (STD) and lower computational time values. Further, it was observed thatthe segmented image gives greater detail when the threshold level increases. Moreover, the statistical test result confirms that the best and mean values are almost zero and the average difference between best and mean value 1.86 is obtained through the 30 executions of the proposed algorithm.Thus, these images will lead to better segments of gray, white and cerebrospinal fluid that enable better clinical choices and diagnoses using a proposed algorithm.
Can cryptocurrencies overtake the fiat money

Since the awakening of Bitcoin, the terms 'blockchain', 'cryptocurrencies' have become the buzz words. Cryptocurrencies were thought to be very promising and boomed feverously in the hope that it might be capable of replacing the traditional currency. On this background, the paper has following objectives: 1) to compare the fiat money with cryptocurrency; 2) to study the potential of cryptocurrency of being capable of replacing the fiat money; 3) to propose the framework to evaluate the sustainability of the possible candidates to be the dominating currency. The researchers have applied the analytic hierarchy process (AHP) method to derive ratio scales from paired comparisons and rated the expected performances for two alternatives. Nine factors were selected with the hierarchical structure to measure the relative performance of proposed candidates. The results found that fiat money is still preferred to cryptocurrency due to many reasons.
Detecting phishing attacks using a combined model of LSTM and CNN

Phishing, a social engineering crime which has been existing for more than two decades, has gained significant research attention to find better solutions to face against the very dynamic strategies of phishing.The financial sector is the primary target of phishing, and there are many different approaches to combat phishing attacks.Software-based detection approaches are more prominent in phishing detection; however, still, there is no robust solution that can stable for a long period.The primary purpose of this paper is to propose a novel solution to detect phishing attacks using a combined model of LSTM and CNN deep networks with the use of both URLs and HTML pages.The URLs are learned using an LSTM network with 1D convolutional, and another 1D convolutional network is used to learn the HTML features.These two networks were trained separately and combined through a sigmoid layer by dropping the last layer of each model to have the proposed model.The proposed model reached 98.34% in terms of accuracy, and that is above the previously recorded highest accuracy of 97.3% among the detection models used both URL and HTML features in the explored literature.The solution requires feature extraction only with HTML pages, and URLs were directly fed with a minimum pre-processing.Although the proposed solution uses extracted HTML features, those do not depend on third-party services.Therefore, an efficient real-time application can be implemented using the proposed model to detect phishing attacks to safeguard Internet users.
Open Data Kit: Mobile Data Collection Framework For Developing Countries

To evaluate and improve the delivery of health care services and programs; to collect and share information on agro-biodiversity; to fill the gaps in the data of artisanal or industrial fisheries, the use of computer tools of data collection is a major asset. It is now obvious that most of these tools are difficult to use in developing countries. And this is because of the complexity of use, either because they are commercial solutions or simply because these tools require a stable internet connection. This study is the result of thorough research on Open Data Kit (ODK), a suite of tools fully compatible with the context of developing countries and fully open source software. This article traces the research work done for the design, implementation, and improvement of ODK. A description of the different ODK extensions that can be found in the literature is given in addition to the reasons that explain the success of ODK in developing countries. To highlight the utility of ODK, an analysis of the areas of use of ODK has been made. This analysis concludes that ODK is mainly used to improve the intervention effectiveness of health system agents in developing countries.
Credit Card Fraud Detection Using Machine Learning

It is indispensable that credit card organizations can recognize deceitful transactions with the goal that client is not charge for things which they didn't buy.these issues can be handled with data Science and thier significance, alongside machine/soft learning, couldn't be more important.This venture expects to delineate the demonstrating of data sets utilizing machine/soft learning with Credit cards fraud/scam Identification.The Credit Cards fraud/scam detection Issue incorporates displaying previous credit cards exchanges with information of the ones that ended up being misrepresentation.That model is then used for perceive whether another transaction is deceitful or not.Our goal is to recognize 100% of deceitful transactions and limiting the wrong fraud/scam classification.Credit cards fraud/scam Identification is a average example in grouping.here In this cycle, we had centered on dissecting, pre-preparing datas set collections just lie the sending of numerous inconsistency detection or identification numerous algorithm, for example, Random forest algorithm, KNN algorithm and
Keyword guessing on multi-user searchable encryption

Multi-user searchable encryption enables the client to perform keyword search over encrypted data while supporting authorisation management. Most of these schemes are constructed using public key encryption. However, public key encryption with keyword search is vulnerable to keyword guessing attack. Consequently, a secure channel is necessarily involved for secret information transformation, which leads to extra severe burden. This vulnerability is recognised in traditional searchable encryption, but it is still undecided whether it also exists in multi-user setting. In this paper, we firstly point out that keyword guessing attack is also a problem in multi-user searchable encryption without the supposed secure channel. By an in-depth investigation of some schemes proposed recently and simulating the keyword guessing attack on them, we present that none of these schemes can resist this attack. We make a comprehensive security definition and propose some open problems.
Can Abnormality be Detected by Graph Neural Networks?

Anomaly detection in graphs has attracted considerable interests in both academia and industry due to its wide applications in numerous domains ranging from finance to biology. Meanwhile, graph neural networks (GNNs) is emerging as a powerful tool for modeling graph data. A natural and fundamental question that arises here is: can abnormality be detected by graph neural networks? In this paper, we aim to answer this question, which is nontrivial. As many existing works have explored, graph neural networks can be seen as filters for graph signals, with the favor of low frequency in graphs. In other words, GNN will smooth the signals of adjacent nodes. However, abnormality in a graph intuitively has the characteristic that it tends to be dissimilar to its neighbors, which are mostly normal samples. It thereby conflicts with the general assumption with traditional GNNs. To solve this, we propose a novel Adaptive Multi-frequency Graph Neural Network (AMNet), aiming to capture both low-frequency and high-frequency signals, and adaptively combine signals of different frequencies. Experimental results on real-world datasets demonstrate that our model achieves a significant improvement comparing with several state-of-the-art baseline methods.
Variational quantum attacks threaten advanced encryption standard based symmetric cryptography

We propose a variational quantum attack algorithm (VQAA) for classical advanced encryption standard (AES)-like symmetric cryptography, as exemplified by the simplified-data encryption standard (S-DES). In the VQAA, the known ciphertext is encoded as the ground state of a Hamiltonian that is constructed through a regular graph, and the ground state can be found using a variational approach. We designed the ansatz and cost function for the S-DES's variational quantum attack. It is surprising that sometimes the VQAA is even faster than Grover's algorithm as demonstrated by our simulation results. The relationships of the entanglement entropy, concurrence, and the cost function are investigated, which indicate that entanglement plays a crucial role in the speedup.
Research on Feature Fusion Speech Emotion Recognition Technology for Smart Teaching

At present, there are numerous intelligent teaching applications based on various advanced pattern recognition technologies, such as face sign-in, classroom action recognition, student facial expression recognition, and other systems which have been gradually applied to major schools. Speech emotion recognition can analyze the characteristics of current teaching emotions, so as to discover the rules and details of teachers’ grasp of emotions. In order to realize the task of emotion classification in the process of intelligent teaching through machine learning technology, a teaching speech emotion recognition method with multifeature fusion and deep learning is proposed. The proposed method front-end processes the speech spectrum features in a multifeature fusion manner and combines them with an artificial neural network classifier to form the final speech emotion recognition model. First, after speech preprocessing, three features are selected and fused features using a network structure trained by parallel subnetworks. Then, the classifier used is a hybrid neural network classifier combining a convolutional neural network and a recurrent neural network. Finally, 100 open web courses were used to train and test the model. The test results show that the hybrid neural network sound model using multifeature fusion has good teaching speech emotion recognition capability.
Gesture Recognition System Based on Improved YOLO v3

In order to realize the normal communication between the hearing impaired and the society, this paper combines the Raspberry Pi and YOLO v3 target detection to achieve the automatic human gesture recognition. The system consists of four parts: image acquisition and transmission module, image preprocessing, training recognition module, speech interaction and front-end display module. In this paper, we combine the image algorithm in OpenCV for image preprocessing, and propose a human gesture recognition system based on improved YOLOV3, to solve the problem of low accuracy and slow speed of the gesture recognition algorithm based on feature extraction in image recognition, and test it in different scenes. The experimental results show that the recognition accuracy of the system with the advanced algorithm is improved compared with that before. The gesture recognition accuracy of the system is around 90%, which means the recognition results are more reliable and can meet the real-time requirements.
EOCSA: Predicting prognosis of Epithelial ovarian cancer with whole slide histopathological images

Ovarian cancer is one of the most serious cancers that threaten women around the world. Epithelial ovarian cancer (EOC), as the most commonly seen subtype of ovarian cancer, has rather high mortality rate and poor prognosis among various gynecological cancers. Survival analysis outcome is able to provide treatment advices to doctors. In recent years, with the development of medical imaging technology, survival prediction approaches based on pathological images have been proposed. In this study, we designed a deep framework named EOCSA which analyzes the prognosis of EOC patients based on pathological whole slide images (WSIs). Specifically, we first randomly extracted patches from WSIs and grouped them into multiple clusters. Next, we developed a survival prediction model, named DeepConvAttentionSurv (DCAS), which was able to extract patch-level features, removed less discriminative clusters and predicted the EOC survival precisely. Particularly, channel attention, spatial attention, and neuron attention mechanisms were used to improve the performance of feature extraction. Then patient-level features were generated from our weight calculation method and the survival time was finally estimated using LASSO-Cox model. The proposed EOCSA is efficient and effective in predicting prognosis of EOC and the DCAS ensures more informative and discriminative features can be extracted. As far as we know, our work is the first to analyze the survival of EOC based on WSIs and deep neural network technologies. The experimental results demonstrate that our proposed framework has achieved state-of-the-art performance of 0.980 C-index. The implementation of the approach can be found at https://github.com/RanSuLab/EOCprognosis.
Digital Watermarking as an Adversarial Attack on Medical Image Analysis with Deep Learning

In the past years, Deep Neural Networks (DNNs) have become popular in many disciplines such as Computer Vision (CV), and the evolution of hardware has helped researchers to develop many powerful Deep Learning (DL) models to deal with several problems. One of the most important challenges in the CV area is Medical Image Analysis. However, adversarial attacks have proven to be an important threat to vision systems by significantly reducing the performance of the models. This paper brings to light a different side of digital watermarking, as a potential black-box adversarial attack. In this context, apart from proposing a new category of adversarial attacks named watermarking attacks, we highlighted a significant problem, as the massive use of watermarks, for security reasons, seems to pose significant risks to vision systems. For this purpose, a moment-based local image watermarking method is implemented on three modalities, Magnetic Resonance Images (MRI), Computed Tomography (CT-scans), and X-ray images. The introduced methodology was tested on three state-of-the art CV models, DenseNet 201, DenseNet169, and MobileNetV2. The results revealed that the proposed attack achieved over 50% degradation of the model's performance in terms of accuracy. Additionally, MobileNetV2 was the most vulnerable model and the modality with the biggest reduction was CT-scans.
A focus groups study on data sharing and research data management

Abstract Data sharing can accelerate scientific discovery while increasing return on investment beyond the researcher or group that produced them. Data repositories enable data sharing and preservation over the long term, but little is known about scientists’ perceptions of them and their perspectives on data management and sharing practices. Using focus groups with scientists from five disciplines (atmospheric and earth science, computer science, chemistry, ecology, and neuroscience), we asked questions about data management to lead into a discussion of what features they think are necessary to include in data repository systems and services to help them implement the data sharing and preservation parts of their data management plans. Participants identified metadata quality control and training as problem areas in data management. Additionally, participants discussed several desired repository features, including: metadata control, data traceability, security, stable infrastructure, and data use restrictions. We present their desired repository features as a rubric for the research community to encourage repository utilization. Future directions for research are discussed.
Aeneas: Rust verification by functional translation

We present Aeneas, a new verification toolchain for Rust programs based on a lightweight functional translation. We leverage Rust's rich region-based type system to eliminate memory reasoning for many Rust programs, as long as they do not rely on interior mutability or unsafe code. Doing so, we relieve the proof engineer of the burden of memory-based reasoning, allowing them to instead focus on functional properties of their code. Our first contribution is a new approach to borrows and controlled aliasing. We propose a pure, functional semantics for LLBC, a Low-Level Borrow Calculus that captures a large subset of Rust programs. Our semantics is value-based, meaning there is no notion of memory, addresses or pointer arithmetic. Our semantics is also ownership-centric, meaning that we enforce soundness of borrows via a semantic criterion based on loans rather than through a syntactic type-based lifetime discipline. We claim that our semantics captures the essence of the borrow mechanism rather than its current implementation in the Rust compiler. Our second contribution is a translation from LLBC to a pure lambda-calculus. This allows the user to reason about the original Rust program through the theorem prover of their choice. To deal with the well-known technical difficulty of terminating a borrow, we rely on a novel approach, in which we approximate the borrow graph in the presence of function calls. This in turn allows us to perform the translation using a new technical device called backward functions. We implement our toolchain in a mixture of Rust and OCaml. Our evaluation shows significant gains of verification productivity for the programmer. Rust goes to great lengths to enforce static control of aliasing; the proof engineer should not waste any time on memory reasoning when so much already comes "for free"!
A Novel Effective Vehicle Detection Method Based on Swin Transformer in Hazy Scenes

Under bad weather, the ability of intelligent vehicles to perceive the environment accurately is an important research content in many practical applications such as smart cities and unmanned driving. In order to improve vehicle environment perception technology in real hazy scenes, we propose an effective detection algorithm based on Swin Transformer for hazy vehicle detection. This algorithm includes two aspects. First of all, for the aspect of the difficulty in extracting haze features with poor visibility, a dehazing network is designed to obtain high-quality haze-free output through encoding and decoding methods using Swin Transformer blocks. In addition, for the aspect of the difficulty of vehicle detection in hazy images, a new end-to-end vehicle detection model in hazy days is constructed by fusing the dehazing module and the Swin Transformer detection module. In the training stage, the self-made dataset Haze-Car is used, and the haze detection model parameters are initialized by using the dehazing model and Swin-T through transfer learning. Finally, the final haze detection model is obtained by fine tuning. Through the joint learning of dehazing and object detection and comparative experiments on the self-made real hazy image dataset, it can be seen that the detection performance of the model in real-world scenes is improved by 12.5%.