Reviewerly/exHarmony huggingface dataset:
Mobile Educational Applications: Remote Usability Study

The self-contained mobile educational applications support the student in the learning process combining technology and pedagogy through the use of mobile devices. The adoption of these applications is closely related to their level of usability. Generally, the usability studies of these applications are carried out through usability metrics or heuristics oriented for generic systems. In this paper, we describe in detail a usability study of two independent commercial mobile educational applications that incorporate interactivity, socialization, and adaptability. The usability study is presented with the intention of evaluating the usability of the applications in terms of the application interface, the educational support functions, and the characteristics of M-Learning. The objective of the usability study allowed us to identify the usability problems present in these types of applications. The application of the usability tests was carried out remotely due to the COVID-19 health contingency period. This usability study can serve as a reference for evaluators to identify and carry out a remote evaluation of these types of applications; as well as the test results could serve as a reference to improve the usability of mobile educational applications.
The Development and Testing of Lightweight Pseudorandom Number Generators

The paper presents the types of random number generators and their disadvantages. The lightweight construction of the physical model of the generator of pseudorandom numbers was reviewed. Tests with the use of multidimensional statistics are presented, which make it possible to better investigate a sequence for randomness, by assessing simultaneously several characteristics of the sequence. The process of testing and optimizing the generator using tests of multidimentional statistics illustrates the suitability of the software package for use and its integral role in creating a quality random number generator, especially for use in IoT devices.
Reconstruction of Airports Coordinates Based on Flight Times Data

We propose a data-centric approach to the issue of the shape of the Earth’s surface. We base the research solely on widely available data on flight times between airports from all over the world. The airport coordinates will be treated as unknown variables to be estimated in such a way that the flight times between the estimated ones match their real counterparts as closely as possible. This leads to the non-linear optimization task that we handle using the BFGS algorithm. The real data are acquired using web scraping. The performance of the proposed method is illustrated on artificial examples: sphere, disc, circle. The results obtained for the real data are presented. Interesting properties of the task came up during the simulation study performed with the BFGS optimization routine.
A Hybrid Reversible Image Watermarking Technique based on Fractal Encryption and Grey Wolf Optimization

To improve robustness and imperceptibility of watermark, a new hybrid image reversible watermarking technique is proposed in the research paper on the basis of fractal encryption and Modified Quadratic Difference Expansion (MQDE). At first, fractal encryption with L shaped tromino theorem is applied to the watermark (secret image) for improving the security of data transmission with limited system complexity. Further, Grey Wolf Optimization (GWO) is used for optimizing the block assignment to imbibe the secret image into cover (host) image that significantly improves the visibility. Finally, MQDE technique is used for improving the embedding rate and visual quality of watermarking. Experimental metrics validate proposed technique is superior to existing models in light of robustness against noise, cropping attacks, rotation and histogram equalization. It also shows satisfactory imperceptibility. The proposed model retrieves the original cover and secret image without losing the essential information and delivers optimum security.
An efficient malicious webpage static detection framework based on optimized Bayesian and hybrid machine learning

Summary Malicious webpage detection is a crucial work in both theory and practical environment. In practical applications, static detection methods are usually regarded as a priority choice, which can quickly detect unknown malicious web pages and avoid a costly in‐depth analysis. However, existing solution of static detection typically has the following problems. For example, a single static detection may lead to a higher false positive rate, and the integrated detection usually has a lower detection efficiency. In this article, we propose an efficient webpage static detection framework, especially considering both the detection efficiency and the detection accuracy. Then, on the basis of the extended feature sets from URL, HTML, and JavaScript, we introduce an optimized naive Bayesian algorithm, in which a novel amplification factor strategy is proposed. Finally, a webpage threat assessment model oriented to general machine learning is presented to achieve the refined detection. Three main properties are provided: high detection efficiency, high detection accuracy, and better applicability. Furthermore, the comprehensive experimental results and comparative analysis is given to show the advantages of the proposed framework.
The Mythical Lines of Code Metric

Using the lines of code (LOC) metric in software project management can be a financial moral hazard to an organization. This is especially true for upper management who handles an organizational budget and strategic plan. Software project managers have their own budgets. However, if they fail to meet the budget, the organization's cash flow, rather than the project manager's personal cash flow, will suffer. This chapter will discuss the practice of software project management, the field of software metrics, game theory, and the game theory issue of moral hazard. It will demonstrate why using LOC as a metric can present a moral hazard to senior management and an organization.
Record Types

Record types are a simple way of recording small groups of values. You define a set of names and corresponding types; then you can create, compare, and amend instances of these groupings with some extremely simple syntax. But behind this simplicity lies some powerful and well-thought-out functionality. Learn to wield record types effectively and you’ll be well on the way to becoming an expert F# developer. It’s also worth knowing when not to use record types and what the alternatives are in these circumstances. We’ll cover both explicitly declared named record types and also implicitly declared anonymous record types.
On Constructing Vessel Dataset Structure Using GAN-based Data Augmentation

Conventional methods using classical image processing techniques is to limitedly augment basic data for extension of the dataset volume in deep learning network system. A new proposed approach using synthetic data by 3D virtual model and Generative Adversarial Networks (GAN) is able to resolve the lack of dataset adequately, and the performance of the dataset structure can be verified by a classification network model. The single and combined data groups with various types of images were constructed for the accuracy comparison of classification system, and it indicated that the proposal has an appropriate profit for improvement of the system. The composed dataset using data augmentation methods can be applied on both academic and industrial field which have little actual data for deep leaning network systems. Further work will aim to improve the quality of the data from GAN and find the relevant quantity of dataset according to data type.
Research on Fines of Dangerous Driving Crime Based on Random Forest and Deep Learning

Fine analysis is a typical problem in criminal crimes, and it is of great significance for the judgment of similar cases and the recommendation of litigation lawyers. This paper focused on the dangerous driving crime, extracted the key features from the semi-structured judgment document data (27543 articles in total), used keywords matching to identify whether there was a fine in the document, and made a preliminary analysis on whether the dangerous driving crime was sentenced to a fine by using Decision Tree and Random Forest model. The accuracy of classification was 75.23 % and 74.92% respectively. The ranking of feature importance showed that “Blood alcohol content” and “Previous Conviction or not” were important influencing factors; TextCNN, DPCNN and TextRNN were used to study whether to impose a fine. It is verified that TextCNN had the best prediction effect and the accuracy was 92%; The accuracy of DPCNN and TextRNN were 91 % and 88% respectively. The three models performed well in this task and had high reference value.
Fully Convolutional Neural Network with Relation Aware Context Information for Image Parsing

Image parsing is among the core tasks in the field of computer vision. The automatic pixel-wise segmentation offers great potential in terms of application adaptability. Traditional convolutional networks have produced better segmentation maps however the research is continued for integration of context information with neural network approaches. In this paper, we propose an image parsing framework that explores the traditional convolutions in fully convolutional networks and learns rich semantic contextual information using the adjacent and spatial modules to generate probability maps. The implicit fusion of the probability maps generated enhances the accuracy of segmentation labels. The proposed framework improves the segmentation accuracy on the CamVid dataset achieving global accuracy of 89.8 %. A comprehensive comparison with state-of-the-art approaches demonstrates that the proposed network exhibits the capability to adapt to the dataset specific information and has the potential to outperform cutting-edge segmentation models.
Generating Device Fingerprints for Smart Device Pairing Using the Unique Spectrum Characteristic From LEDs

Currently, the vast majority of smart devices with LEDs are on the rise. It has been observed that the lights emitted by each LED have unique spectral characteristics. Despite the fact that there are a number of methods out there to generate fingerprints, none seem to explore the possibility of generating fingerprints using this unique feature. In this chapter, the method to perform device fingerprinting using the unique spectrum emitted from the LED lights is discussed. The generated fingerprint is then used in device pairing.
Deep attributed network embedding by mutual information maximization

Abstract There is a growing body of literature that recognizes the importance of network embedding. It intends to encode the graph structure information into a low-dimensional vector for each node in the graph, which benefits the downstream tasks. Most of recent works focus on supervised learning. But they are usually not feasible in real-world datasets owing to the high cost to obtain labels. To address this issue, we design a new unsupervised attributed network embedding method, deep attributed network embedding by mutual information maximization (DMIM). Our method focuses on maximizing mutual information between the hidden representations of the global topological structure and the node attributes, which allows us to obtain the node embedding without manual labeling. To illustrate the effectiveness of our method, we carry out the node classification task using the learned node embeddings. Compared with the state-of-the-art unsupervised methods, our method achieves superior results on various datasets.
Harmony in the Light of Computational Ludics

Prawitz formulated the so-called inversion principle as one of the characteristic features of Gentzen's intuitionistic natural deduction.In the literature on proof-theoretic semantics, this principle is often coupled with another that is called the recovery principle.By adopting the Computational Ludics framework, we reformulate these principles into one and the same condition, which we call the harmony condition.We show that this reformulation allows us to reveal two intuitive ideas standing behind these principles: the idea of "containment" present in the inversion principle, and the idea that the recovery principle is the "converse" of the inversion principle.We also formulate two other conditions in the Computational Ludics framework, and we show that each of them is equivalent to the harmony condition.
A method of adaptive filtering and learning based on label noise

We present a label noise self-filtering based learning method called ”NSFL” for improving generalizability of a classifier in label-noisy data. In this method, label noise is identified from normal samples by iteratively implementing the 2- means on loss values according to their different effects on loss values; and then, the label noise are filtered in a validation process. The NSFL does not rely on a specific loss function,resulting in a good performance in generalizability. Besides,it does not require to optimize any extra parameters of a specific measurement or noise estimation, so it is adaptive. In addition, it is proven that the learning process has the same convergence speed as the used loss function and is consistent with the optimal solution of the noise-free samples. To the best of authors knowledge, this is the first general and adaptive label noise-filtering method. The experimental results on synthetic and real datasets confirm that in comparison with the state-of-the-art methods, the proposed method is more effective in label noisy classification.
Research on Multimedia Social Computer Intelligent Interaction System Based on Virtual Reality VR Technology

From the perspective of multimedia development, integration and differentiation are two aspects of things that promote and promote each other. Promote the continuous advancement of the media and the media through the joint effect of these two aspects. Based on this research background, the thesis first defines a multimedia interactive system. Then based on the virtual reality VR, we introduced the design scheme of the integrated media interactive system based on the social media platform from the perspective of function and safety in detail. Through system detection, the spread of social network worms based on virtual reality VR technology in heterogeneous social networks was discovered, which revealed the influence of network topology and the degree of social network hybridization on the spread of worms, and provided important support for research on social network worms.
Knowledge Management Initiatives in Agile Software Development

Agile software development (ASD) is a knowledge-intensive and collaborative activity and thus Knowledge Management (KM) principals should be applied to improve the productivity of the whole ASD process from the beginning to the end of the phase. The goal is to map the evidence available on existing researches on KM initiatives in ASD in order to identify the state of the art in the area as well as the future research. Therefore, investigation of various aspects such as purposes, types of knowledge, technologies and research type are essential. The authors conducted a systematic review of literature published between 2010 and December 2017 and identified 12 studies that discuss agile requirements engineering. They formulated and applied specific inclusion and exclusion criteria in two distinct rounds to determine the most relevant studies for their research goal. Reuse of knowledge of the team is the perspective that has received more attention.
An Efficient Module Arithmetic Logic Unit in Dual Field for Internet of Things Applications

Modular operation is the most basic operation in asymmetric cryptosystems. This paper proposes an improved modular multiplication algorithm and designs a Module Arithmetic Logic Unit (MALU) with high resource utilization, which realizes efficient dual-field modular operation through the multiplexing of basic circuits and the compression of number selection logic. This MALU is used to implement point multiplication of ECC and synthesized in CMOS 40nm process. Experimental results show that the highest frequency is 488 MHz with area of 101K gates. What’s more, the proposed circuit takes 0.267us and an average of 0.695us to finish 256-bit module multiplication and division respectively in GF(p)/GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ).
An Improved Speech Recognition System Based on Transformer Language Model

With the application of deep learning in speech recognition technology, many neural network-based model structures have emerged in speech recognition, while as another important part of language models, traditional statistical language models have not been further developed due to drawbacks such as oversized parameters, purely based on statistical frequencies, and poor generalization ability. To address those drawbacks, this paper uses a fully convolutional deep neural network-based acoustic model, which is then combined with a connected temporal classification method to replace the traditional statistical language model with a Transformer-based language model. Experiments on several datasets such as THCHS30 show that the recognition effectiveness of the Transformer-based language model speech recognition system is effectively improved.
An efficient intelligent decision method for bionic motion unmanned system

In this article, we propose an efficient intelligent decision method for a bionic motion unmanned system to simulate the formation change during the hunting process of the wolves. Path planning is a burning research focus for the unmanned system to realize the formation change, and some traditional techniques are designed to solve it. The intelligent decision based on evolutionary algorithms is one of the famous path planning approaches. However, time consumption remains to be a problem in the intelligent decisions of the unmanned system. To solve the time-consuming problem, we simplify the multi-objective optimization as the single-objective optimization, which was regarded as a multiple traveling salesman problem in the traditional methods. Besides, we present the improved genetic algorithm instead of evolutionary algorithms to solve the intelligent decision problem. As the unmanned system’s intelligent decision is solved, the bionic motion control, especially collision avoidance when the system moves, should be guaranteed. Accordingly, we project a novel unmanned system bionic motion control of complex nonlinear dynamics. The control method can effectively avoid collision in the process of system motion. Simulation results show that the proposed simplification, improved genetic algorithm, and bionic motion control method are stable and effective.
CNN-based Burst Signal Detection with Covariance Matrix

One of the critical issues in burst signal detection is to design the optimal sensing duration. Conventional energy detection methods generally determine the threshold based on the target detection performance, such as false-alarm probability or detection probability. However, these approaches could not accurately detect the burst signal without prior information. Motivated by this, in this paper, we use a convolutional neural network (CNN) to effectively extract the feature from the data. As a realization of the developed CNN-based detector, we adopt the sample covariance matrix as the input data of a neural network. Finally, we also show the performance of the proposed method for both optimal and non-optimal sensing cases. Particularly, the proposed method could achieve a detection probability of 98.4 % for the non-optimal sensing case at SNR= −15 dB, which significantly outperforms the conventional methods.
Qualitative Study in Natural Language Processing: Text Classification

Recently Natural Language Processing (NLP) has excessive attention due to increased data available online. Although there is huge development in Arabic NLP, but still it is behind English NLP. The aim of this study is to examine the most useful qualitative research design that can be used in the field of Arabic Natural Language Processing in general and in-text classification in specific. Two research designs have been examined in detail; survey and experimental research. Both benefits and drawbacks illustrated for each research design with examples for each method. Thus to have better understanding for some of the research methods. Then deciding which one is best fit to design a qualitative study in this field. Furthermore, this paper suggested framework can be used while researching in the field of NLP. The framework suggested to start with survey or Literature review then follow it with experiment.
Wavelet thresholding techniques implementation in retinal images for suppressing noises

In medical image processing, noise plays a major role of reducing the details of the images and blurs the features which are important for the diagnosis of the disease. Retinal images are high resolution images. Gaussian noise, Salt & Pepper noise affects certain important features of them which are needed for proper diagnosis of the retinal diseases. So removing noises in these images is a kind of difficulty that all researcher experiences. The main objective is to reduce the effect of Gaussian noise and Salt & Pepper noise in retinal images. Recently, wavelet transform is being used in all areas of image processing and especially in medical image denoising, it is highly used because it has multi resolution property and sparsity. So here, wavelet thresholding techniques such as Visu shrink, SURE Shrink and Bayes shrink are implemented to remove Gaussian noise and Salt & Pepper noise. The performance of all the three methods are evaluated using PSNR (Peak Signal to Noise Ratio), MSE (Mean Square Error), NK (Normalized cross correlation) and Time elapsed. Also, the performances of all the thresholding techniques were compared using above mentioned metrics.
Human Behavior Recognition Based on BP Neural Network

Aiming at the problem of human behavior recognition, this paper proposes a human behavior classification algorithm based on radial basis function (BP) neural network. The singular value decomposition (SVD) and principal component analysis (PCA) are used for feature extraction, and the linear discriminant analysis (LDA) is used for feature transformation to make it easier to distinguish between various categories. Finally, BP neural network classifier is used to recognize human behavior. The experimental results show that compared with the nearest neighbor classification and k-nearest neighbor classification (kNN), the proposed algorithm has a higher recognition rate. The result of BP neural network classifier is the best, it can fit the boundary of each category well.
The Trapezoidal Sketch for Frequency Estimation in Network Flow

The sketch is one of the typical and widely-used data structures for estimating the frequencies of items in data streams. However, since the counter sizes in traditional rectangular sketch (r-sketch) are the same, it is hard to achieve small space usage, high capacity (i.e., the maximum frequency can be recorded), and high estimated accuracy simultaneously. Moreover, when considering the high skewness of data streams, this problem will become even worse. Consequently, we propose the trapezoidal sketch (t-sketch) in this paper. In the t-sketch, different from the r-sketch, the counter sizes in different layers are different. Therefore, the low space usage and high capacity can be achieved simultaneously in the t-sketch. Moreover, based on the basic t-sketch, we propose the space-saving t-sketch and the capacity-improvement t-sketch, and analyze the properties of these two t-sketches. Compared with the CM sketch, CU sketch, C sketch, and A sketch, the simulation results show that the performances on space usage, capacity, and estimation accuracy are improved successfully by the space-saving t-sketch and the capacity-improvement t-sketch.