Reviewerly/exHarmony huggingface dataset:
Evaluation of Textural Degradation in Compressed Medical and Biometric Images by Analyzing Image Texture Features and Edges

The importance of image compression is now essential during transmission or storage processes in various data applications, especially in medical and biometric systems. To perform the effectiveness of the compression process on images and evaluate degradation caused by this process, image quality assessment becomes an important tool in image services. We note that the objective criteria in image quality depend especially on the image type and image texture composition. The actual tendency is to find metrics making better qualification on errors in compressed images and correlate with the human visual system. This paper presents an investigation to examine and evaluate image compression degradation by the use of a new tendency concept of image quality assessment based on texture and edge analysis. To perform and practice this evaluation, we compress the medical and biometric images using second-generation wavelet compression algorithms and study the degradation of texture information in these images.
Generating instant trajectory of an indoor UAV with respect to its dynamics

The application of autonomous indoor UAVs indicates the need for a deeper examination of the trajectory generation. This is due to the fact that the same UAV dynamics are used in more confined space, greatly decreasing the space for maneuvering. The result of path planning methods usually represents a finite set of points with a certain spacing between them. To optimize the system, an effort is made to maximize these distances, while maintaining the shape of the path. To obtain a trajectory suitable for UAV control, it is necessary to define the path between these points analytically. The global trajectory (the whole flight or a significant part of it) can be generated by interpolation or regression of known points. The generation of instant trajectory takes into account the output of reactive navigation and other control systems. The outputs of these systems might provide position targets with large dynamic and directional range over short distances. In our task, we have chosen an approach to generate an instant trajectory via Hermite's spline. With this approach we can generate an instant trajectory from the current state. During generation the transitions between points are continuous even without knowing the previous states, using only the prescribed dynamics at a given point. Therefore, we know the desired position and velocity of the UAV at all times, preventing sharp movements done by a sudden jump of desired position. The result of the task in this paper is the testing and verification of the Hermite's spline on a real autonomous UAV, which is currently being developed in collaboration with the Institute of Robotics and Cybernetics at the Slovak University of Technology in Bratislava.
An Ordinal Approach to Modeling and Visualizing Phishing Susceptibility

Phishing is a significant, ongoing cybersecurity threat facing both individuals and organizations, and the consequences of falling victim to phishing can be dire, particularly in terms of financial loss. While much research has focused on understanding or predicting user susceptibility to phishing with the aim of preventing it, little of this research has focused on modeling the process of being phished holistically. Specifically, while the outcome of interacting with a phishing website may be thought of as binary (e.g., "Were you phished or not?"), the actual process typically involves a sequence of stages spanning from the choice to visit (or not visit) a link all the way to transacting with a website and giving away valuable personal or financial information. To better understand the variables that influence phishing website traversal, we conducted a controlled lab experiment with a large sample of 908 participants. In this experiment, each participant was given a task such as opening an online checking account and presented with a series of simulated search results that included both legitimate and phishing websites. By monitoring participants' interactions with these websites and collecting additional information via surveys, we evaluated which variables were the most likely to result in behavior that could lead to greater phishing exposure using a multi-model comparison approach. The results of our analyses shed light on the key variables that can lead to a greater propensity for being phished and may prove invaluable to researchers interested in designing new interventions.
Effect of vocal cord polyp on Mandarin tones recognition by native Chinese speakers

Intelligent Diagnosis for pathological voice contains two parts. One is intelligent detection, and the other is intelligent comprehension. Before the application of intelligent comprehension, it is important for us to know how human perceive pathological voice. This paper first investigated acoustically a patient who had vocal cord polyp read Chinese characters with Mandarin tones before and after the surgeries. Second, identification test was used to find out the effect of vocal cord polyp on Mandarin tones recognition. The results show that first, the effect of vocal cord polyp on Tone 1 and Tone 3 in F0 contour are statistically significant, but not Tone 2 and Tone 4. Second, vocal cord polyp does not affect identification of Mandarin tone types. Third, vocal cord polyp affects Tone 1 and Tone 2 in identification rate significantly, but not Tone 3 and Tone 4. It is concluded that vocal cord polyp has little influence on the intelligibility of Mandarin tone types. And our research results are referential to intelligent diagnosis for pathological voice.
A Time-Based Dynamic Operation Model for Webpage Steganography Methods

The webpage steganography technique has been used for a covert communication method for various purposes in which a sender embeds a secret message into a plain webpage file like an HTML file by using various steganography methods. With human eyes, it is very difficult to distinguish between the original webpage (cover webpage) and the modified webpage with the secret data (stego webpage) because both are displayed alike in a web browser. In this approach, when two communicating entities want to share a secret message, a sender uploads a stego webpage to a web server or modifies an existing webpage in the web server by using a webpage steganography method, and then a receiver accesses the stego webpage to download and extract the embedded secret data from it. Meanwhile, according to our extensive survey, we observed that most webpage steganography methods focused on proposing or improving steganography algorithms but did not well address how to operate a stego webpage as time passes. However, if a stego webpage is used in a static way such that the stego webpage does not change and is constantly exposed to web clients until the sender removes it, such a static operation approach will limit or badly affect the hiding capacity and undetectability of a webpage steganography method. By this motivation, in this paper, we proposed a time-based dynamic operation model (TDOM) that improves the performance of existing webpage steganography methods in terms of hiding capacity and undetectability by dynamically replacing the stego webpage with other stego webpages or the original webpage. In addition, we designed two time-based dynamic operation algorithms (TDOA-C and TDOA-U), which improve the hiding capacity of existing methods and TDOA-U for improving the undetectability of existing methods, respectively. To validate our model and show the performance of our proposed algorithms, we conducted extensive comparative experiments and numerical analysis by implementing two webpage steganography methods with our TDOM (CCL with TDOA-C and COA with TDOA-C) and tested them in the web environment. According to our experiments and analysis, our proposed algorithms could significantly improve the hiding capacity and undetectability of two existing webpage steganography methods.
Fast automatic multi-defects recognition based on the spatial characteristics of Freeman chain code

Shearography is a very effective non-destructive testing method for detecting the internal defects of composite materials. How to improve the detection efficiency of this technique to help achieve the rapid and automatic recognition of multiple defects has always been a hot research topic for researchers. Based on the results of the shearography, we propose a method to identify multiple defects in the phase map using the spatial characteristics of Freeman chain code. First of all, we use Canny operator to binarize the phase map, then refine the fringe by morphological means, and finally identify the defects by the spatial characteristics chain code. In addition, Otsu is used to assist in judging the fracture or incomplete defects. For the fringes under vacuum loading or thermal loading, this method can detect the defects quickly and successfully. The theoretical description is given and the effectiveness and practicability of the method are verified by experiments.
The Characteristics and Impact of Uncompilable Code Changes on Software Quality Evolution

Software repositories allow multiple developers to iteratively contribute commits, with the intention of improving the system. However, commits can negatively impact software quality, or even cause the software to become uncompilable. Recent studies show that uncompilable commits exist even in high-profile open-source software. Identifying broken code, a potential symptom of careless development, and analyzing how software changes when it becomes uncompilable can shed light on how software quality evolves when developers do not follow best practices. Since comprehensive software quality analysis tools are incapable of analyzing uncompilable commits, there is little insight as to what happens and how quality changes when a commit breaks the compilability. In this paper, starting from an analysis of the software quality metric changes that happen when the project become uncompilable, we explore the purposes of commits and the relations between commit type, size and compilability, analyzed across 68 open-source Java repositories.
Handwritten Bangla Character Recognition Using Deep Convolutional Neural Network: Comprehensive Analysis on Three Complete Datasets

Bangla handwritten character recognition is a difficult job compared to other languages due to the morphological complexity of adjacent characters and a wide variety of curvatures in writing styles people have. Another reason for that is the unique presence of compound characters. Most of the recent research works conducted in this field standardize Deep Convolutional Neural Network (DCNN) models for delivering the most effective outcomes. This paper proposes a DCNN model to classify all the character classes from three popular databases known as BanglaLekha Isolated, Ekush, and CMATERdb. As for BanglaLekha Isolated, our model achieves 93.446% accuracy on the 50 alphabets category and an overall 91.45% considering the whole dataset. The other two datasets, Ekush and CMATERdb result in 95.05% and 94.17% respectively, where the second one holds 171 classes of compound characters alone and performs 93.259% correctness, which is so far the best for this specific category in this dataset.
AGI Components for Enterprise Management Systems

As a continuation of previous work, a functional architecture of a full-fledged AGI is proposed for working in an enterprise management system. Since the creation of such an AGI will require more than one decade, an approach is proposed that allows combining individual more ready-made blocks from a full-fledged AGI with traditional enterprise management systems. Such systems including one or two AGI blocks and Consciousness are called Lean AGI. For these systems, it is possible to use not full Consciousness, but some its limited industry-specific edition, located in the interval between the minimal and full Consciousness. Such editions are called Lean Consciousness. In the paper, the cases for the possible use of Lean AGI as part of enterprise management systems are shown. For one of these cases, the Lean AGI architecture based on the Goal analytics block is proposed for working on top of traditional enterprise management applications.
A Percentil Gravitational Search Algorithm an Aplication to the Set Covering Problem

The study and understanding of algorithms that solve combinatorial problems based on swarm intelligence continuous metaheuristics, is an area of interest at the level of basic and applied science. This is due to the fact that many of the problems addressed at industrial level are of a combinatorial type and a subset no less than these are of the NP-hard type. In this article, a mechanism of binarization of continuous metaheuristics that uses the concept of the percentile is proposed. This percentile concept is applied to the An Lion optimization algorithm, solving the set covering problem (SCP). Experiments were designed to demonstrate the importance of the percentile concept in the binarization process. Subsequently, the efficiency of the algorithm is verified through reference instances. The results indicate that the binary percentile gravitational search algorithm (BPGSA) obtains adequate results when evaluated with a combinatorial problem such as the SCP.
Investigating the Incorporation of Machine Learning Concepts in Data Structure Education

This Research to Practice Work-In-Progress paper discussed the incorporation of machine learning (ML) concepts in data structure education. The thriving of the ML especially deep learning techniques has led to an increased demand for trained professionals with ML skills to solve challenging engineering problems in many fields. Getting students familiar with ML as early as from CS2 (the data structure course) could benefit them in many aspects, but this direction has not been explored yet. In this paper, we discussed possible ways to integrate the ML concepts into data structure (DS) course. First, after introducing the concept of tensor in DS classroom teaching, we propose a practical experiment to implement the forward propagation of a pretrained convolutional neural network (CNN) aiming at classifying handwritten digits. Second, an experiment of decision tree based classification is set to give students an illuminating context via practicing the usage of tree structure. Finally, we design the experiment of computing graph to help the understanding of Directed Acyclic Graph (DAG), in which the students are required to implement the calculation of a multiple-variable function and its gradient based on DAG. Practicing DS knowledge in interesting ML-related problem contexts would intrigue the study enthusiasm of students and give them a general understanding of the application of DS knowledge in frontier technology, which could benefit the education of both DS and ML-related courses.
Quantum speed limit time in the presence of disturbance

Quantum theory sets a bound on the minimal time it takes for a system to evolve from initial state to target state. This bound is called the quantum speed limit (QSL) time. The quantum speed limit time is used to quantify the maximal speed of the quantum evolution. The quantum evolution will be faster if the quantum speed limit time decreases. In this work, we study the quantum speed limit time for an open quantum system in the presence of disturbance in an environment. We use the model which is provided by Ban [Phys. Rev. A 99, 012116 (2019)]. In this model, two quantum systems [Formula: see text] and [Formula: see text] interact with environment sequentially. At first, quantum system [Formula: see text] interacts with the environment [Formula: see text] as an auxiliary system, then quantum system [Formula: see text] starts its interaction with disturbed environment immediately. In this work, we consider the dephasing coupling with two types of environment that has different spectral density: Ohmic and Lorentzian. We observe that, non-Markovian effects will appear in the dynamics of the second quantum system [Formula: see text] due to the interaction of the first quantum system [Formula: see text] with the environment. Given the fact that the quantum speed limit time reduces due to the non-Markovian feature of quantum evolution, we show that disturbance effects will reduce the quantum speed limit time for the dynamics of the second quantum system [Formula: see text].
Non-negative Sparse Laplacian regularized Latent Multi-view Subspace Clustering

Recently, in the area of artificial intelligence and machine learning, subspace clustering of multi-view data is a research hotspot. The goal is to divide data samples from different sources into different groups. We proposed a new subspace clustering method for multi-view data which termed as Non-negative Sparse Laplacian regularized Latent Multiview Subspace Clustering (NSL2MSC) in this paper. The method proposed in this paper learns the latent space representation of multi view data samples, and performs the data reconstruction on the latent space. The algorithm can cluster data in the latent representation space and use the relationship of different views. However, the traditional representation-based method does not consider the non-linear geometry inside the data, and may lose the local and similar information between the data in the learning process. By using the graph regularization method, we can not only capture the global low dimensional structural features of data, but also fully capture the nonlinear geometric structure information of data. The experimental results show that the proposed method is effective and its performance is better than most of the existing alternatives.
Contextual Reactive Pattern on Chatbot building Platforms

Building a chatbot with an iterative development process poses certain challenges for the chatbot developer. The developer is expected to produce a deployable version of the chatbot at the end of a short development cycle. Every iteration should incrementally increase the capability of the chatbot and implement a subset of overall user stories based upon a priority list, similar to any other project developed using iterative development. In this regard, commercial chatbot-building platforms offer multiple advantages to the chatbot developer, provided that the developer can map these user stories in a particular form. To do so, for every query the chatbot is expected to answer, the developer must evaluate the intention of the user. Based on the intention, the query must be processed differently, which may involve execution of some business logic. In addition, the processing of the query may require specific data items which the user must supply as part of the conversation with the chatbot. Thus, the chatbot is defined by supplying a "context" that it may encounter, and the "reaction" that must take place when the context is observed. In this work, we discuss the effects of using a platform to build a chatbot and discuss the Contextual Reactive pattern used for chatbot definition.
Span Level Model for the Construction of Scientific Knowledge Graph

In the recent years, knowledge graphs (KGs) are getting lots of attention due to their wide applications. Constructing KGs involves two major steps, named entity recognition (NER) and relation extraction (RE). Current approaches for extracting entities and relation use (beginning, outside, inside) BIO/(beginning, inside, last, outside, unit) BILOU based models which faces issues of cascading errors. In this paper, we introduce a novel neural architecture for the construction of scientific KG employing span based methods for extraction of entities, unlike previous models which use BIO/BILOU based approach. We use bidirectional encoder representations from transformers (BERT) word embeddings for this task and neural network classifier for the detection of entities in the span and convolutional neural network (CNN) for extracting relation between the entities. Evaluation results yield the recall, precision and f-measure values of 70.61%, 71.16% and 70.88% respectively for NER, and 54.89%, 50.72% and 52.72% respectively for RE task. Results show that our model outperforms the previous models for the scientific dataset, SciERC, by a margin of 3% in entity extraction task and 7% for the relation extraction task. We achieved these results without raising the overall complexity of the model.
Augmented Reality Application

Augmented reality (AR) is an extension of extended reality that superimposes virtual images onto real world view. It has been implemented across a versatile range of fields including education, entertainment, military, and much more. Unlike virtual reality (VR), AR focuses on enhancing the real-world view and enriching people with a better way to display the learning content in an attractive way. AR provides content simulation and interaction which can display textual data in a more immersive way which can retain learner's concentration for longer periods of time. Technology has always helped people with disabilities. Mentally differently abled children require special attention right from their childhood. Many applications have analysed the challenges faced on a daily basis by the differently abled. Hence, AR-based learning would make their learning much more easier through the personalized and immersive platform. In the given context, this chapter analyses the use of AR in education and developed an AR-learning platform for mentally differently abled based on Unity3D and Vuforia.
PHM Technology for Memory Anomalies in Cloud Computing for IaaS

The IaaS (Infrastructure as a Service) is one of the most popular services from todays cloud service providers, where the virtual machines (VM) are rented by users who can deploy any program they want in the VMs to make their own websites or use as their remote desktops. However, this poses a major challenge for cloud IaaS providers who cannot control the software programs that users develop, install or download on their rented VMs. Those programs may not be well developed with various bugs or even downloaded/installed together with virus, which often make damages to the VMs or infect the cloud platform. To keep the health of a cloud IaaS platform, it is very important to implement the PHM (Prognostics and Health Management) technology for detecting those software problems and self-healing them in an intelligent and timely way. This paper realized a novel PHM technology inspired by biological autonomic nervous system to deal with the memory anomalies of those programs running on the cloud IaaS platform. We first present an innovative autonomic computing technology called Bionic Autonomic Nervous System (BANS) to endow the cloud system with distinctive capabilities of perception, detection, reflection, and learning. Then, we propose a BANS-based Prognostics and Health Management (BPHM) technology to enable the cloud system self-dealing with various memory anomalies. AI-based failure prognostics, immediate self-healing, self-learning ability and self-improvement functions are implemented. Experimental results illustrate that the designed BPHM can automatically and intelligently deal with complex memory anomalies in a real cloud system for IaaS, to keep the system much more reliable and healthier.