Reviewerly/exHarmony huggingface dataset:
Automatic Subjective Answer Evaluator Using BERT Model

Subjective answers might get tedious to check and take time and effort. Although there has been much research in this field, there needs to be an all-developed and deployable method that considers all factors like keyword matching, context analysis, and relative scoring. We have developed an algorithm for Subjective Answer Evaluation using Tf-idf scoring, BERT-based context analysis, and assigned scores based on relevance. We calculate the Tf-idf value for the keywords, and then if they match with the keywords from the evaluator’s answer, we add the Tf-idf value to generate a total score for each answer. Further, we use transfer learning from BERT and assign scores based on context relevance. Finally, we use a combination of both scores to calculate relative scoring and then give the results as relative scores using a custom algorithm. Comparing these scores to manual scores, i.e., checked by the evaluator, we obtained an accuracy of 90%.
A Novel Geofence-Oriented Approach to Activity Alerts and Notifications for Dementia Patients

The primary objective is to create an efficient system that makes use of a geofencing, which would be beneficial to specific groups of people that require geo-location-based prompts. Groups of people that this may aid in day-to-day activities would include memory-loss patients such as dementia patients, fishermen who rely on location for maximum yield, and even the common person who would like to be reminded about a certain task (such as groceries) on their way to a different location. This would be particularly useful for reminders on common tasks which (mostly considered to be trivial) a person would usually forget. The novelty in this application is the usage of foreground services to ensure that the application is performing tasks in the foreground, while consuming system resources, and notifies the user through the status bar notification. It ensures that the service continues running even when the user is not actively interacting with the app. To quote an example of the workflow, if a user sets a reminder called ‘pick up medicines’ and sets the location as ‘pharmacy’, then the application would remind the user to pick up their medicines once they actually reach a pre-set radius of the closest pharmacy, else a specific branch of desired pharmacy can also be specified as the location.
Suitable Artificial Intelligence Techniques for Multispectral Image Classification

Hydrologic modelling is a complicated process depending on various factors. Since the evaluation of factors are exposed to high uncertainty due to high spatial variation. Hence, the precision of each factor becomes essential for modelling. Multispectral images are a highly informative set of data. It holds details of spectral characteristics and spatial structure of the objects in the image. Such data is beneficial to identify and classify the land covers on the bases of spectral signatures. Machine learning and AI techniques have made this work more efficient. This paper aims to understand the ability and suitability of AI techniques such as maximum likelihood classification (MLC), random trees (RT), and support vector machine to classify the image correctly. This paper discusses the basic principles of these AI techniques for land use classification. Accuracy assessment was considered as the criteria of comparison. Based on the obtained results, the performance of the SVM technique is found better than MLC and RT based on overall and Kappa coefficient (>0.80) for training and Kappa (0.64) for testing.
A Lightweight Global Taxonomy Induction System for E-Commerce Concept Labels

Given a domain-specific set of concept labels as input, global taxonomy construction (GTC) is defined as the problem of automatically inducing a taxonomy over the concept labels. Despite its importance in domains such as e-commerce and healthcare, and recent algorithmic research as a result, practical tools for inducing and interactively visualizing taxonomies over domain-specific concept sets do not currently exist. To be truly useful, such a tool must permit a reasonable solution in a relatively unsupervised setting, and be applicable to general subsets of concept labels. In this paper, we present InVInT (Interactive Visualization and Induction of Taxonomies), an unsupervised and lightweight global taxonomy induction system for arbitrary concept-labels. We demonstrate the utility of InVInt on e-commerce concept labels taken from challenging real-world datasets. The system is end-to-end, only taking a simple text file as input and yielding a tree-like taxonomy as output that can then be rendered on a browser, and that a non-technical practitioner can interact with. Important components of the system can also be customized by a technically experienced user.
User Interest Based POI Recommendation Considering the Impact of Weather Details

Point of Interest (POI) recommendation is the most illuminating topic in the recent era. Being a part of Location-Based Social Networks (LBSN) it has achieved the popularity level rapidly. The POI recommendation system became very famous since planning a tour needs several POIs as per choices in the tourism industry. Generating routes for travel based on the interest of various users is a tough job to accomplish in real life as there are huge amount of data available (e.g., geo-tagged photos, maps, check-in histories, etc.). It is also quite challenging to recommend POIs considering various trip constraints. To deal with such difficulties an algorithmic approach has been proposed using user interest and the weather context. The proposed recommendation system is modeled with the help of the orienteering problem. It uses two constraints of the trip, the time duration of visiting each POI and the weather condition of each POI at different travel sequences. The proposed technique called ClimeRecTour considers the duration of visits to POIs which helps to determine user’s interest over certain categories of POIs on specific weather condition and generates a list of recommended POIs. To analyze the performance of the proposed technique alongside the baselines the Flickr User-POI Visits dataset has been used. The results of the experiment are compared with respect to recall, precision, and F1-score. From the comparison it has been observed that, the proposed algorithm outperforms other baselines.
Detecting Trending Topics and Influence on Social Media

In today’s world, social media platforms have a significant potential impact on individuals in various areas and issues. Twitter is one of these social media platforms with its wide communication network. In addition, Twitter has become a popular social media platform for sharing and communication in the field of health. It has the potential to have a significant impact on individuals in the field of health, as it is a platform used by both the public to obtain information and the scientists to share information. At this point, the analysis of the tweets and tweet owner on Twitter in the field of health for Turkey has been considered as an important topic to be focused on. Accordingly, the main purpose of this study is identifying and predicting trending healthcare topics and influential people in Turkey during a specific period. By doing this, important health issues and problems in Turkey will be determined. Accounts with potential impact in the field of health will identify identified. In line with this goal, an efficient model as a method is proposed for determining healthcare trending topics and influential people. The proposed method to cope with this problem includes two aspects, namely utilizing the n-grams algorithm to identify trending topics and employing social network analysis to identify influencers. In addition, the developed framework has an interface design to display trending topics and influential people for the desired time interval.
Centric Management of Resources in Modern Distributed Computing

The advent of cheap, high-quality digital cameras and widespread use of computers has resulted in an explosion in the production of digital documents. High-resolution digital cameras produce documents that require a lot of space for storing data and time for communicating. Magnetic resonance imaging (MRI) and computer tomography are just two examples of the medical imaging modalities in the healthcare sector that generate voluminous amounts of documentation for use in diagnosis and examination. The need for large amounts of storage space and efficient communication bandwidth has led to the creation of document compression methods. A primary objective of document compression methods is to define documents in their compact form in order to lessen the volume of data needed for storage or transmission over a network. Fractal document compression is a powerful algorithm for reducing file sizes (RFS). Due to its minimum speed limitation, fractal compression is best used for creating extremely small files that decompose extremely quickly. This study examines the effectiveness of various metaheuristic algorithms for enhancing the compression efficiency of RFS, with the goal of resolving these problems. The following is a list of the specific research goals that were pursued throughout the project.
Local Optima Networks of the Black Box Optimisation Benchmark Functions

Compressed monotonic local optima networks (CMLONs) provide a powerful means to visualise the global structure of the fitness landscapes of optimisation problems as network graphs. Historically, they have been developed for discrete optimisation problems, but they have more recently been extended to continuous problems. However, experience is limited because they have only been applied to a few analytical problems and even fewer real-world problems. This work aims to address that gap by providing a systematic and comprehensive catalogue of CMLONs for the well-known black box optimisation benchmark functions. These are a set of continuous analytical functions with diverse properties. CMLONs are calculated for each of the twenty-four benchmark functions in three, five, eight, twelve, and twenty dimensions. Network metrics are also calculated for each of the CMLONs and dimensionality reduction used to classify and compare the functions. It was found that the CMLONs have diverse representations that are related to both the functions' properties and their dimensionality. Network metrics were important for multimodal functions in higher dimensional problems, where the CMLONs were too dense to visualise meaningfully. These results provide an extensive catalogue of CMLONs for a variety of continuous functions and provide a useful reference for real-world optimisation problems.
Low-Latency Federated Learning via Dynamic Model Partitioning for Healthcare IoT

Federated learning (FL) is receiving much attention in the Healthcare Internet of Things (H-IoT) to support various instantaneous E-health services. Today, the deployment of FL suffers from several challenges, such as high training latency and data privacy leakage risks, especially for resource-constrained medical devices. In this article, we develop a three-layer FL architecture to decrease training latency by introducing split learning into FL. We formulate a long-term optimization problem to minimize the local model training latency while preserving the privacy of the original medical data in H-IoT. Specially, a Privacy-ware Model Partitioning Algorithm (PMPA) is proposed to solve the formulated problem based on the Lyapunov optimization theory. In PMPA, the local model is partitioned properly between a resource-constrained medical end device and an edge server, which meets privacy requirements and energy consumption constraints. The proposed PMPA is separated into two phases. In the first phase, a partition point set is obtained using Kullback-Leibler (KL) divergence to meet the privacy requirement. In the second phase, we employ the model partitioning function, derived through Lyapunov optimization, to select the partition point from the partition point set that that satisfies the energy consumption constraints. Simulation results show that compared with traditional FL, the proposed algorithm can significantly reduce the local training latency. Moreover, the proposed algorithm improves the efficiency of medical image classification while ensuring medical data security.
First-Order Logic and Set Theory

This chapter briefly recapitulates the main ingredients of Booleans, propositional logic, first-order logic, set theory, functions, and relations. The main purpose of this chapter is to provide a quick reference for formal notation used in later chapters; for more detailed information, examples, and intuition, we refer the reader to textbooks on this topic, such as [154].
Social engineering the dispersed workforce

The workplace has gone through a forced and dramatic change. Remote and hybrid working have been normalised, putting a huge strain on the technology infrastructures that support them and rendering many of our traditional cyber defences obsolete. Of all the threats that face today's workforce, social engineering is arguably the most pernicious and difficult to mitigate.
DDNAS: Discretized Differentiable Neural Architecture Search for Text Classification

Neural Architecture Search (NAS) has shown promising capability in learning text representation. However, existing text-based NAS neither performs a learnable fusion of neural operations to optimize the architecture nor encodes the latent hierarchical categorization behind text input. This article presents a novel NAS method, Discretized Differentiable Neural Architecture Search (DDNAS), for text representation learning and classification. With the continuous relaxation of architecture representation, DDNAS can use gradient descent to optimize the search. We also propose a novel discretization layer via mutual information maximization, which is imposed on every search node to model the latent hierarchical categorization in text representation. Extensive experiments conducted on eight diverse real datasets exhibit that DDNAS can consistently outperform the state-of-the-art NAS methods. While DDNAS relies on only three basic operations, i.e., convolution, pooling, and none, to be the candidates of NAS building blocks, its promising performance is noticeable and extensible to obtain further improvement by adding more different operations.
Regulation as a function of social adaptation management of military school cadet

The article reveals such an important function of management of social adaptation of a military school cadet as regulation (regulation). It plays a leading role in correction of other functions — goal-setting, information, planning, organization, motivation and stimulation, control and analysis. It is argued that the regulation of the management of social adaptation of the cadet is a type of professional activity, which represents a way of maintaining the unity of any system, its development. Particularly significant is the relationship of regulation in numerous army organizations, as well as in society as a whole, in which social regulators are becoming increasingly important as state guidelines.
METHODOLOGICAL BACKGROUND FOR DEVELOPING INTEGRATED DISCIPLINES IN THE HIGHER EDUCATION INSTITUTIONS

Integration is a perspective direction for improving modern education. For persons who have entered a shortened form of education or have an educational and qualification level obtained in another speciality, it is necessary to introduce the teaching of an integrated discipline in the practice of learning. This will allow such applicants for education to systematize their knowledge and focus on the most pressing issues of their future profession. The small number of integrated disciplines in the educational process is due to a lack of awareness among teachers of the advantages and opportunities of integrated disciplines for training specialists, and insufficient development of methodological foundations for the promotion of integrated disciplines. The purpose of the article is to characterize the methodological bases for the development of integrated disciplines in higher education institutions. The main scientific results of the study include the definition of integration as a process, result and phenomenon; characterization of the features and forms of integration of educational disciplines (intra-disciplinary, interdisciplinary, multi-level, combined, trans-subject); identification of the positive and negative aspects of the introduction of an integrated educational discipline. The authors define integration as a phenomenon characterizing the synthesis of procedural and resultant components of integration with the aim of targeted formation of new knowledge and practical skills among students. The main advantages of the introduction of integrated disciplines include systematization and concentration of knowledge within one discipline; comprehensive consideration of the object of the study taking into account all its characteristics; no duplication of information in different courses; saving students� time as a result of workload optimization. The general conclusion is that the development and implementation of an integrated discipline in educational practice contributes to the further development of the professional knowledge and skills of a future specialist. Prospects for the further introduction of integrated disciplines in higher education are related to the improvement of the existing format of training using integrated disciplines, as well as regulatory and methodological bases for the development and implementation of integrated disciplines in higher education institutions.
PROFESSIONAL DEVELOPMENT EVALUATION OF THE PERSONNEL OF THE HIGHER EDUCATION INSTITUTIONS

Personnel evaluation occupies a dominant place in the personnel management system. The focus on strengthening the reasonableness of managerial personnel decisions actualizes the issue of using a methodological toolkit for choosing effective and efficient methods of evaluating the personnel of a higher education institution. A well-designed personnel evaluation system performs a number of functions, which include influencing the motivation of employees, increasing productivity and quality of work, planning professional development and career, improving qualification characteristics, and making personnel decisions about remuneration, promotion and dismissal. The purpose of the research is to determine the methodical basis of personnel evaluation in the aspect of professional development in higher education institutions and to develop a step-by-step process for evaluating employees in these institutions. In particular, the existing approaches to the interpretation of the concept of �personnel evaluation� are characterized and the author�s approach to the interpretation of this concept is described. The article also provides the goals of personnel evaluation, defines the functions of employee evaluation, and characterizes its principles. The greatest attention in the article is paid to the author�s approach to the process of assessment of professional and personal qualities of employees of higher education institutions, consisting of six consecutive stages. The prospect of further scientific research in this area is the development of an effective system of motivational methods to encourage staff in higher education institutions to work efficiently and productively.
Compendium of Qubit Technologies in Quantum Computing

Quantum computing is information processing based on the principles of quantum mechanics. Qubits are at the core of quantum computing. A qubit is a quantum state where information can be encoded, processed, and readout. Any particle, sub-particle, or quasi-particle having a quantum phenomenon is a possible qubit candidate. Ascendancy in algorithms and coding demands knowledge of the specificities of the inherent hardware. This paper envisages qubits from an information processing perspective and analyses core qubit technologies.
Analysis of Existing Datasets of Household Objects for AI-Enabled Techniques

Artificial intelligence is defined as computer-based programs having self-learning capability. Robotics are one of the important application areas of artificial intelligence. Robots which can be trained to perform several day-to-day activities are the major task to achieve in the field of automation. It becomes imperative for the robotic machines to recognize numerous objects readily available in the household. With the rapidly increasing demand of automation, there is sudden rise in the need to train our machines. For this purpose, there is a requirement of large datasets which can be trained and tested for the complete validation. Robots that are trained to recognize household items will also be employed for numerous automated functioning of day-to-day chores. It impacts the society by helping the people with special needs. This study presents the available datasets aiming at the training of machines to classify, recognize, and detect the presence of a particular household object. This work aims at providing the researchers a clear insight of all that is available for use. Having enlisted datasets will help the researchers to use the readily available resource to apply novel methods for automation in households. The analysis of datasets gives the information regarding the number of classes as well as the number of images present in the dataset. It also enlists the size of the datasets. The application that has been applied on these datasets has also been mentioned beside the dataset. The paper also shows a small snippet of the images included in the datasets.
Narrative Paragraph Generation for Photo Stream Using Neural Networks

Humans have the innate ability to perceive an image just by looking at it, for us images are not just a collection of objects but a network of interconnected object relationships. The problem arises when a machine tries to inspect an image, hence we try to convert image data to textual data. Despite major achievements in the image captioning field, there is a lack of models that provide concise captions of a given image; moreover, already existing models are so much bigger in size that the number of learning parameters is very high. The objective of this paper is to fill that gap; hence, we propose a model that incorporates an advanced deep convolution neural network to extract image features and an attention GRU with a local attention network to generate captions. We have also identified a class imbalance problem with Flickr dataset. We rectified this problem by adding images of some specific classes. The model has been trained on our improvised Flickr dataset.
Comparative Analysis of Different Machine Learning Approaches for Sentiment Analysis

As a result of the transition from Web 2.0 to Web 3.0, people have adapted to being educated and socially connected with others while also becoming more empowered to deliver and obtain various services based on their individual thoughts and views. The natural language processing approach, known as sentiment analysis, uses the emotional tone behind the written text material and determines whether such implications are positive, negative, or neutral. It encompasses text extraction for sentiment and qualitative information using data mining, machine learning, and artificial intelligence. This research paper is a review of the literature about different machine learning techniques. It compares accuracy, benefits, and limitations of each machine learning method.