Reviewerly/exHarmony huggingface dataset:
Investigating the Sentiment in Italian Long-COVID Narrations

Through an overview of the history of the disease, Narrative Medicine (NM) aims to define and implement an effective, appropriate and shared treatment path. In the context of COVID-19, several blogs were produced, among those the “Sindrome Post COVID-19” contains narratives related to the COVID-19 pandemic. In the present study, different analysis techniques were applied to a dataset extracted from such “Sindrome Post COVID-19” blog. The first step of the analysis was to test the VADER polarity extraction tool. Then the analysis was extended through the application of Topic Modeling, using Latent Dirichlet Allocation (LDA). The results were compared to verify the correlations between the polarity score obtained through VADER and the extracted topics through LDA. The results showed a predominantly negative polarity consistent with the mostly negative topics represented by words on post virus symptoms. The results obtained derive from three different approaches applied to the COVID narrative dataset. The first part of the analysis corresponds to polarity extraction using the VADER software, where, from the score, polarity was inferred by dichotomizing the overall score. In the second part, topic modeling through LDA was applied, extracting a number of topics equal to three. The third phase is based on the objective of finding a qualitative relationship between the polarity extracted with VADER and the latent topics with LDA, considering it a semi-supervised problem. In the end, the presence of polarized topics was explored and thus a correspondence between sentiment and topic was found.
A User Perspective on Sustainability

As our society undertakes an energy transition to low carbon future, many sectors ranging from the energy grid to transportation are in the midst of a transformation to newer and cleaner technologies such as electric vehicles, electric heat pumps, and distributed renewables. These technologies will have a direct impact on an end-users in terms of behavior, comfort, and lifestyle. In this column, we examine the user's perspective of an energy transition.
The Application of Bayesian Network in Higher Vocational English Application Ability Examination

At present, the status of English application ability test in vocational College is getting higher and higher in practice teaching, and gradually becomes the main means of testing the English level of non-English majors. It can not only accurately judge the students’ learning situation, but also provide effective information for English teaching in vocational college. Therefore, in the context of the era of big data, scholars propose to mine valuable content from massive data, so as to provide support services for the decision-making management of higher Vocational English application ability test and meet the needs of higher education for information system. On the basis of understanding the concept of data mining, this paper studies the experimental analysis of classification model by using Bayesian network classification tool and ID3 algorithm according to the theoretical basis of Bayes. The final results show that the model based on Bayesian network can play a positive role in the vocational College English application ability test.
Path Optimization and Multi-level Path Planning for the Steam Field Navigation Algorithm

The computational problem of path planning is considered as a core functionality for robotic systems. Physics-based path planning algorithms are developed to generate a path for a robotic agent between two points using a mathematical model as the main building block. In these physical algorithms, the field is not represented as a grid of cells or a set of nodes where a search technique is used to find the path, such as the case for combinatorial and sampling-based algorithms. The Stream Field Navigation (SFN) algorithm is a physical algorithm that is developed to solve the path planning problem by simulating a fluid field. One of the properties of paths generated by the SFN algorithm is that they may take a longer path than required due to the nature of the underlying physical model. To overcome this phenomena, this research proposes a method to optimize the path generated by the SFN algorithm. The proposed method consists of three algorithms that can be successively applied to a path with the aim of making it shorter. The results for the developed path optimization algorithms show an average of 14.5% reduction in path length compared to the original path generated by the SFN algorithm. Such contribution has a favorable effect on energy expenditure by systems using the SFN algorithm due to the direct relation between path length and system energy consumption. This research also proposes a method to allow the SFN algorithm to operate in multi-level environments. This addition enhances the standard SFN algorithm as it is designed for single-plane path planning. The results for the developed multi-level path planning approach show a comparison of the effectiveness of the SFN algorithm with other algorithms in these multi-level room-based environments where the SFN algorithm is shown to produce paths in less computational time and the paths are of higher quality.
Review on Image Steganography Transform Domain Techniques

In today’s society, data security is of the utmost importance. Securing data by ensuring confidentiality and integrity is crucial. Security of information is essential for transferring confidential data, data storing, and database systems. Steganography is one of the methods of protecting data. As you can see, steganography is the art and science of concealing communication, i.e., encasing the secret message inside another medium, such as images, audio, video, or text. Steganography is a method with several advantages, including high concealment capacity and undetectability. There are different types of steganography. The type of steganography depends on the type of cover object such as text, image, video we use to conceal the secret. Image steganography is hiding the existence of the data using the image as the cover image. Lastly, the parameters Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), and Structured Similarity Index Measure (SSIM) are calculated which give an indication of the efficiency of the proposed method.
Evaluation of College Students’ Entrepreneurial Quality Under Internet Environment Based on BP Neural Network

In the context of the new era, promoting employment based on entrepreneurship is the main topic of social and economic construction and development, encouraging and supporting college students to entrepreneurship is an effective way to solve the current employment dilemma. From the perspective of the learning and growth trend of college students, in order to build a good entrepreneurial environment in the increasingly complex social environment, it is necessary to not only transfer the basic knowledge and skills needed for entrepreneurship and employment to college students, but also strengthen their comprehensive quality. Therefore, it is very important to comprehensively evaluate the entrepreneurial quality of college students. From the perspective of college students’ growth under the Internet environment, this paper constructs the evaluation index system of college students’ entrepreneurial quality, and uses BP neural network to conduct empirical research. The final results show that the evaluation of entrepreneurial quality of college students based on BP neural network plays a positive role in the Internet environment.
Brightness Preserving Medical Image Contrast Enhancement Using Entropy-Based Thresholding and Adaptive Gamma Correction with Weighted Distribution

The contrast enhancement has a significant role to play in improving the visibility and interpretation during analysis by medical experts and machines. The enhancement of the image must preserve the original image characteristics such as restoring the original brightness. The main aim of the proposed method is to improve the visual quality while retaining image details such as the entropy and brightness of the images. We proposed a technique for contrast enhancement that uses entropy Otsu thresholding as the base method for binarization, and then one of the sub-images is equalized using adaptive gamma correction with weighted distribution while the other part is equalized with contrast limited adaptive histogram equalization (CLAHE) technique. The results are compared with benchmark contrast enhancing techniques such as MMBEBHE, BBHE, and with Otsu thresholding binarization technique. The results are evaluated on several quantitative parameters such as structural similarity index measurement (SSIM), average mean brightness error (AMBE), FSIM, and entropy.
Research on the Application of Student Information Management System in the Management of Higher Vocational Colleges

As an effective countermeasure to ensure the quality and safety of the educational environment, the management of higher vocational colleges, combining information technology to construct a student information management system, is an important subject of practical innovation research, which directly determines the internal management level of higher vocational colleges and the integration of students Quality. Generally speaking, the management of college students involves education, basic management, rewards and subsidies, employment and accommodation, etc. As the overall work is more complex, it is difficult to implement informatization construction. After understanding the current problems faced by higher vocational colleges during the education management period, this article discusses in depth how to build a diversified system module that meets the development needs of the times based on the construction of the student information management system. The final use results prove that the use of student information management system during the management of higher vocational colleges can solve the problems of traditional education management on the basis of improving overall work efficiency.
Using an Interactive Theorem Prover for Formally Analyzing the Dynamics of the Unmanned Aerial Vehicles

The dynamical analysis of Unmanned Aerial Vehicles (UAVs) is based on accessing their performance, stability, and various other control systems properties and it involves modelling their dynamical behavior that is generally captured by a set of differential equations. The state-of-the-art approaches used to study the dynamics of UAVs are analytical and computer-based testing or simulations. However, the inherent limitations of these methods, i.e., human error proneness, sampling-based analysis, approximations of the mathematical results and the presence of unverified algorithms in the core of the associated tools, make them unsuitable for analyzing the UAVs, which are extensively being advocated to be used in many safety-critical applications. Recently, interactive theorem proving, a formal verification technique, has been utilized for analyzing the dynamics of UAVs to overcome the above-stated limitations of the conventional approaches. This chapter briefly overviews these interactive theorem proving based efforts while highlighting their strengths and weaknesses.
Construction and Practice of Art and Design Education Resources Based on Big Data

In the rapid development of information technology and the Internet, the way of social production and learning entertainment has undergone new changes. In the traditional sense of the artistic design education form as emerging technological innovation put forward more requirements, so also should follow the time development of colleges and universities to integrate the education resources construction, information technology such as the rational use of cloud computing and big data to build sharing framework, design and develop digital curriculum resources, realizing the objective of the modernization of education development in at the same time, To improve the art and design education level and core competitiveness of colleges and universities. Based on the understanding of the status quo of art and design education in the era of big data, this paper makes clear the importance and technical characteristics of resource sharing architecture, and deeply discusses how to build personalized art and design education resource recommendation system based on big data technology.
Advanced Python Programming for Data Science

This chapter will introduce the advanced Python programming concepts and skills necessary to excel as a data scientist.
Research of Professional Diagnosis and Improvement Index System in Higher Vocational Colleges Based on CIPP

Professional education level is the key to the development of higher vocational colleges, and professional diagnosis and improvement can ensure the continuous improvement of professional education quality. Based on the CIPP evaluation model, this paper constructs the professional medical reform system of higher vocational colleges by analytic Hierarchy Process (AHP) and selects the applied electronic technology major of the author's school to carry out empirical research, which provides certain reference for the professional medical reform of higher vocational college.
Data wrangling with Python

Data wrangling is the process of transforming and mapping data from one raw data form into another format with the intent of making it more appropriate and valuable for data science purposes.
Blockchain Technology for Food Supply Chain Traceability and Authentication

To enhance food quality and value, transparent traceability systems, also proofed by authentication methods, are used to give information about the product from the primary production stages to the selling point. Nowadays, information is accessed in multi- or omni- channel (websites, mail, app, social media). Data analytics and Artificial Intelligence can then highly enhance marketing strategies. Furthermore, there is a high demand of proof of authenticity systems able to certify high-value food credential attributes. Thus, in this paper a blockchain-based system is proposed to manage and certify traceability information and, therefore, the authenticity of food products. In the context of the PININ project, the traceability of dairy supply chains has been considered. A decentralized application (DApp) has been developed for the registration of the collected traceability data in a consortium blockchain, where a limited number of actors can submit transactions and access data. The DApp also allows the publication of recorded data in a regional blockchain to render them publicly available. A mobile application has been developed to connect the physical products with their digital twins. By means of a QR code printed on labels, certified data are then available to food supply chain public and private stakeholders. Electronic collars equipped with GPS and Bluetooth systems have been also developed to geo-referencing cattle in mountain pastures. The acquired data, recorded in the blockchain, allow competent authorities to securely verify the position of the animals for the correct disbursement of European funds to farmers, thus preventing frauds.
Attribute Relevance and Discretisation in Knowledge Discovery: A Study in Stylometric Domain

The paper demonstrates the research methodology focused on observations of relations between attribute relevance, displayed by rankings, and discretisation. Instead of transforming all continuous attributes before data exploration, the variables were gradually processed, and the impact of such a change on the performance of a classifier was studied. Considerable experiments carried out on stylometric data illustrate that selective discretisation could be more advantageous to predictive accuracy than some uniform transformation of all features.
Elementary: Fundamental Library Styles

In this chapter, we will show you how multiple frameworks bring famous design systems to the presentation layer of the Web: CSS. We’ll begin with an inspection of current CSS frameworks and the benefits they offer to web developers.
Long-Term Prediction of Cloud Resource Usage in High-Performance Computing

Cloud computing is gaining popularity in the context of high-performance computing applications. Among other things, the use of cloud resources allows advanced simulations to be carried out in circumstances where local computing resources are limited. At the same time, the use of cloud computing may increase costs. This article presents an original approach which uses anomaly detection and machine learning for predicting cloud resource usage in the long term, making it possible to optimize resource usage (through an appropriate resource reservation plan) and reduce its cost. The solution developed uses the XGBoost model for long-term prediction of cloud resource consumption, which is especially important when these resources are used for advanced long-term simulations. Experiments conducted using real-life data from a production system demonstrate that the use of the XGBoost model developed for prediction allowed the quality of predictions to be improved (by 16%) compared to statistical methods. Moreover, techniques using the XGBoost model were able to predict chaotic changes in resource consumption as opposed to statistical methods.
To Multiply Value, Mitigate System Complexity

Software can generate seemingly limitless value, but it is complex to get it right. This complexity is exemplified in the struggle of traditional car companies trying to become software companies. Vehicle recalls are increasing in large part due to software defects. In contrast to traditional car makers, Tesla has become the most valuable car maker because they are a software company first. How they deal with complexity of software development can teach a lot about how enterprise companies should, while trying to become software companies. [1]
Balance of Features in Mobile Development

In Chapter 3 , we reviewed how to plan out and prevent mistakes in your mobile app development. Now, we will go into more details and explain further how to select and prioritize desired features and how to start with an MVP for your mobile app.
How to Promote Your Mobile App in the Market

Chapter 5 was all about your users and how to listen to their needs and show them that your mobile app offers great value for them.
How to Choose the Platform for Your Mobile App?

In the previous chapter, I’ve explained why your business needs a mobile app—in this chapter, I will help you better understand different platforms for developing a mobile app and provide reasons a custom app can be a better choice for your business. Additionally, I bring you guidelines on how to choose a reliable development partner and what time it will take for your mobile app to be delivered.
An Investigation of Design Utilies App’s In Play Store

Kavathia Haresh Himmatbhai, Dr. G. C. Bhimani
Trends, impacts and responses to cybercrime in the Zimbabwean retail sector

Purpose This study aims to explore the nature and impacts of cybercrime in the Zimbabwean retail sector and evaluate the effectiveness of the current measures to deal with cybercrime. Design/methodology/approach This study adopted a mixed research design on a sample of 38 retail players in a small mining town, Bindura, who were selected using stratified random and purposive sampling techniques. Data were collected through a mainly closed-ended questionnaire and in-depth interview guide. Statistical Package for Social Sciences (SPSS) was used to analyse quantitative data, whereas summative content analysis was used to analyse qualitative data. Findings Virus dissemination, hacking and card fraud were identified as the major forms of cybercrime prevalent in the retail sector. Additional security costs, loss of sensitive data and direct financial losses were found to be the major impacts posed by cybercrime on retail sector. It was also established that the current internal measures and policing efforts to fight cybercrime in the retail sector were ineffective. Originality/value This study provides context-specific information on the scourge of cybercrime in a developing third world country that has of late been characterised by a myriad of other socioeconomic challenges. Although similar studies have been conducted in other parts of the globe, there has been a dearth of literature on retail sector cybercrime in Zimbabwe. This study also interrogates the Zimbabwean retail sector’s preparedness in dealing with this contemporary threat.
Data analysis with performance and privacy enhanced classification

Abstract Privacy is the main concern in cyberspace because, every single click of a user on Internet is recognized and analyzed for different purposes like credit card purchase records, healthcare records, business, personalized shopping store experience to the user, deciding marketing strategy, and the list goes on. Here, the user’s personal information is considered a risk process. Though data mining applications focus on statistically useful patterns and not on the personal data of individuals, there is a threat of unrestricted access to individual records. Also, it is necessary to maintain the secrecy of data while retaining the accuracy of data classification and quality as well. For real-time applications, the data analytics carried out should be time efficient. Here, the proposed Convolution-based Privacy Preserving Algorithm (C-PPA) transforms the input into lower dimensions while preserving privacy which leads to better mining accuracy. The proposed algorithm is evaluated over different privacy-preserving metrics like accuracy, precision, recall, and F 1-measure. Simulations carried out show that the average increment in the accuracy of C-PPA is 14.15 for Convolutional Neural Network (CNN) classifier when compared with results without C-PPA. Overlap-add C-PPA is proposed for parallel processing which is based on overlap-add convolution. It shows an average accuracy increment of 12.49 for CNN. The analytics show that the algorithm benefits regarding privacy preservation, data utility, and performance. Since the algorithm works on lowering the dimensions of data, the communication cost over the Internet is also reduced.