Reviewerly/exHarmony huggingface dataset:
Review of Deep Learning and Interpretability

Deep learning has revolutionized machine learning. Deep convolution networks play a pivotal role in image classification. A conventional image classification technique requires extensive feature engineering; this approach is exhaustive as rigorous manual efforts are needed. Deep learning automatically detects the feature set required for classification. It uses backpropagation to achieve this. Deep learning is being used in a variety of applications including healthcare. The use of deep learning specifically in cancer biology is significant. For Pathologists, classifying tumors as benign or malignant is a complicated task. Classification of malignant lymphoma is challenging as features are not uniform across whole slides, and there are various patterns for classification of cancer. Deep learning is ideal for such a scenario where automated feature learning is required further deep learning detects features that are invariant to the location in an image. Tumor region-wise classification of a slide can also be achieved which can be further subjected to recurrent neural networks. Textual explanations of cancer regions can be generated, providing further insights into diagnosis. Here, three different cancer datasets are reviewed and subjected to deep learning methods. Explainable AI approaches can be applied to decision trees as well as on gradient boosted trees to obtain explanations.
A Novel Artificial Intelligence Technique for Cloud Computing Using a New Heuristic Initialisation and PSO-Parallel Execution

When talking about cloud computing, task scheduling optimisation comes into action as one of the biggest challenges facing any potential solutions. On the other hand, most used optimisation algorithms in task scheduling require considerable execution time to compute some evaluation value. This paper focuses on task scheduling optimisation, especially makespan optimisation. Additionally focuses on minimising the execution time of the implemented algorithm. A novel task scheduler based on Heuristic Initialization and Parallel execution using the Particle Swarm Optimisation (HIP-PSO) proposed. The experimental results based on a cloud simulator (CloudSim) show that the proposed HIP-PSO outperform the compared PSO implementation in terms of the makespan metric. Show also the effect of reducing the execution time of the PSO algorithm in the global execution time.
Experience in organizing the cadets’ practical training by means of the discipline “Foreign language”

Nowadays practice-oriented learning in higher education is fixed as a legal norm in Russian legal system. The legislator emphasizes the importance of developing students practical skills while they are at the higher school. Primarily it deals with specialized subjects of educational programs. Nevertheless, general studies, which include a foreign language, should also cover specialists/bachelors practical training.&#x0D; There is a need to determine the possibilities of organizing practical (professionally-oriented) language training for students of educational organizations of higher education. We see the improvement of the practical component of the discipline Foreign Language in the possibilities of interdisciplinary integration of the course material. This task is solved by selecting typical situations of business communication, clarifying the terminology necessary for elementary foreign language communication, and determining the appropriate teaching forms, methods and techniques.&#x0D; The purpose of the paper is to present the experience of organizing the cadets practical training within higher education settings of the Federal Penitentiary Service in foreign language classes. The proposed materials are developed with the specifics of the program being mastered in mind.&#x0D; The research is based on systematic and functional, activity, competency-based and humanistic approaches. The normative documents study and the generalization of the higher learning mass pedagogical experience made it possible to systematize and plan one lesson in this course.&#x0D; Methodological foundations, empirical and theoretical research methods allowed us to determine the logic of in-class learning, a set of training tasks, to specify the forms of the cadets classroom activities (shift groups working) and the principles of evaluating the learners success.&#x0D; The proposed version of the lesson allows you to activate the mechanisms of cadets professional self-identification, promotes the formation of their personal and professionally significant qualities, helps to master legal terminology and apply it in oral communication, as well as to get an idea of the similarities and differences between the legal systems of Russia and the countries of the studied language.&#x0D; The author also formulated a number of problems related to the cadets practical training by means of a foreign language.
Image Resizing in DCT Domain

There is a high demand for effective resizing of images in order to preserve the region of interest as much as possible on various screens with different dimensions and aspect ratios. In addition, image compression is important in many applications. This paper presents a compressed domain image-resizing algorithm by converting the discrete cosine transform (DCT) blocks of the original image into the DCT blocks of the resized one. Experimental results show that the proposed method outperforms other existing algorithms.
Machine Learning Based Techniques for Detection of Renal Calculi in Ultrasound Images

The Ultrasound imaging is a non-invasive procedural technique which is used for detection of kidney diseases in the medical/clinical practice. This work emphasizes on different preprocessing methods to remove the speckle noise, embedded in kidney Ultrasound images. Preprocessing filters like, adaptive median and wiener are applied to both normal and renal calculi US images, evaluated for noise variance ranging from 0.01 and 0.08 against the parameters like Signal to Noise Ratio (SNR), Peak Signal to Noise Ratio (PSNR), Root Mean Square error (RMSE), Mean Squared Error (MAE), to determine the optimum noise variance value to be considered in preprocessing the Ultrasound images. The work also recommends adaptive median filter applied for kidney Ultrasound images with experimental results indicates increase in Peak Signal to Noise Ratio up to 33.51 dB, compared to Weiner filter. The next step is to select the best classifiers like Support Vector Machine with kernels, Multi-Layer Perceptron to preprocessed Ultrasound kidney images to estimate the accuracy, Recall, F1 score and precision. The experimental results obtained by Support Vector machine with poly kernel reaches an accuracy of 81.1% and are compared with results obtained from similar works.
Counterfactual Generative Smoothing for Imbalanced Natural Language Classification

Classification datasets are often biased in observations, leaving onlya few observations for minority classes. Our key contribution is de-tecting and reducing Under-represented (U-) and Over-represented(O-) artifacts from dataset imbalance, by proposing a Counterfac-tual Generative Smoothing approach on both feature-space anddata-space, namely CGS_f and CGS_d. Our technical contribution issmoothing majority and minority observations, by sampling a ma-jority seed and transferring to minority. Our proposed approachesnot only outperform state-of-the-arts in both synthetic and real-lifedatasets, they effectively reduce both artifact types.
Decision Tree as Neural Network

We show that given a dataset and a decision-tree classifier for the dataset, we can construct a neural network from the decision-tree such that the neural network classifies the data points exactly as the decision-tree does. This provides a point of view to partially explain neural networks.
Based on Advanced Connected Domain and Contour Filter for CASA

Computer-assisted sperm analysis (CASA) is a method that can help the doctor detect sperm efficiently. It can reduce patients' waiting time and pain while the patient is surgering for obtaining sperm. In this paper, a CASA system is developed for finding sperms on the microscopic image, which can raise the detection speed and increase the success rate of finding sperms. A method is proposed which combines advanced connected domain algorithm and contour filter to improve CASA performance. The advanced connected domain algorithm uses 8-connected domains to improve the ability which can detect small targets from the background. Sperms can be distinguished and positioned from lots of small objects by the contour filter. The experiment result shows that our method outperforms traditional human eye recognition and can produce promising results for detecting sperm mixed in the blood.
THE DEVELOPMENT OF THE EDUCATION SYSTEM IN THE CONTEXT OF GLOBALIZATION

The article is devoted to the development of the education system in the context of globalization of the economy, socio-economic relations and the introduction of the distance education, telecommunication, communication technologies. It presents aspects of the knowledge management and the formation of educational complexes as institutional conditions and social environment for the development of educational systems.
Deep Learning Photograph Caption Generator

Generating caption for an image is challenging problem present in artificial intelligence. For a given photograph text, descriptions must be generated. So we need to have computer vision method for understanding the exact content present inside image and also natural language processing field to turn image understanding into proper words in right sequence. Methods of deep learning have successfully demonstrated the ability to solve this problem statement. The advantage of deep learning methods is that it is possible to define an end-to-end model for predicting the caption whenever picture is given, rather than needing a pipeline of specifically designed models or sophisticated data preparation. Our paper focuses on preparing the text and image data for training our deep learning model, we need to design and also train our deep learning caption generation model, and we need to evaluate the trained caption generation model, using it to caption entirely new photographs. Finally, we are hosting a webserver to which the user can upload images and fetch the generated captions.
A Smart Flight Controller based on Reinforcement Learning for Unmanned Aerial Vehicle (UAV)

Traditional flight controllers consist of Proportional Integral Derivates (PID), that although have dominant stability control but required high human interventions. In this study, a smart flight controller is developed for controlling UAVs which produces operator less mechanisms for flight controllers. It uses a neural network that has been trained using reinforcement learning techniques. Engineered with a variety of actuators (pitch, yaw, roll, and speed), the next-generation flight controller is directly trained to control its own decisions in flight. It also optimizes learning algorithms different from the traditional Actor and Critic networks. The agent gets state information from the environment and calculates the reward function depending on the sensors data from the environment. The agent then receives the observations to identify the state and reward functions and the agent activates the algorithm to perform actions. It shows the performance of a trained neural network consisting of a reward function in both simulation and real-time UAV control. Experimental results show that it can respond with relative precision. Using the same framework shows that UAVs can reliably hover in the air, even under adverse initialization conditions with obstacles. Reward functions computed during the flight for 2500, 5000, 7500 and 10000 episodes between the normalized values 0 and −4000. The computation time observed during each episode is 15 micro sec.
Multi Scale Feature Fusion Crowd Density Estimation Technology Based on Residual Network

The multi-scale problem caused by the camera perspective is different from person to person. To achieve the objective, a multi array scale fusion crowd density estimation method based on residual network is offered in this research. The improved ResNet network is adopted in the front end of the network to extract the crowd features, and the output feature map is compressed into 1/8 of the original map to improve the accuracy of the prediction density map. The back-end uses three array dilated convolution modules with different dilated ratio to acquire the multi-scale features of the crowd, so that the network can obtain more scale details and edge information. At the end of the network, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1\times 1$</tex> convolution pairs are cascaded to get higher quality predictive density map. Test the network with datasets Shanghaitech and UCF_CC_50 respectively, and the performances are more excellent to the current mainstream crowd density estimation methods. The MAE value is 61.2% higher than that of MCNN network and 6.4% higher than that of SANET network.
Chaos-Based Cryptographic Mechanism for Smart Healthcare IoT Systems

Smart and interconnected devices can generate meaningful patient data and exchange it automatically without any human intervention in order to realize the Internet of Things (IoT) in healthcare (HIoT).Due to more and more online security and data hijacking attacks, the confidentiality, integrity and availability of data are considered serious issues in HIoT applications.In this regard, lightweight block ciphers (LBCs) are promising in resourceconstrained environment where security is the primary consideration.The prevalent challenge while designing an LBC for the HIoT environment is how to ascertain platform performance, cost, and security.Most of the existing LBCs primarily focus on text data or grayscale images.The main focus of this paper is about securing color images in a cost-effective way.We emphasis high confidentiality of color images captured by cameras in resource-constrained smartphones, and high confidentiality of sensitive images transmitted by low-power sensors in IoT systems.In order to reduce computational complexity and simulation time, the proposed Lightweight Symmetric Block Cipher (LSBC) exploits chaos-based confusion-diffusion operations at the inter-block level using a single round.The strength of LSBC is assessed by cryptanalysis, while it is ranked by comparing it to other privacy-preserving schemes.Our results show that the proposed cipher produces promising results in terms of key sensitivity and differential attacks, which proves that our LSBC is a good candidate for image security in HIoT.
Human Temporality: Qualitative Description

The present chapter is devoted to a qualitative description of the human temporality and the complex present as its constituent component. Special attention is focused on action strategies representing intentional, goal-oriented actions. The concept of space-time clouds is elaborated in detail in order to describe the mental representation of perceived physical objects as well as intentional actions in mathematical terms. This concept takes into account intrinsic uncertainty in human perception of physical objects and quantitative evaluation of properties attributed to the corresponding mental images. The human temporality is introduced as a specific mathematical object describing human behavior and comprising past-oriented mental time travels, the complex present, and future-oriented mental time travels. It is elucidated that the complex present can be regarded as the minimal fragment of human temporal dimension within which intentional, goal-oriented actions can admit a closed mathematical description.
Automated graph representation learning: a survey

Research on Graph Representation Learning has seen many innovations in the recent years given the close resemblance of real-world data to graphs and its already wide usage. Graph data are considerably more difficult to analyse than traditional Euclidean data structures, which is the gap that Graph Representation Learning aims to cater to. However, automating Graph Representation Learning is a research area that is only starting to gain the focus it deserves. Most of the work that has been made available is not adequately documented or clear in their approach. It would be greatly beneficial for such work to be thoroughly analysed and evaluated under a common criterion. This paper introduces a novel automated Graph Representation Learning workflow that is utilized by an ongoing research project called AutoGRL which is built by the authors. It provides a standardized and modular production-ready platform for automated Graph Representation Learning with a level of autonomy and performance that has not been discovered. This paper also delivers an analysis of the existing work in the automated Graph Representation Learning domain, the benefits and limitations of the existing work, including comparisons of features along with new and unique research directions that are currently under-explored in the field.
POT: A Dataset of Panoramic Object Tracking

Object tracking in spherical panoramic videos is an important yet less studied problem. Besides the common challenges in planar video tracking, panoramic video tracking should address the issues of image distortion and split caused by the sphere-to-plane projection. In this paper, we build a panoramic object tracking dataset, namely POT, containing 40 fully annotated spherical panoramic videos, which are annotated on the pixel level. Moreover, we propose a simple yet effective panoramic tracking framework based on local field-of-view projection, which considers the spherical geometry explicitly and performs object tracking on the tangent plane. Our framework can be easily integrated with well-studied planar video trackers. Besides, we boost five mainstream trackers with our framework. Comprehensive experimental results on the POT dataset demonstrate that the proposed framework is well behaved in panoramic object tracking.
Paradigm Shift from 1G to 5G Technology: A Comparative Study

Now-a-day technology is evolving on a large scale. Primarily when the technology of mobile phones came into existence, a wireless technology came into being. This was the 1G technology that was trending. Further as the need for technology started increasing, 1G(first generation) technology got evolved to 2G(second generation), later on 3G,4G as per the need and now currently to 5G technology which makes use of lifii.e light fidelity which uses light as a source of transmitting data from source to destination. Mobile wireless technology is growing from one generation to other in terms of data rate, bandwidth, internet services, and with many more factors. This paper contains the information about how the transition took place from 1G to 5G network over time. Also the paper contains the reasons of why this transition took place, the importance of each generation and a few use cases of those generations.
Facilitating Five-Dimensional Cultivation through “Wall Culture”: A Case Study of Primary School A in Guangdong Province

To cultivate the socialist builders and successors with all-round development of morality, intelligence, physique, aesthetics and labor is a key aim of fundamental education in China. As an important part of school culture, wall culture should realize its invisible cultivation functions in the following aspects: to focus on the essence of the school’s culture; to help teachers and students establish a consciousness of all-round development; to facilitate the educational activities targeting students’ all-round development. Empirical analysis identifies several practical barriers hindering the functions of wall culture: emphasizing more design over refinement, emphasizing more publicity over education, and emphasizing more adults over children. The wall culture should be rooted in the school’s operational features, teaching activities and student activities to realize its function of promoting students’ all-round development.
Introduction to the Special Section on Research challenges and directions of Data Mining in Edge Computing systems (VSI-dmec)

On a daily basis, people perform planned or routine activities related to their needs, such as going to the office, playing sports and so on. Alongside them, unpleasant unforeseen situations can take place such as being robbed on the street or even being taken hostage. Providing information related to the crime scene or requesting help from the competent authorities is difficult. That is why, mechanisms to support users in such situations, based on their physical status, would be of great importance. Based on such idea, a context-aware model for detecting specific situations of danger is proposed. It is characterized by a set of defined features related to the body posture, the stress level and geolocation whose values are gathered through a smartphone and a smartwatch, as enabling technologies for the local computation. A machine learning technique was adopted for supporting body posture recognition, whereas a threshold-based approach was used to detect the stress level and to evaluate of user’s location. After the description of the proposed model, the achieved results as well as current limits are also discussed.
Impact of Service Quality on Customer’s Satisfaction in the Airline Industry (A case study of PIA, Oman Airline and Air-Arabia) -

Objective: The research study aimed to investigate the prevailing competitive environment in the airline industry.By comparing the compare service quality of threedifferent airlines (PIA, Oman Airline, and Air Arabia) using the SERVPERF model.Background: Observing the competitive landmark of the airline industry, the airlines concentrate on the need to analyze the customers’ demands and provide services according to their demands. Method: A total of 150 customers were selected randomly to collect the data through self-administered questionnaire. The statistical reliability and validity of each questionnaire were tested through Cronbach’s Alpha and factor analysis with positive responses. The research applied ANOVA, correlation, and regression to predict the results.Results: The results declared that PIA shows significant means for empathy while insignificant for tangibility, reliability, responsiveness, and assurance. Oman Airline showed a significant mean for assurance dimension of service quality while insignificant for tangibility, responsiveness, reliability, and empathy. While for Air Arabia the results declared a significant mean for tangibility, assurance, and insignificant mean for responsiveness, reliability, and empathy. Conclusion: The detailed analysis has proved that service quality in the form of tangibility, assurance, responsiveness, reliability, and empathy is the most important contributor to the satisfaction of customers of the Airline Industry.
Chapter 2. Digital Literacy and Digital Legacy

Technology is an ever-present factor for students, as they are immersed in a world of digital influences. The foundation of digital literacy has four factors—technological skills and access, authorship rules, representation rules, and online social responsibility. For students and employees to interact responsibly in a digital society, it’s imperative that they understand all four parts of the puzzle. It is suggested that by 2020 77 percent of jobs will require digital competency (Lynch 2017).