Reviewerly/exHarmony huggingface dataset:
Dictatorial Psychopathological Commination in Server-Side Public Mental &amp; Psychological Menace

Background: Communist formation introduced Marxist’s non-falsifiable anthropocentric theory for ideology and discourse. The form of communication sustaining torture has been noticed in the Chinese indoctrination both in education and mass communication. The research adopted scientific methods in observation to the Marxist structuralism with psychoanalysis of dominance in power-incentivized structuralism. Aim: With the global authoritarian drawbacks and emergence of dictatorship in the structural realist powers, the psychopathology of modern &amp; contemporary cyber-based informatics penetrations with nuclear threat semantics is impacting global mental &amp; psychological health, and especially for the LGBTQIA+ individuals. The research aims to mitigate the harms that transgress the Geneva Conventions and against the spirit of Universal Declaration of Human Rights. Methods: The research adopted a media analysis approach to the sexological elements in anthropological and sociological developments. It focuses on the derivatives of Oedipus Complex with relation to death wish that impact the LGBTQIA+ suicidal ideations in the structured cybercrimes, with a Gestalt psychoanalysis in informatics. The research conceived a somatic method in media psychology counteracting the media-based extension of gender &amp; sexuality castes. Results: The Bell curve of memory were retrieved on the fundamental hypothesis of LGBTQIA+ stigmatization with new combinations in power structure, cosmological, and anthropological consciousness re-combinations. The multilateral dictatorial semantic spread in the cyber-information domains through the monopolization of the Internet Corporation for Assigned Names and Numbers (ICANN) was conceptualized as a structural source of dictatorial psychopathology epidemiology with artificial intelligence (AI). Military cyber psychological warfare spillover in the civil economy is recognized to be the source of global mental &amp; psychological disparities, possibly the contributing factor for LGBTQIA+ suicidal ideation, and derogation of human security
Metacognitive Projections of Linguistic-Literature Didactic Training of Future Primary Teachers in a Digital Environment

The article examines parameters in the implementation of digital training in the field of linguistic-literary didactic discourse. It is included in the educational process of students from pedagogic specialties who study at Trakia University – Stara Zagora, Bulgaria. Descriptive statistics were used to present the identified metacognitive strategies for the realized simulation form – an analogue of the lesson in a real work environment. They can relate to the analyzing, creating and evaluating through testifying to the realized reflection of future elementary teachers. Problems related to goal setting and time distribution of educational activities were registered, as well as when outlining the parameters of an imaginary communicative speech situation. A high level was found in the self-assessment of pedagogical students regarding the implemented simulation form in a digital environment.
Exploration on the Ways to Improve Students' Aesthetic Ability in Higher Vocational Music Education

Aesthetic ability is a special ability formed by human beings under the long-term influence of beautiful things.In the music education courses of vocational colleges, students can appreciate excellent music tracks from around the world, improve their ability to analyze music while enjoying the auditory feast, and establish a correct aesthetic view.This paper first emphasizes the importance of cultivating students' aesthetic abilities in vocational music education, then analyzes the problems existing in current vocational music education, and finally proposes corresponding paths to promote the improvement of aesthetic abilities of vocational students.
A Contextual Information-Augmented Probabilistic Case-Based Reasoning Model for Knowledge Graph Reasoning

Knowledge Graph Reasoning (KGR) is one effective method to improve incompleteness and sparsity problems, which infers new knowledge based on existing knowledge. Although the probabilistic case-based reasoning (CBR) model can predict attributes for an entity and outperform other rule-based and embedding-based methods by gathering reasoning paths from similar entities in KG, it still suffers from some problems such as insufficient graph feature acquisition and omission of contextual relation information. This paper proposes a contextual information-augmented probabilistic CBR model for KGR, namely CICBR. The proposed model frame the reasoning task as the query answering and evaluates the likelihood that a path is valuable at answering a query about the given entity and relation by designing a joint contextual information-obtaining algorithm with entity and relation features. What’s more, to obtain a more fine-grained representation of entity features and relation features, the CICBR introduces Graph Transformer for KG’s representation and learning. Extensive experimental results on various benchmarks prominently demonstrate that the proposed CICBR model can obtain the state-of-the-art results of current CBR-based methods.
Emotion, Age and Gender Prediction Through Masked Face Inpainting

Prediction of gesture and demographic information from the face is complex and challenging, particularly for the masked face. This paper proposes a deep learning-based integrated approach to predict emotion and demographic information for unmasked and masked faces, consisting of four sub-tasks: masked face detection, masked face inpainting, emotion, age, and gender prediction. The masked face detector module provides a binary decision on whether the face mask is available or not by applying pre-trained MobileNetV3. We use the inpainting module based on U-Net embedding with ImageNet weights to remove the face mask and restore the face. We use the convolutional neural networks to predict emotion (e.g., happy, angry). Besides, VGGFace-based transfer learning has been used to predict demographic information (e.g., age, gender). Extensive experiments on five publicly available datasets: AffectNet, UTKFace, FER-2013, CelebA, and MAFA, show the effectiveness of our proposed method to predict emotion and demographic identification through masked face reconstruction.
Towards Addressing Problem-Distribution Drift with Case Discovery

Case-based reasoning (CBR) is a problem-solving and learning methodology that applies records of past experiences, captured as cases, to solve new problems. The performance of CBR depends on retrieving cases relevant to each new problem that the reasoner encounters. In real-world applications, the distribution of problems can change over time, which can cause an issue for the competence and efficiency of CBR systems. This paper proposes addressing this issue through predictive case discovery, which involves predicting cases expected to be useful for future problems to acquire them in advance. It presents an overview of case discovery for problem-distribution drift, including the challenges involved, proposed strategies, and future research directions. It illustrates with a case study evaluating a clustering-based case discovery strategy in a path planning domain across four scenarios: no drift, non-cyclical drift, cyclical drift, and drift from obsolescence.
T-AES- and ECC-Based Secure Data Communication in Peer-to-Peer Networks

Presently, data security is the biggest concern, and every organization wants to secure their data. Data can be shared using peer-to-peer (P2P) networks among multiple nodes; therefore, it became necessary that data should be highly encrypted and latency should be less. Many work in the past has been done on securing the data using advanced encryption standard (AES) and some other methods; however, very less work has been done to secure the AES key. Moreover, the time taken for the encryption is also little higher. The aim of the proposed work is to provide a generic architecture that can be used to share data securely by using threaded advanced encryption standard (T-AES) and elliptic curve cryptography (ECC). In the proposed work, AES is implemented using a thread that increases the efficiency of data encryption up to a greater extent. The performance evaluation of the proposed work reveals that it is the efficient enough to be used to transfer data securely between multiple networks. The total transmission time of T-AES is less in comparison to standard AES and modified AES (MAES) (Arman et al., 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE), IEEE: 191–195, 2020). From the simulation result, it has been found that T-AES took almost half-time for transferring the data in comparison to other algorithms used in the proposed work. The total execution time is reduced by a factor of 1.72, and it is 42% more efficient than AES.
The Influence of Mixed Online to Offline Teaching on the Academic Motivation of Students in Vocational College

With the development of education informatization, the popularization and development of the Internet and the leapfrog development of vocational education, which industry in our country rises suddenly.People pay more and more attention to vocational education, which makes the mixed Online to Offline (OTO)teaching devices become the focus of educators.Mixed teaching, as an innovative teaching devices, makes maximum use of classroom learning time and resources, promotes students' personalized learning, and subverts the convention teaching devices to a large extent.However, due to the fact that a considerable number of students in vocational colleges have unknown learning purpose, academic motivation is insufficient.Therefore, how to stimulate and cultivate the academic motivation of higher vocational students these aspects of research is particularly important.Based on the data studies, the comparison method and the questionnaire survey method, this paper studies the influence of mixed OTO teaching on the academic motivation of higher vocational students.Research findings: the current mixed OTO teaching devices can realize the effective reform of the convention education mode, significantly improve the quality and effect of teaching, boost development of students' autonomous learning ability, improve students' ability to find and solve problems, and improve students' comprehensive quality.Therefore, mixed teaching has a positive and positive impact on the academic motivation, learning habits and learning methods of higher vocational students.
Construction characteristics of intelligent teaching space of innovation and entrepreneurship studio from the perspective of 5G+AI

The emerging technology of 5G+AI is quietly going deep into everyone's life, and in this stage, we also have a renewed pursuit of the improvement of living standards and quality of life.While the traditional learning way has been far from meeting the basic needs of people now, the intellectualization of teaching space is the main melody of The Times and the trend of The Times.This paper introduces 5G+AI, briefly analyzes the advantages of the integration of the two, and puts forward feasible strategies.
Research on the Application of Online and Offline Blended Courses—Taking the Course of “Musical Instrument Performance–Erhu” of Sichuan Minzu College as an Example

Online and offline blended teaching is an effective means to improve the quality of teaching.The paper takes the course "Instrumental Performance -Erhu" of Sichuan Minzu College as an example, through the online and offline blended courses design of the teaching content design, course platform construction, course resources construction, course goals construction, combined with the implementation of courses construction throughout the entire teaching process, it shows the reform effects of online and offline blended courses, in order to better develop online and offline blended courses.
A Comparative Study on Choir Teaching Based on Diversified Models in Chinese and Foreign Universities under the Background of Information Technology

With the continuous development of information technology, the traditional choir teaching mode can no longer meet the needs of students, and diversified teaching modes need to be adopted.This article aims to explore a comparative study of choir teaching based on diversified models in Chinese and foreign universities under the background of informatization.By comparing the different modes of choir teaching in Chinese and foreign universities and analyzing their characteristics, advantages and disadvantages, this paper explored the impact of diversified modes on choir teaching and proposed corresponding improvement suggestions.In addition, a questionnaire survey was conducted to analyze the diversified mode of choir teaching.The research results indicated that 86% of people agree with diversified teaching platforms.Diversified models can effectively improve the effectiveness and quality of choir teaching, but it is also necessary to pay attention to adapting to different teaching environments and student needs.
Synergies Between Case-Based Reasoning and Deep Learning for Survival Analysis in Oncology

Survival analysis is a field of statistics specialized in making predictions about the survival length of patients, even though it can be applied to the prediction of any future event. It is routinely used in medical research to stratify patients in groups based on risk, such as high-risk groups and low-risk groups, and has paramount important in patient stratification and treatment. Recently, deep neural networks (DNNs) have raised considerable attention for survival analysis because of their non-linear nature and their excellent ability to predict survival, in comparison to statistical methods. In this domain, case-based survival methods have started to by applied as well, with some success. It is therefore interesting to study how to synergistically combine the two for improved performance for several reasons. From the case-based reasoning standpoint, the deep neural network can detect deep similarity between cases with a time-to-event structure and from the DNN standpoint, case-based reasoning can provide the glass-box approach that remedies the “black box” label attached to them. In this study, we propose a synergy between case-based reasoning and Long Short-Term Memory (LSTM) model for survival prediction in oncology. In this deep survival model network, the total loss function combines four different factors and uses an adaptive weights approach to combine the four loss terms. The network learns a prototype layer during training which naturally comes with an explanation for each prediction. This study employs cross-validation and the concordance index for assessing the survival prediction performance and demonstrate on two cancer methylation data sets that the developed approach is effective.
Big Data Database for Business

Key-value databases, one of the most common NoSQL (Not only SQL), are essentially shared sorted arrays or distributed hash tables. The hash table is a data structure that is accessed directly based on key values, that is, it accesses records by mapping key values to a location in a table to speed up lookups. This mapping function is called a hash function and the array that holds the records is called a hash table. Data in a key-value database is stored as a key-value pair, with a particular key pointing to a particular data value.
An Improved Apriori Algorithm for Interestingness of Association Rules: A Case Study on the Mushroom Dataset

Currently, data mining is a field that is growing very strongly thanks to the trend of digital transformation globally. An important branch of data mining is Mining Association Rule, which tries to extract useful information from large amounts of data through finding frequent itemsets and association rules. The most popular and well-known algorithms of this domain is the Apriori algorithm. However, this algorithm still has weaknesses in terms of runtime as well as the quality of the generated rules is not high. There have been many studies aimed at reducing the running time of Apriori algorithm, but the quality of the generated rules has not been improved much. This drawback leads to the problem that users cannot use these rules or in worse case, get misleading information. This work focuses on proposing a new approach to improve the quality of association rules derived from the Apriori algorithm on Mushroom dataset. Results show that the new approach helps to improve the quality of rules and reduce the run time compared to the original Apriori.
How Predictive Software Engineering Addresses Issues in Custom Software Development and Boosts Efficiency and Productivity

Many of the bottlenecks facing businesses today can be overcome with an improved service delivery framework. This paper describes the concept of Predictive Software Engineering (PSE) and how it ensures transparency, controllability, and predictability in software development, mainly focusing on the seven principles that the framework is based on. Each principle offers a unique way of benefiting the company that implements it and is associated with its own set of strategies, best practices, methodologies, and important KPIs. Throughout the paper, the authors detail the advantages of using PSE in custom software development, including project transparency and budget management. Together, the principles can transform and streamline processes associated with custom products and help build successful businesses. The PSE framework draws on over 27 years of experience in software development and considers recognized and generally accepted practices.
Smart Cities Improving Government Management Systems with Blockchain Technology

The confluence of two phenomena, accelerated dissemination of information and communication technologies (ICTs)–digital transformation, associated with the increase of population in large urban centers converge to the emergence of the phenomenon that is conventionally called smart cities. Smart cities are understood as those that employ ICTs to optimize their services, ensuring not only a more efficient and effective governance, but also an improvement in the quality of life of their citizens. And, in this ICT-dominated environment, a digital interconnection of everyday objects with the Internet (“Internet of Things” or IoT) is created. In this sense, from the literature review, this paper seeks to explore to what extent the use of blockchain technology would succeed in being the secure governance infrastructure in the development of smart cities filled by the connected devices. The study explains how blockchain creates an information registration system which allows network participants to exchange data with a high degree of reliability and transparency without the need for a centralized management. And given the characteristics of blockchain, this study aims to analyze how this technology can contribute to improving governance in smart cities, toward efficient urban management, and namely to improvement the Sustainable Development Goals (SDGs). The results achieved with the case studies allowed us to identify the limits and possibilities concerning the use of blockchain for the governance of cities. Thus, it can be concluded that despite the associated limits, such as ethical use problems and sustainability issues, blockchain has the potential to function as the integral infrastructure of digital ecosystems that may be employed in smart cities.
String Processing

In C programming, string is a sequence of characters terminated with a null character “\0”. This chapter introduces some of the ready-to-use functions to work with strings. The functions studied here are defined in the string.h header file.
Maintaining Performance with Less Data: Understanding Useful Data

As deep learning tasks become more popular, their computational complexity increases, leading to more intricate algorithms and models which have longer runtimes and require more input data. The result is a greater cost on time, hardware, and environmental resources. We propose a novel method for training a neural network for image classification to reduce input data dynamically, in order to reduce the costs of training a neural network model. By using data reduction techniques, we reduce the amount of work performed and therefore the environmental impact of AI techniques, and with dynamic data reduction, we show that accuracy may be maintained while reducing runtime by up to 50% and reducing carbon emission proportionally and gives a way forward for the better implementation of useful data.
The Digital Survival Game to Enhance the Digital Quotient of Lower Secondary Students

This research aims to study the results of a digital game with a student-centered approach to enhance the digital quotient of lower secondary school students. The sample group consisted of 133 lower secondary school students by multi-stage sampling from two classes of each level of the Assumption College English Program in the 2020 academic year. The research tools included (1) a digital game with a student-centered approach to enhance the digital quotient of lower secondary school students; and (2) a digital quotient test. The data were analyzed using the dependent sample’s arithmetical mean, standard deviation, and a t-test. The research findings revealed the following: (1) the content and game design of a digital game with a student-centered approach had an Index of Item-Objective Congruence of 0.88 and 0.85 and an effectiveness Index of 0.28; and (2) the digital quotient achievement score was significantly higher than the pre-test scores at a level of 0.01.
SR-OIR-SSD: Super-Resolved Eyes in the Sky

In many situations, observing from a high altitude is always fruitful. Humans have been aware of this fact since ancient times, and the military watchtower that changed the result of many wars is the best example. In the current scenario, humans have the latest technology, like satellites, unmanned aerial vehicles (UAVs) that provide aerial images from high altitudes. Aerial photos can be analysed manually or through AI algorithms like object detectors and tracking. In this age of automation, manual execution is not recommended. In this work, object detection has been performed on aerial images to get inside aerial photos. Object detection is a mature technique in AI. Several state-of-the-art deep learning methods for object detection, such as R-CNN, Fast R-CNN, Faster R-CNN, SSD, and YOLO, have been published in recent years. However, these methods perform well with standard and low-resolution photos and are inappropriate for aerial images. Aerial images are very different as compared to standard images. It contains too many objects in a single image. Pixel per object is significantly less as compared to the traditional image. The conventional image has a front view of the object, while the aerial photo has a top view. Hence none of the existing algorithms works fine for aerial images. This work proposes a novel technique based on a GAN and modified SSD architecture for object detection on aerial images attaining high mAP. This architecture has improved mAP from 0.72 to 0.92 on the Stanford data set, i.e. (28% improvement), while from 0.04 to 0.586 on Visedrone2018.