Reviewerly/exHarmony huggingface dataset:
A Conditional GAN Architecture for Colorization of Thermal Infrared Images

The applicability of visible spectrum cameras is limited to nighttime and extreme weather conditions. To overcome these limitations, infrared (IR) cameras were introduced, but their images lack luminance and representation quality, limiting the analytical ability and response time of humans. To be understandable by humans, image enhancement is not sufficient; conversion to visible RGB format is required, and this process is popularly known as colorization. However, the thermal infrared (TIR) images are low in both luminance and chrominance in comparison to grayscale images, which are only low in chrominance. Therefore, TIR colorization needs image-to-image translation; simple color transfer is not enough. In this paper, we investigated and modified one of the most commonly used conditional generative adversarial networks, known as pix2pixHD GAN for TIR-to-visible RGB translation. We are proposing a new composite loss function with noise augmentation in training. The improvement in the average values of NRMSE, PSNR, LPIPS, and NIQE is observed when compared with the state-of-the-art on the publicly available KAIST dataset. The results of the extensive experiments proved the effectiveness of the proposed method for TIR colorization, which is shown using both subjective (visual) and objective assessments for evaluation of image quality.
Routing Resource Allocation for FPC in Complex Scenarios

Since the outline of the flexible printed circuit (FPC) is complex and changeable and the pins are densely distributed in some regions of the layout, it is difficult to route all nets completely. In this paper, we propose a partition algorithm for complex FPC to divide the FPC routing problem into escape routing and area routing in order to better allocate the routing resource. Our algorithm is based on constrained Delaunay triangulation (CDT), several techniques are proposed to adapt it to FPC. These techniques are as follows: (1) a pin cluster dendrogram based on FPC outline information, which makes pin clustering result more accurate; (2) a ternary tree based on CDT to determine the escape boundary of each region; (3) an escape-passage-connection model (EPC model) to describe the topological connection relationship; and (4) a global dynamic routing graph based on CDT to calculate the crossing points on the escape boundaries and generate the topological path of each net without congestion. Experimental results on some industrial cases show that our method has great generalization ability, and the routing results are better than those without considering the area partition.
Self-Supervised Interactive Image Segmentation

Although interactive image segmentation techniques have made significant progress, supervised learning-based methods rely heavily on large-scale labeled data which is difficult to obtain in certain domains such as medicine, biology, etc. Models trained on natural images also struggle to achieve satisfactory results when directly applied to these domains. To solve this dilemma, we propose a Self-supervised Interactive Segmentation (SIS) method that achieves superior generalization performance. By clustering features from unlabeled data, we obtain classifiers that assign pseudo-labels to pixels in images. After refinement by super-pixel voting, these pseudo-labels are then used to train our segmentation network. To enable our network to better adapt to cross-domain images, we introduce correction learning and anti-forgetting regularization to conduct test-time adaptation. Our experiment results on five datasets show that our approach significantly outperforms other interactive segmentation methods across natural image datasets in the same conditions and achieves even better performance than some supervised methods when across to medical image domain. The code and models are available at https://github.com/leal0110/SIS.
The Exciting MTT-S Inter-Society Technology Panels at the 2022 EuMW [MTT-S Society News]

Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
Rewards [MicroBusiness]

I worked in an organization that had just finished a high-profile project. We had been challenged to develop a chipset for a new customer on an accelerated schedule. We hadn’t quite met all the milestones or specifications, but we were close. Overall, we were quite successful. Most importantly, the customer was pleased. I led one of the product development teams. At the start of the project, our general manager provided additional motivation. He suggested there would be cash rewards if we were successful. Once we had finished, a bonus budget was approved. That left us with the task of deciding how the rewards should be allocated.
Points-to Analysis for Context-Oriented JavaScript Programs

Static analyses, as points-to analysis, are useful to determine and assure different properties about a program, such as security or type safety. While existing analyses are effective in programs restricted to static features, precision declines in the presence of dynamic language features, and even further when the system behavior changes dynamically. As a consequence, improved points-to sets algorithms taking into account such language features and uses are required. In this paper, we present and extension of the point-to sets analysis to incorporate the language abstractions introduced by context-oriented programming adding the capability for programs to adapt their behavior dynamically to the system’s execution context. To do this, we extend WALA to detect the context-oriented language abstractions, and their representation within the system, to capture the dynamic behavior, in the particular case of the Context Traits JavaScript language extension. To prove the effectiveness of our extension, we evaluate the precision of the points-to set analysis with respect to the state of the art, over a set of context-oriented programs taken from the literature.
Quantum Computing

Today Quantum Computing is central in the advance engineering.This is because there is more demand for faster and more effective computer to treat huge amount of information in a very short amount of time.
The association between statistical learning and the development of second language grammar learning

Abstract Statistical learning (SL) is a cognitive mechanism that detects and extracts regularities from environmental input. SL is componential, varying across auditory/visual modalities and verbal/nonverbal domains (The verbal domain refers to communication through language, while the nonverbal domain encompasses communication without words, using cues like images and tones). The study explores the association between SL and second language (L2) grammar learning, considering its componential nature. Thirty‐four participants completed SL tasks and were trained with artificial grammar sequences over 5 days, followed by grammaticality judgment tasks. Visual‐nonverbal and auditory‐nonverbal SL mechanisms were implicated in the early stages of L2 grammar learning, while visual‐verbal SL played a role in the proficient phase. Results indicate modality and domain variation in the SL mechanism, supporting the multi‐component account. The findings contribute to understanding SL's role in L2 grammar learning and support the multi‐component account. They also have implications for L2 grammar learning.
Text Classification using LSTM

Text classification is an vital application in Natural Language Processing (NLP), which includes separating content into classes or categories.Text classification is a fundamental task in natural language processing that aims to automatically assign predefined categories or labels to textual documents.Over the years, deep learning approaches have shown remarkable performance in text classification tasks, surpassing traditional machine learning methods.This paper provides a comprehensive overview of text classification techniques using deep learning models, highlighting recent advances and state-of-the-art approaches.Deep learning approaches for text classification typically involve the use of neural networks, which are computational models inspired by the structure and functioning of the human brain.These models excel at automatically learning meaningful representations from raw data, making them well-suited for text analysis tasks.
Correction: Zhang, N.; Lin, C. The Image Definition Assessment of Optoelectronic Tracking Equipment Based on the BRISQUE Algorithm with Gaussian Weights. Sensors 2023, 23, 1621

There was an error in the original publication [...].
Prioritizing Career Management Activities on Youth Leaders’ Retention: A Spherical Fuzzy Analytic Hierarchy Process-Based Approach

As youth organizations play a vital role in nation-building, retaining elected officials is essential for organizations to maintain created value. Youth are distinct from previous generations in their attitudes, behaviors, and personalities, and retaining them requires a distinct management strategy. This study aims to provide decision-makers with a more effective and efficient instrument for evaluating the types of career management activities contributing to the retention of SK Chairpersons. This study uses a spherical fuzzy analytic hierarchy-based approach to examine factors and activities according to their importance. It was conducted at Talakag, Bukidnon, participated by the SK Federation with twenty-nine (29) SK Chairpersons. The results indicate that "leadership and Management" is the most important factor, that "supportive leadership" has a significant impact on their retention, and that "development-oriented career management activities" have the greatest influence on increasing the retention of SK Chairpersons. To accomplish more with youth programs and activities, the federation, under the supervision of the Barangay Council in each barangay, should prioritize Career Planning and Reflection, Performance Feedback, and Skill Development.
The Impact Of Climate Change On The Increase Of Flood And Landslide Disaster In Kuningan Regency, West Java Province

Kuningan Regency, which is one of the areas that prone to landslides and floods. Based on its geological and geographical conditions, Kuningan Regency is located on the slopes of Mount Ciremai so that this area has a high level of vulnerability to landslides and floods. This study aims to identify climate change with rainfall indicators that have an impact on increasing the incidence of floods and landslides in Kuningan Regency as well as disaster risk reduction efforts by the Regional Disaster Management Agency (RDMA) and the local community. This study uses a qualitative method with a descriptive approach, supported by data obtained from observations, interviews and literature studies. The results showed that climate change with high rainfall indicators had an effect on an increase in landslides and floods that occurred in Kuningan Regency. Disaster risk reduction efforts from the Kuningan Regency BPBD in tackling the risks of floods and landslides, namely by taking preventive action. RDMA Kuningan Regency has built public facilities such as shelter for refugees, signs for evacuation routes, early warning system, as well as strengthening disaster risk reduction forums in Kuningan District. The efforts that can be made by the community are by carrying out repairs, cleaning waterways, building cliff retaining walls and closing soil cracks
Importance of Feature Extraction in Sentiment Analysis Implementation

Twitter is a major online social networking website that has grown in popularity among millions of users. Sentiment analysis was employed by a number of companies to discover their customers’ mentalities and feelings regarding their products. Sentiment analysis is a technique for determining the intended meaning or polarity of tweets sent by Twitter users. As a result of the high volume of sentiment tweets, it is impossible to discern people’s overall perceptions without automatic sentiment analysis and classification. Numerous classifications of sentiment are emerging, with study concentrating on tweets. Five steps are used to analyse the sentiment data. The first phase is to collect data using the Twitter API, and the second step is to analyse and classify the dataset’s unique text data using natural language processing and text analytics. It’s a necessity because the dataset is so vast and includes so many slang terms and phrases. The second process is text preparation, which involves examining and extracting specific material as well as cleaning out non-textual components such as emoticons and numerical figures. Finally, sentiment analysis is performed on the extracted tweets, identifying emotion and inspecting the opinions. There is no objective communication, but individual expressions remain. Positive and negative sentiments are classified in the fourth phase of the algorithm. Sentiment analysis primary purpose is to turn unstructured text into useful information. The study’s tweets were subjected to sentiment analysis.In our dataset, the above five steps are handled by extracting a specific text feature and analysing it. Finally, we apply our suggested hybrid algorithm, which is based on ACO-PSO with SVM to classify sentiment in extracted data.
Effect of Kernel Learning in Unsupervised Learning for Clustering High Dimensional Databases

This paper reviews the effectiveness of kernel learning in unsupervised data analysis using clustering. Cluster analysis is an explorative data analysis tool that assists in discovering hidden patterns or natural grouping and has many effective applications in various disciplines. The unison of kernel learning with the objective of unsupervised clustering algorithms facilitates in recognizing non linear structures in high dimensional data containing outliers with heavy noise. The recent kernel clustering methods considered in this paper are the kernelized versions of K-Means, Fuzzy C-Means, Possibilistic C-Means and Intuitionistic Fuzzy C-Means. Computational complexities in kernel based clustering algorithms are quiet prominent and our objective is to understand the performance gains while using kernels in clustering. Experimental studies of this paper substantiate that kernel based clustering algorithms yields significant improvements over their traditional counterparts.
An Interactive Interface for Novel Class Discovery in Tabular Data

Novel Class Discovery (NCD) is the problem of trying to discover novel classes in an unlabeled set, given a labeled set of different but related classes. The majority of NCD methods proposed so far only deal with image data, despite tabular data being among the most widely used type of data in practical applications. To interpret the results of clustering or NCD algorithms, data scientists need to understand the domain- and application-specific attributes of tabular data. This task is difficult and can often only be performed by a domain expert. Therefore, this interface allows a domain expert to easily run state-of-the-art algorithms for NCD in tabular data. With minimal knowledge in data science, interpretable results can be generated.
Identification of the Leading Economic Sectors of Nigeria: A Typology Juxtapose of Nigerian Economy Vis-À-Vis African Economies

Objectives: The typology of Nigeria's economy was examined alongside those of West Africa, sub-Saharan Africa, and developing economies in Africa using time-series data from 1970 to 2018. Some issues remain and will continue to attract the attention of academics, activists, members of civil society, and practitioners to answer the question if Nigeria is capable of realizing its vast potential and maintaining its ability to grow.&#x0D; Methods: The data covered the values of nine sectors, each in the economy at constant prices, and the data were analyzed using inferential statistics.&#x0D; Results: Empirical evidence has shown that crude oil and solid minerals are the main core sectors of the Nigerian economy at the subregional level before Nigeria's economy shifts to agriculture and sectors of the third production chain, possibly the likely cause of Africa's emerging oil economies. Furthermore, concerning regional economies, agriculture and related activities, and the third sector of Nigeria's economy, it has the potential to grow faster. The possible reasons for this are that they are long-term sustainable, appropriate, and reliable alternative sources of revenue earning due to the global collapse in oil prices and insurgencies affecting the Nigerian economy's mineral resources and secondary production sectors. Besides, the secondary production chain needs to be strengthened to become potential, thus complementing the non-oil sectors of the Nigerian economy.&#x0D; Conclusions: For the well-being of Nigerians, the surest means is for policymakers to adhere to the optimal potential development of agriculture and related activities and the high-production sectors of the country's economy. To sustain the Nigerian economy, it must be diversified non-oil supported by agriculture and the allied sector because the appropriate way to strengthen the country's economy is to harness the economic activities that form the basis of the economic life of Nigerians and will be able to absorb the current human resources conditions.
Developing a Personality through Humanizing Professional Education

This study is a scientific justification for the hypothesis stating that the humanization of education contributes to developing a student's personality as a subject of educational activity and helps master knowledge and skills that boost the development of professional skills.The study includes 300 cadets from the Ivan Kozhedub Kharkiv National Air Force University in their 1-4 years of study.The study determines the main pedagogical support components of professional and personal self-development of military students.In the survey, 86.6% of military students positively assessed their professional and personal self-development, although senior students (46.8%) demonstrated less interest in this regard.The study reveals that modern higher education needs further humanization so that future specialists endorse and share humanistic values; the humanization of training at the military university results in positive changes in all criteria of professional and personal self-development of military students.It is necessary to provide three stages in the integral process of professional training: the motivation and value-oriented stage; the cognitive and activity stage; the reflexive and transformative stage.
Chapter 09 Introduction to Spreadsheets

Spreadsheets are electronic documents that help users to manipulate data. While word processors and email are probably the most used productivity applications, much of the world’s business runs on spreadsheets. This is because spreadsheets are the productivity applications designed to store numbers. Spreadsheets make it easy to visualize data and identify trends in that data. Any teacher tracking student grades will find it easiest to do so using spreadsheets. Student clubs will find it easiest to track contributions and expenses if they use spreadsheets. You may not use spreadsheets every day of your life, but your most valuable information is likely to be stored on spreadsheets.
Chapter 12 Introduction to HTML and Website Development

The Internet has been around since the early 1970s. In the early years, the Internet only supported plain text and using the Internet required users to learn the precise syntax of several computer commands. For example, to send an email to user jane@example.com required users to type the command: mail -s “mail subject” jane@example.com <<< “Mail body.” Most users found this too challenging and the only users on the early Internet were computer science researchers at universities.
Chapter 14 Database Applications

Databases are structured collections of data that can be efficiently searched and manipulated by computers. Databases are the underlying information store powering our modern technology- driven world. These databases are like digital vaults where you can keep data safely, and authorized users can read or update the data as needed. While you may not yet know what databases are, they touch your life daily. Your student records, your score history in online video games, your checkout history at the library are all stored in databases somewhere. In all likelihood, your personal information, including your home address, your phone number, and your school grades are stored in multiple databases.
Chapter 16 Cybersecurity

The National Institute of Standards and Technology (NIST), founded in 1901, is part of the U.S. Department of Commerce and supports the U.S. mission to promote today’s innovations and industrial competitiveness. This mission is accomplished by advancing measurements and standards in science and technologies that enhance economic security and quality of life. NIST, defines cybersecurity as:
Chapter 04 Researching and Using the Web

Never in history have we had so much knowledge stored, organized, and ready to be consumed with the click of a mouse or the tap of a button. If we step back for a moment and think about what is available on the web, we will be amazed by the information close at hand, all thanks to computers and the Internet. You can find the score of any weekend football game, price of grain in Nebraska, weather in Antarctica, tide schedules in South Australia, and the outcome of an election in Britain by typing your question in any search engine. If you are so inclined, you could have someone explain to you the Theory of Relativity, how to lay a brick wall, how to plan a party, or how to edit genes. If you want to learn to play the piano, understand road regulations in Florida, or learn about mountaineering, you can do it all from the comfort of your couch.
Chapter 07 Productivity Applications

In the chapters so far, we have talked about different types of computers and the important components of computers. We are now ready to introduce how these computers are used. Though computers were created to perform complex calculations such as the correct angle to position a gun to hit a specific target and to find the correct trajectory to return safely to earth from the moon, most users do not have such exotic uses for computers. Most folks own computers to work on assignments, prepare reports, calculate business profits, communicate with collaborators, or even just for entertainment, such as listening to music, watching videos, or playing games. In the next set of chapters, we will provide an overview of the most common categories of computer applications used in business. These applications help you create and process information and are called productivity applications. They include word processors, spreadsheets, presentations, and communication utilities such as email.
Interuniversity actions for the constitution of a network of material libraries

This paper aims to report on the actions taken to build and implement the interinstitutional partnership between the University of São Paulo (USP), Federal University of Rio Grande do Norte (UFRN), and Federal University of Espírito Santo (UFES) to establish a network of material libraries. To do so, it presents the relevance of the topic for Design and Architecture projects and its peculiarities; contextualizes the creation of the network's material libraries, as well as the actions and projects developed in partnership; and shows the similarities between the material libraries and the importance of establishing the network.