Reviewerly/exHarmony huggingface dataset:
Measuring grammatical status in Chinese through quantitative corpus analysis

This paper applies a quantitative model developed for measuring grammatical status, using data from the Lancaster Corpus of Mandarin Chinese (lcmc). The model takes into account four quantitative factors (token frequency, collocate diversity, colligate diversity and deviation of proportions) and uses them as predictors in a binary logistic regression in order to compute a score of grammatical status between ‘0’ (lexical/non-grammatical) and ‘1’ (highly grammatical) for each given element. The results of the lcmc model are then compared to those of a similar study of the British National Corpus (bnc). The comparison suggests that token frequency emerges as one of the most relevant parameters for quantifying degrees of grammatical status in both language models, together with the collocate diversity measure when using a broad window span. On the other hand, the colligational measures (left- or right-based) and the other collocate diversity measures using small spans (left- or right-based) contribute very differently to the two languages due to their typologically distinctive structures.
Human Trait Analysis via Machine Learning Techniques for User Authentication

Machine learning is an extremely important technique that has become heavily used in different types of applications such as detection systems for fraud, intrusion or fault and monitoring systems for health or computer. Human trait analysis and identification is a field of research that needs a strong implementation for machine learning. Human trait analysis provides a tool with which human identification factors can be verified. Currently, detail aspects of human behavior are digitally and continuously logged in Big Data based platforms such as Twitter and Facebook. This continuous flow of high-volume data requires sophisticated data analysis to examine huge amounts of behavioral evidence so that human traits can be modeled. This paper proposes an innovative technique for human trait analysis that fits the needs for user's identity verification. The pioneering work of this technique is in the distinction of the normal and abnormal actions of the users, where the focus is given to these abnormal actions to establish security potential profiles. The data analysis and prediction of the proposed technique is based on the concept of machine learning and uses several models based on four techniques; K-means, Hidden Markov Model (HMM), Auto-Encoder Neural Network, and Gaussian Distribution. Experiments have been carried out in four main phases: prediction of rare user actions, filter security potential actions, build/update a user profile, and generate a real-time (i.e. just in time) set of challenging questions. Real-world scenarios are considered to demonstrate the benefits of these challenging questions in building secure knowledge-based user authentication systems.
A Survey: Benchmarking and Performance Modelling of Data Intensive Applications

In recent years, there has been a lot of focus on benchmarking and performance modelling of data-intensive applications to understand and improve the development of big data systems. Several interesting approaches were proposed; however, as of writing this paper and to the best of our knowledge, there are no comprehensive surveys that thoroughly examine the gaps, trends and trajectories of this area. To fill this void, we, therefore, present a review of the state-of-art benchmarking and performance modelling efforts in data-intensive applications. We start by introducing the two most common dataflow patterns used, for each of these patterns, we review their approach to benchmarking, modelling and validation & experimental environments. Furthermore, we construct a taxonomy and classification to provide a deep understanding of the focus areas of this domain and identify the opportunities for further research. We conclude by analysing each research gap and highlighting future trends.
Looking Back on the Past: Active Learning With Historical Evaluation Results

Active learning is an effective approach for tasks with limited labeled data. It samples a small set of data to annotate actively and is widely applied in various AI tasks. It uses an iterative process, during which we utilize the current trained model to evaluate all unlabeled samples and annotate the best samples based on a specific query strategy to update the underlying model iteratively. Most existing active learning approaches rely on only the evaluation results generated by the current model and ignore the results from previous iterations. In this paper, we propose using more historical evaluation results which can provide additional information to help better select samples. First, we apply two kinds of heuristic features of the historical evaluation results, the weighted sum of historical results and the fluctuation of the historical evaluation sequence, to improve the effectiveness of active learning sampling. Next, to further and more globally use the information contained in the historical results, we design a novel query strategy that learns how to select samples based on the historical sequences automatically. Our proposed idea is general and can be combined with both basic and state-of-the-art query strategies to achieve improvements. We test our approaches on two common NLP tasks including text classification and named entity recognition. Experimental results show that our methods significantly promote existing methods.
Subject Intention Speed Control Of Electric Wheelchair For Person With Disabilities Using Myoelectric Signals

Electric wheelchair used to help mobility of disabilities person be more independent. In the development of electric wheelchair technology, electric wheelchairs can be controlled using myoelectric signals. In previous studies of electric wheelchairs with myoelectric, the using of adaptive myoelectric signals for needs of speed changes has not become a focus. In this study design thresholding process with an adaptive threshold, then compare adaptive threshold process with analog circuit and programming algorithm. Furthermore, control wheelchair speed based on subject intention variable speed is designed which Carpi Radialis muscle movements. Results of the threshold process in the myoelectric system to determine the classification of wheelchair changes are more effective in using a programming algorithm than using an analog circuit, which is proven by a success rate of 92%. In this study, PID control system would be appropriate method for maintaining wheelchair balance and for control speed of a wheelchair.
Detection of Material Surface Cracks by Infrared Non-destructive Testing

When cracks exist on the surface of the material, there are various risks during the use of the specimen. Surface crack is also an important reason for specimen failure and damage. In this paper, the surface crack of the material is studied by using infrared thermal image microscope and adopting the passive infrared detection which is different from the traditional active infrared detection. The cracks in different directions and sizes were found by qualitative analysis of the chromium plated stainless steel. Because the cracks in the test block were too small and the contrast was not high, Matlab software was used for image processing to improve the contrast. The length of defects was detected by introducing a scale for quantitative analysis of the iron block. The error of the test results was analyzed, and a new test method to reduce the error was designed. The results of the two methods are compared and analyzed, which show that the passive infrared thermal imaging microscope has the ability to detect the size and shape of defects.
Testing the Population Administration Website Application Using the Black Box Testing Boundary Value Analysis Method

The population administration website application is a computer platform that makes it easy for the government to integrate population data collected by the Department of Population and Civil Registration with the Central Statistics Agency. This application was created to maximize the validity, accuracy, and quality of data. Each module in the application was subject to testing to ensure that it was running according to the expected functionality. This aimed to see the level of errors that occurred in the software. In this study, application testing was carried out with Black Box Testing using the Boundary Value Analysis (BVA) technique, which was to determine the lower and upper limit values based on the data being tested. The BVA test results showed that the application was able to handle data, both normal and abnormal, with a success percentage of 90.9%.
A State-of-the-art Deep Transfer Learning-Based Model for Accurate Breast Cancer Recognition in Histology Images

Breast cancer is widely prevalent in women. To diagnose it, pathologists evaluate breast histopathology images under a microscope in various magnifications. However, the number of pathologists per population is low in many countries, and a mistake is probable. Nowadays, deep learning has become a popular artificial intelligence research trends. Even though deep neural networks have obtained promising results in image processing, the need for extensive training data avoids its use in medical image processing. After the evaluation of various state-of-the-art deep learning methods and algorithms in medical data processing, this study proposes an effective deep transfer learning-based model, relying on pre-trained DCNN using an extensive data of ImageNet dataset which improves state-of the-art systems in both binary and multi-class classification. The weights of the pre-trained DesneNet121 on the Imagenet are transferred as initial weights first, and then the model is fine-tuned with a deep classifier along with data augmentation to distinguish various malignant and benign samples in the two categories of binary and multi-class classification. In the multi-class classification, the proposed model obtained up to 97% image-level accuracy. In binary classification, the model obtained up to 100% image-level accuracy. The achieved-results are outperforming previous studies accuracies in multiple performance metrics in breast cancer CAD systems. Moreover, the proposed method is both flexible and scalable, meaning it can be easily expanded to cover the detection of other types of diseases in the future and be integrated with more CNNs to increase its generalization capabilities.
Functionality Aware Dynamic Composition of Web Services

The composition of the web service is a common technique to attain the best results of complex web tasks.The selection of appropriate web services, linking those services in the action flow and attaining the actual functionality of the task are the important factors to be considered.Even though different frameworks and methods have been proposed to dynamically compose web services, each method has its advantage and disadvantage over the other.Most of the methods give much importance to the Quality of Service (QoS) but fail to achieve the actual functionality after composition.This paper proposes a functionalityoriented composition technique for composing web services.Moreover, this method helps reach the extreme functionality of each web service in the composition towards customer satisfaction.Apart from considering the overall QoS of every single service, the non-functional parameters associated with these services are also considered for achieving the expected functionality.Each of these nonfunctional parameters has a vital role in the functional performance of the web service.The web services that satisfy the non-functional requirements are chosen to form the composition to attain the best performance.The list of services in the proposed composition method is different from the conventional one, which is composed based on the overall QoS.The non-functional parametric values, the QoS of each web service and the overall QoS after composition are evaluated for the proposed method and experimentally analyzed to prove their advantage over the others.
Constructing an attribute based chameleon hash function

Chameleon hash function equips with several special properties and is widely implemented to construct chameleon digital signatures. In this research, we take the advantages of chameleon hash and attribute based cryptography to present an attribute based chameleon hash function. The new presented function not only inherits the properties of chameleon hash, but also adds the new features brought by attribute based cryptography. By proof, the attribute based chameleon hash function achieves the essential security properties. The proposed scheme can be used be construct attribute based chameleon signature in the future.
Detection of Recoloring and Copy-Move Forgery in Digital Images

Due to the availability of numerous image manipulation tools, fraud images can be generated very easily and effectively. These fraud images are quite difficult to recognize. A section of the image is copied and pasted at some other location on the same image in copy-move forgery to drop meaningful objects or to bring additional information which is not present actually in the image. Whereas, the image recoloring techniques normally change the images via a variety of mechanisms like contrast enhancement and colorization. In the proposed method, copy move forgery detection is based on similarities in the images and finding the forged part by using threshold and contouring techniques. Recolored image detection uses a convolution neural network with three layers which outputs the probability of recoloring. As the techniques for image forging are developing faster, the necessity of highly efficient and accurate image forgery detection also increases. Here, this proposed system focuses on both recoloring and copy-move forgery detection.
Digital transformation of higher educational system

Abstract Digital technologies impact innovation into all areas of society, changing socio-economic relations, put demands on qualification of university graduates. The need to build the competences of educators and students in relation to digital education is acute, the range of strategies proposed to achieve this outcome. A new education system is forming. It is characterized by blending online learning with traditional approaches. Online learning and teaching marketplaces have become the primary source of knowledge all over the world. Vectors of digital transformation, core university processes, and workflows are included in the scope of the study. The article proposes ways to advance business model of university through digitalization and possible sources of funding.
HyperWall: A Hypervisor for Detection and Prevention of Malicious Communication

Malicious programs vary widely in their functionality, from key-logging to disk encryption. However, most malicious programs communicate with their operators, thus revealing themselves to various security tools. The security tools incorporated within an operating system are vulnerable to attacks due to the large attack surface of the operating system kernel and modules. We present a kernel module that demonstrates how kernel-mode access can be used to bypass any security mechanism that is implemented in kernel-mode. External security tools, like firewalls, lack important information about the origin of the intercepted packets, thus their filtering policy is usually insufficient to prevent communication between the malicious program and its operator. We propose to use a thin hypervisor, which we call "HyperWall", to prevent malicious communication. The proposed system is effective against an attacker who has gained access to kernel-mode. Our performance evaluation shows that the system incurs insignificant ( $$\approx $$ 1.64% on average) performance degradation in real-world applications.
Inferring Coflow Size Based on Broad Learning System in Data Center Network

In recent years, coflow size-based scheduling has become a hotspot in data center networks since it can effectively optimize the coflow completion time (CCT). However, it is a challenging work to predict the coflow size accurately and rapidly with limited information. This paper proposes a Broad Learning System based coflow size inferring model, which employs Multi -RELIEF method to extract features and trains BLS model on Facebook logs with incomplete information. Experiment results show that the accuracy and sensitivity of BLS-based inferring model are higher than the ELM and XGBoost methods by 31.6% when we use only half of coflow information, and the prediction time remains below 6ms that meets the online processing requirement of most data center networks. Besides, the simulated scheduling results indicate that the CCT of the BLS-based coflow scheduler outperforms the FIFO, Aalo and MCS schedulers at most time.
Weighted Adaptive Partition for Heterogeneous IoT Data Stream

In the past, many researchers used sliding-window mechanism in Internet of Things (IoT) data stream mining, which can divide the incoming large volume data into small segments and improve the information processing rate. With the emergence of heterogeneous IoT data streams, data forms become richer and more diverse. The fusion analysis of multimodality information can improve the accuracy of information processing and network decision-making, because it can make up for the lack of some information in a single-mode data stream. However, the representation of information in different data sources is different, and the amount of information contained in it is distinct. Thus, a weighted adaptive partition algorithm is proposed in this article, which performs a dynamic weighted fusion of multisource heterogeneous IoT data in sliding windows. The article adopts the Gaussian mixture model (GMM) to adaptively determine the weights of each data source, which is calculated based on the judgement confidence of each modal data to the change in data stream. Besides, the probability distribution change detection mechanism based on multimodality restricted Boltzmann machine (multimodality RBM) is proposed, which measures the joint probability distribution by the weighted data’s energy value. Finally, according to the energy change rate of the data stream, the size of the window is adjusted adaptively to realize the partition for heterogeneous IoT data stream. The algorithm proposed in this article is verified on the CUAVE data set and the analysis of results shows that the data under adaptive weighted fusion has better performance.
Mixed Reality of Augmented Reality in Mobile Learning for Aircraft Maintenance

Recently, job instruction training (JIT) has widely used mobile learning (M-learning) in undertakings, such as technical maintenance and other collaborative fields. Emerging new media, including mixed reality, has influenced the way people learn through its adaptive learning algorithm. With the concept of self-directed learning, M-learning may be a better choice for JIT when an enterprise has no available instructor and minimal operating profits. The M-learning system derives from Malcolm Knowles' theory [2] of andragogy and constructs multimedia instruction tools that result in suitable game-based learning. With its practical and easy-to-use function, the system focuses on the simulation training of installing and disassembling the A330 Airbus brakes system, for example. The development process shows how one can effectively use adaptive learning in the mobile devices' interactive design when following the ADDIE model. In this study, we analyze the user experience's validity to realize both the advantages and disadvantages of mobile learning in augmented reality. We demonstrate how the learning system improves through the instruction methods, animation display, and interactive design to achieve the target of assimilating and consolidating knowledge. We hope that this study increases the efficiency of job instruction training in Taiwanese companies. We have developed this research in three phases: 1) Research consolidation on the development of M-learning strategies within the theory of andragogy; 2) The improvement of visual perception through technology aids in M-learning, and 3) The comparative analysis of the characteristics of multimedia interfaces.
Post-quantum Certificates for Electronic Travel Documents

Public key cryptosystems play a crucial role in the security of widely used communication protocols and in the protection of data. However, the foreseen emergence of quantum computers will break the security of most of the asymmetric cryptographic techniques used today, including those used to verify the authenticity of electronic travel documents. The security of international borders would thus be jeopardised in a quantum scenario. To overcome the threat to current asymmetric cryptography, post-quantum cryptography aims to provide practical mechanisms which are resilient to attacks using quantum computers. In this paper, we investigate the practicality of employing post-quantum digital signatures to ensure the authenticity of an electronic travel document. We created a special-purpose public key infrastructure based on these techniques, and give performance results for both creation and verification of certificates. This is the first important step towards specifying the next generation of electronic travel documents, as well as providing a valuable test use case for post-quantum techniques.
CNN Pre-initialization by Minimalistic Part-Learning for Handwritten Numeral Recognition

The performance of all types of neural networks is affected by initialization of weights since most of these networks follow some form of the derivative gradient descent algorithm for weight optimization that tends to get trapped in local minima. A new scheme is presented in our work for initializing the weights of Convolutional Neural Networks (CNNs) by part-learning using only 5% of the training data, in a minimalistic approach. The problem at hand is the classification of handwritten numeral images. The parts that are learned, by two-way CNNs, comprise of the top-half and bottom-half of the numeral image respectively, with the second half of the image kept masked. The two set of weights initialized in this manner are respectively fine-tuned on the remaining 95% training images. The probabilistic softmax scores of the two CNNs are fused in the last stage to decide the test label. Our work validates the theory inspired from human cognition that learning in stages with increasing size and complexity of the training data improves the performance over time, rather than training on the complete dataset at one go. Experiments on the benchmark MNIST handwritten English numeral dataset yield high accuracies as compared to the state of the art.
The Enhancement of Underexposed Images with Blurred Reflectance

The images captured in the low-light conditions always suffer from low visibility. Enhancing the visibility of the low-light image is of broad application to various computer vision tasks. Based on the classical Retinex model, previous methods assume the reflectance components as a well-exposed image. In this paper, we introduce the blurring distortion into the Retinex model to cover more general and challenging scenarios. We further propose a two-stage framework to extract the reflectance images and remove the blurring distortion separately. Specifically, we optimize the whole network by embedding a mechanism robust to the pixel misalignment in the training dataset. The experimental results show that our proposed method achieves promising results.