Reviewerly/exHarmony huggingface dataset:
A Blockchain-based Flexible Traceability System for IoT Cards

The real-name registration management of the Internet of Things (IoT) card is of great significance to the security development of the Internet of Things and the security construction of Cyberspace. However, IoT cards are decoupled from people in the circulation process, are often "re-sold", data sharing is difficult in cross-departmental circulation, and data quality is low. But the IoT card is the key for things to connect to the Cyberspace. The traceable management of the IoT card can ensure the identity analysis of Machines, Things, and People in the Cyberspace. This paper gives us a solution based on blockchain and secure terminals. With this solution, we can promote the data on the chain of each transaction chain effectively, improves the accuracy and effectiveness of the collected information, realizes the joint participation and system co-governance of different responsible entities, and improves the IoT card Real-name registration rate, the management ability to the network, etc. Experiments have fully proved the effectiveness, reliability, and advancement of the system, which has a massive role in accelerating the improvement of the comprehensive level of network security technology and helping the construction of a cyberspace integrity system.
Atom specific multiple kernel dictionary based Sparse Representation Classifier for medium scale image classification

Kernel based Sparse Representation Classifier (KSRC) can classify images with acceptable performance. In addition, Multiple Kernel Learning based SRC (MKL-SRC) computes the weighted sum of multiple kernels in order to construct a unified kernel while the weight of each kernel is calculated as a fixed value in the training phase. In this paper, an MKL-SRC with non-fixed kernel weights for dictionary atoms is proposed. Kernel weights are embedded as new variables to the main KSRC goal function and the resulted optimization problem is solved to find the sparse coefficients and kernel weights simultaneously. As a result, an atom specific multiple kernel dictionary is computed in the training phase which is used by SRC to classify test images. Also, it is proved that the resulting optimization problem is convex and is solvable via common algorithms. The experimental results demonstrate the effectiveness of the proposed approach.
ELMarket: An E-Learning System Based on Blockchain

With the development of information technology in the era of big data, the traditional education model based on offline courses can no longer meet people's needs. Online education (i.e., E-Learning) has become a new type of education model. Blockchain has the characteristics of decentralization, traceability, collective maintenance, data disclosure, non-tampering and so on. Its characteristics make it an ideal solution for the existing problems of online education. In this paper, we proposed a new type of decentralized system which called ELMarket(ELM), aiming to solving the problems and deficiencies of existing online educational platforms, including that student user power is insufficient, participants are not interactive enough, and the copyright of educational resources is not protected well.
SPA: Harnessing Availability in the AWS Spot Market

Amazon Web Services (AWS) offers transient virtual servers at a discounted price as a way to sell unused spare capacity in its data centers. Although transient servers are very appealing as some instances have up to 90% discount, they are not bound to regular availability guarantees as they are opportunistic resources sold on the spot market. In this paper, we present SPA, a framework that remarkably increases the spot instance reliability over time due to insights gained from the analysis of historical data, such as cross-region price variability and intervals between evictions. We implemented the SPA reliability strategy, evaluated them using over one year of historical pricing data from AWS, and found out that we can increase the transient instance lifetime by adding a pricing overhead of 3.5% in the spot price in the best scenario.
Compilation flow for classically defined quantum operations

We present a flow for synthesizing quantum operations that are defined by classical combinational functions. The discussion will focus on out-of-place computation, i.e., <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$U_{f}$</tex> : <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\vert x\rangle\vert y\rangle\vert 0\rangle^{k}\rightarrow\vert x\rangle\vert y\oplus f(x)\rangle\vert 0\rangle^{k}$</tex> . Our flow allows users to express this function at a high level of abstraction. At its core, there is an improved version of the current state-of-the-art algorithm for synthesizing oracles [1]. As a result, our synthesized circuits use up to 25 % fewer qubits and up to 43 % fewer Clifford gates. Crucially, these improvements are possible without increasing the number of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$T$</tex> gates nor the execution time.
E-Learning and M-Learning Technological Intervention in Favor of Mathematics

This paper is based on pre-experimental research that assessed the impact of E-Learning and M-Learning technological intervention to promote mathematical skills learning. The sample consisted of 16 students between 11 and 12 years old (Mage = 11.94, SD = .25). They received an educational technology program for 6 sessions. Pre and posttest measurements were performed to assess direct proportionality knowledge and ability to solve problems with 2 artifacts respectively. The results showed statistically significant differences between the pre and posttest (knowledge t (15) = −7.20, p = <. 001, d = .88 and ability to solve problems t (15) = −6.75, p = < .001, d = .87). Consequently, it is highlighted the improvement that the learning process based on E-learning and M-learning has in the learning process of mathematics, as evidenced after the intervention ended. The data is discussed around the need to incorporate this type of technological tools in the learning of calculation skills and previous research.
Identifying cheating behaviour with machine learning

We have investigated machine learning based cheating behaviour detection in physical activity-based smart-phone games. Sensor data were acquired from the accelerometer/gyroscope of an iPhone 7 during activities such as jumping, squatting, stomping, and their cheating counterparts. Selected attributes providing the most information gain were used together with a sequential model yielding promising results in detecting fake activities. Even better results were achieved by employing a random forest classifier. The results suggest that machine learning is a strong candidate for detecting cheating behaviours in physical activity-based smartphone games.
Meta-peering

We introduce meta-peering, a term that encompasses the set of tools needed to ease and automate the Internet Service Provider (ISP) peering process; starting with identifying a list of ISPs that are likely to peer, generating respective BGP configurations, and monitoring these sessions for outages or peering agreement violations. In this paper, we describe how existing tools can be leveraged to implement meta-peering and focus on instrumenting the automation of peer selection process. Utilizing PeeringDB and CAIDA datasets to identify possible peering points for requester and candidate ISPs, we estimate candidate ISP's traffic matrix and consider ISPs' internal policies to generate acceptable peering contracts.
A robust method for dynamic image stitching on a fully mechanized mining face

Abstract Video surveillance systems can be applied in coal mines for remote monitoring and for production control. Stitching video images into a panorama enhances the usability of video systems, since a panorama offers a wider view than single images do. But there are big challenges when conventional image stitching methods are applied to the domain of coal mine, especially in the mining faces. These challenges consist of non-uniform illumination, missed scenes and oblique panoramas. In this paper, a robust method was proposed to solve these three problems: (i) to overcome the non-uniform illumination on a mining face, the wide dynamic range technology and the histogram matching algorithm were used to enhance single images and reduce differences among images, respectively; (ii) to eliminate the missed scenes, overlapped images were quickly taken, then the feature matching method and template recognition method were adaptively used to achieve robust stitching and (iii) to mitigate the obliqueness of panoramas, vertical correction technology was used, which exploited the posture information of the camera. Next, the adjacent panoramas were concatenated and experiments were conducted on a fully mechanized mining face. The results show that the proposed method solves these three problems well and a dynamic panorama of the partial long-wall mining face is outputted. The research provides a new approach for displaying extended scenes of stope faces in intelligent collieries.
A Comparative Study on the Detection of Image Forgery of Tampered Background or Foreground

Recently tampering and manipulating of images has become easier with advanced image editing applications. The process of making a composite image by combining image fragments from different images is called image splicing. One of the common types of image splicing forgery is to change the whole background of the images for different purposes. However, very few studies in the literature directly address the tampered background or foreground detection. We present a comparative study and analysis of image splicing detection methods on the detection of image forgery of tampered background or foreground. First, we create a novel dataset contains both tampered and non-tampered images. Using the dataset, we conduct a comparison of the state-of-the-art image splicing detection methods. The experimental results indicate that existing image splicing detection methods are not sufficient for detecting background or foreground forgeries, and new methods should be developed.
Enhancing argumentation component classification using contextual language model

Abstract Arguments facilitate humans to deliver their ideas. The outcome of the discussion heavily relies on the validity of the argument. If an argument is well-composed, it is more effective to grasp the core idea behind the argument. To grade the argument, machines can be utilized by decomposing into semantic label components. In natural language processing, multiple language models are available to perform this task. It is divided into context-free and contextual models. The majority of previous studies used hand-crafted features to perform argument component classification, while state of the art language models utilize machine learning. The majority of these language models ignore the context in an argument. This research paper aims to analyze whether by including the context in the classification process may improve the accuracy of the language model which will enhance the argumentation mining process as well. The same document corpus is fed into several language models. Word2Vec and GLoVe represent the context free models, while BERT and ELMo as context sensitive language models. Accuracy and time from each model are then compared to determine the importance of context. The result shows that contextual language models are proven to be able to boost classification accuracy by approximately 20%. However, time comes as a cost where contextual models require longer training and prediction time. The benefit from the increase in accuracy outweighs the burden of time. Thus, as a contextual task, argumentation mining is suggested to use contextual model where context must be included to achieve promising results.
Evolutionary Algorithm Based Summarization for Analyzing COVID-19 Medical Reports

The COVID-19 pandemic emerged in the month of December from the very popular city Wuhan of China and spread rapidly all over the country. The present situation depicts the progress in various less-explored areas to strengthen the global knowledge base for preventing and addressing the COVID-19 pandemic. Novel coronavirus disease became a major health concern due to its pathogenicity and world widespread distribution. Many good scientific research papers related to COVID-19 have recently published using the dataset CORD-19. Analysis of these massive data and identifying meaningful information is a crucial and very challenging task in health care and bio-medical science research. In this chapter, we try to extract important information about COVID-19 from the available text documents, such as research papers, articles, journals, reports, and other publications. Initially, in the chapter, these types of text data are preprocessed by means of sentence tokenization, then removal of stopwords, and finally stemming operations. Consequently, a bag-of-words is generated to represent each report in a structured form and a bi-objective strength Pareto evolutionary algorithm based text summarization technique is concerned to produce the summary of the report. The method has two objective functions: one based on the length of the summary, a predetermined value, and the other based on the clustering coefficient concept of graph theory. After convergence of the evolutionary algorithm, the fittest chromosome in the final population provides a summary of the report. The method is applied on COVID bio-medical reports publicly available in CORD-19 dataset for generating a summary and its performance is finally evaluated by comparing it with a few of the related state-of-the-art methods.
Information and Communication Technologies in the Training of Students Majoring in Applied Mathematics and Computer Science

The purpose of the work is to study various aspects of using information and communication technologies to prepare students studying in the direction of training “Applied Mathematics and Informatics.” The educational objectives, principles, and advantages of using ICT in higher education are highlighted. Based on the analysis of educational standards in this area, the goals, objectives, principles, functions of ICT in teaching stochastics are determined. The conclusion is made about a consistent, expedient combination of traditional educational technologies and ICT in the educational process. It is noted that a modern classical university's information and educational environment creates conditions for the training of specialists who meet these requirements.
Usable Tourism Application: Malaysia Attraction Travel Application (MATA)

Technology has changed the way we live and interact through the rapid adoption of global smartphones and smart devices. This new technology has changed the various ways in which industries including the tourism industry works to remain beneficial and innovative. However, current travel planning applications has the same functionalities and outdated travel information that affects tourists who may have difficulty in planning their tourist vacations. To provide users with innovative way to schedule their trip and get basic attraction information in Malaysia, this work will propose a design of an Android-based application with an added innovation on top of current solutions that incorporate the use of a recommender system and Augmented Reality (AR). This work would address the issues of current applications which include low information maintenance and real time update, it will then offer useful recommendations based on user preferences for attraction sites.
Scoring Cyber Vulnerabilities based on Their Impact on Organizational Goals

Vulnerability Management, which is a vital part of risk and resiliency management efforts, is a continuous process of identifying, classifying, prioritizing, and removing vulnerabilities on devices that are likely to be used by attackers to compromise a network component. For effective and efficient vulnerability management, which requires extensive resources– such as time and personnel, vulnerabilities should be prioritized based on their criticality. One of the most common methods to prioritize vulnerabilities is the Common Vulnerability Scoring System (CVSS). However, in its severity score, the National Institute of Standards and Technology (NIST) only provides the base metric values that include exploitability and impact information for the known vulnerabilities and acknowledges the importance of temporal and environmental characteristics to have a more accurate vulnerability assessment. There is no established method to conduct the integration of these metrics. In this study, we created a testbed to assess the vulnerabilities by considering the functional dependencies between vulnerable assets, other assets, and business processes. The experiment results revealed that a vulnerability’s severity significantly changes from its CVSS base score when the vulnerable asset’s characteristics and role inside the organization are considered.
Coherently Organized Digital Exercises and Expositions

We describe an organizing principle for online learning materials; we term coherently organized digital exercises and expositions. Larger in scale than individual lessons but smaller than a programme of study, this innovation in instructional practice is increasingly guiding our thinking in the development of university mathematics courses. Essentially we have taken the book and put it inside automatically assessed online quizzes. In doing this, we embrace the potential provided by new technology to implement evidence-based practices such as spaced retrieval practice. This paper discusses details of this innovation, and how we have implemented it. On the basis of these experiences, we believe this innovation has the potential to change the model of education for university mathematics courses in substantial and non-trivial ways.
Transfer Learning approach for analysis of epochs on Handwritten Digit Classification

In the broader area of machine learning, deep learning depicts a dramatic twist in enhancing the level of intelligence in the various machines. There is a plethora of parameters like weights, bias, number of hidden layers, activation function, and hyperparameters that are utilized for measuring the accuracy of a training model. The prominent form of hyperparameters that are essential for the training process and extracting information for taking decisions is known as Epochs. The enhancement of results is obtained by practicing on the pre-trained network architecture that includes GPU-based computation in the TPU chips. The paper uses the ResNet50 architecture network to train the ultra-deep neural networks for implementing multiclass image classification that is based on Transfer Learning uses a various number of hidden layers to classify handwritten digits and accuracies at different classes is judged by epochs. The tradition of the MNIST database dataset is that it classifies the image into 10 classes. As the image dataset in the NIST is of type bilevel and that was normalized in dimensions to fit in a 224x224 pixel box for better performance. The normalization algorithm uses the anti-aliasing technique to obtain the resultant grey images for the best recognition rate of 99%. As in Traditional machine learning approaches such as KNN, DNN, and many more that take a prolonged time for training the dataset while transfer learning provides the accuracy of 99%, and that was achieved in a few epochs.
Memory-Assistant Collaborative Language Understanding for Artificial Intelligence of Things

Artificial intelligence shows promising efforts in collaborating the language models with the artificial intelligence of things (AIoT), promoting the edging intelligence on natural language understanding. To adapt to the limited computational resources in AIoT, the large language models (e.g., transformer) are compressed into light-weight models, which always results in poor feature representation and unsatisfactory performance on downstream tasks, especially on those low-resource language understanding tasks. To address the above issues, we propose a method named memory-assistant multi-task learning (MAMT), where an auxiliary memory module is introduced to promote multitask learning (MT), which serves as a surrogate of target domain representation and performs instance-level weighted MT. More importantly, our MAMT module is in a plug-and-play fashion. Thus, researchers can plug in it to conduct collaborative training and plug it out for AIoT model inference without extra computation burdens. Experiments demonstrate that MAMT significantly improves the performance of light-weight transformer models and show its superiority over the state-of-the-arts on eight GLUE subtasks.
On Training Sketch Recognizers for New Domains

Sketch recognition algorithms are engineered and evaluated using publicly available datasets contributed by the sketch recognition community over the years. While existing datasets contain sketches of a limited set of generic objects, each new domain inevitably requires collecting new data for training domain specific recognizers. This gives rise to two fundamental concerns: First, will the data collection protocol yield ecologically valid data? Second, will the amount of collected data suffice to train sufficiently accurate classifiers? In this paper, we draw attention to these two concerns. We show that the ecological validity of the data collection protocol and the ability to accommodate small datasets are significant factors impacting recognizer accuracy in realistic scenarios. More specifically, using sketch-based gaming as a use case, we show that deep learning methods, as well as more traditional methods, suffer significantly from dataset shift. Furthermore, we demonstrate that in realistic scenarios where data is scarce and expensive, standard measures taken for adapting deep learners to small datasets fall short of comparing favorably with alternatives. Although transfer learning, and extensive data augmentation help deep learners, they still perform significantly worse compared to standard setups (e.g., SVMs and GBMs with standard feature representations). We pose learning from small datasets as a key problem for the deep sketch recognition field, one which has been ignored in the bulk of the existing literature.
An Overview of Cybersecurity, Data Size and Cloud Computing in light of Saudi Arabia 2030 Vision

Cybersecurity is a major topic that is essential to be considered nowadays and the growth dependence on data and technology the higher the risks are associated with it. This paper briefly introduces cybersecurity and its impact on data size, cybersecurity governance. It highlights cybersecurity in Saudi Arabia and how does it achieve its 2030 vision. Finally, the paper describes cloud computing security and its limitations.