Reviewerly/exHarmony huggingface dataset:
Tracking Knowledge evolution, hotspots and future directions of breast cancer detection using deep learning: a bibliometrics review

In the medical field, it has been necessary to provide resources to detect ear-ly-stage diseases, including breast cancer. Deep learning is immersed in all aspects of medical image analysis, catapulting it as a possible dominant autonomous technology. In this systematic review, a total of 250 results were located, of which 40 were selected, for which a quantitative methodology with a descriptive basis was chosen. The objective of this bibliometric review is to analyze models in image processing for the early detection of breast cancer using deep learning. As result, digital mammography is the most effective method for detecting abnormalities in images. The research concludes that the application of CNN (Convolutional Neural Networks) is the most preferred choice of experts for medical image analysis due to its powerful pattern recognition and feature classifier.
Multimedia tool for the treatment of dyslexia and dyscalculia

Specific learning disorders constitute a varied group of alterations or difficulties that can cause problems not only at school, but throughout life. These disorders can be evidenced, in the first years of schooling, when children present problems in reading, writing and numerical reasoning.Once the diagnosis is established, the experts develop treatments that generally require specialized teaching materials that, due to the technological gap, are often scarce, expensive and difficult to access, especially in developing countries. This study is based on the analysis of the skills that children with learning disorders have in front of the computer, with this information the tool GARABATO which in English means scribble. was conceptualized and developed. It is a multimedia tool developed in Unity that offers the possibility that children and therapists can develop exercises as part of the treatment of dyslexia and dyscalculia, using drawing as a developmental catalyst. This paper describes the tool design process, its development and implementation; In addition, it offers a comparison of results with other teaching materials, measuring the progress of children in specific areas and competencies that are part of the treatment.
Network Embedding for Graphs with Node Contents

In Chapter 3, we introduced TADW algorithm to learn NE from graph structure and node features jointly. In many real-world networks such as social network and citation network, nodes have rich text content which can be used to analyze their semantic aspects. In this chapter, we assume that a node usually shows different aspects when interacting with different neighbors (context), and thus should be assigned different embeddings. However, most existing models aim to learn a fixed context-free embedding for each node and neglect the diverse roles when interacting with other vertices. Therefore, we present Context-Aware Network Embedding (CANE), a novel NE model to address this issue. CANE learns context-aware embeddings with mutual attention mechanism and is expected to model the semantic relationships between vertices more precisely. In experiments, we compare CANE with baseline models on three real-world datasets. Experimental results show that CANE achieves significant improvement than previous NE methods on link prediction and comparable performance on node classification.
Utilisation of Blockchain Technology for Better Health Outcomes during COVID-19

Blockchain holds immense potential in the field of healthcare as it can make decrease costs, mitigate inefficient practices and prevent patient data breaches. A blockchain is a continuous digital record of transactions that are anonymous to third party users and can be stored on multiple devices. If implemented in the healthcare sector, blockchains can improve outcomes of activities such as infection control and prevention for better management during a pandemic, and also for safer storage of patient data and electronic health records. The use of this technology can help in sharing cryptographically secured data between multiple users for better data assimilation between multiple organizations, especially during COVID-19. Moreover, this technological innovation can provide affordable patient-centred outcomes by sharing clinical data for the development of precision medicine. Apart from medical data management, it can also be used in drug development and clinical trials by making each step more reliable through time-stamping and recording all data inputs. Blockchain can also help keep a record to supply chain operations within hospitals and maintain records of medical equipment, drugs and other necessary stocks. Although blockchain is a promising technological advancement, it may still face several challenges, especially in low and middle-income countries. The usage of an internet source is essential for this advancement and it will take time for all healthcare bodies to transition to this technology by leaving paper-based data record-keeping approaches.
Interactive Gesture of Exhibition Hall Mobile Follow Service Robot’

In order to make gesture-based interaction more natural for users, this research aims to find suitable gestures for interaction between users and exhibition hall service robots. The research process is divided into two stages. The first stage conducts demand analysis and task definition to analyze user needs and tasks in different scenarios during the visit of the exhibition hall, thus to define the task set of the exhibition hall service robot gesture interaction. The second stage conducts two experiments. Experiment one carries out gesture inspiration. Participants are invited to design gestures for different tasks in each scene,therefore obtain the user-defined gesture sets. Experiment two conducts gesture evaluation tests. Experts are invited to select three candidate gestures for each task. In order to collect user preferences for candidate gestures, participants are asked to watch candidate gesture’s demonstration videos and score candidate gestures in terms of ease of use, comfort, matching and memorability The candidate gestures are sorted according to their scores. The most ideal gestures for human-computer interaction between exhibition hall service robots and users are obtained in different scenarios and tasks.
THE ORGANIZATIONAL AND PEDAGOGICAL CONDITIONS FOR THE FORMATION OF PROFESSIONAL COMPETENCES OF FUTURE SOFTWARE ENGINEERS UNDER THE SHORTENED CYCLE OF TRAINING AT UNIVERSITIES

The article reveals some aspects of the problem of organizing the professional training of future software engineers by educational programs of the first (bachelor’s) level with a shortened term of study. A number of scientific works are analysed and the author’s approach to solution of existing problems are grounded on this base. It is shown that the approaches to formulating the conditions of professional training at higher education institutions are well studied by Ukrainian scientists, but a systematic study of the conditions of study in the shortened term was not conducted. It is shown that the shortened cycle of professional training of future software engineers at universities involves the training of students enrolled at universities on the basis of a junior bachelor’s degree or junior specialist’s qualification level for training in the field of knowledge 12 “Information Technology”, which ends with a Bachelor’s degree, accompanied by the recognition and re-crediting of ECTS credits and has a shorter than standard term of study. The organizational and pedagogical conditions for the formation of professional competencies of future software engineers in a shortened cycle of training at universities are determined: the formation of students with a stable positive internal motivation to obtain higher education, professional activity in the specialty, training; systematic review and updating of the content of educational programs of the first (bachelor’s) level of higher education, professional training of the shortened cycle of future software engineers, taking into account current trends in the field of information technology and higher education; providing future software engineers with opportunities for the formation of individual educational trajectories in a shortened cycle of training at universities; application of expedient forms, methods and means of formation of professional competencies of future software engineers in the conditions of the shortened cycle of professional training at universities.
Region of Convergence for the Z -Transform

In the previous module we found the Z-transform of a number of sequences and in the process also looked at the region of convergence. In this module we will take a slightly more detailed look at the region ofconvergence and make an attempt to connect the region of convergence for the Z-transform of a discrete sequence with the Laplace transform of a continuous function.
Development of the hybrid MCDM model for cloud computing adoption strategic management

With the widespread use of distributed alternative energy sources, electric cars, energy storage systems, and technologies like Cloud Computing (CC), Big Data, and the IoT, energy management in CC contexts has developed. Concerns about the Energy Cloud's performance goals are presented in this fashion as a major development point (EC). The aims of this essay include identifying key FPVs and how they relate to issues in EC, as well as formulating an approach to overseeing the growth and maturity of EC settings. Through literature research, FPVs were identified as well as their influence on each other. It was determined that those FPVs were an important, secondary, motivational factor, and independent voters by using the AHP approach. An all-encompassing management model for EC is presented in the article, and it can be used as a compass for making strategic choices on technical, organizational, commercial, and regulatory matters. This model may be tailored to the specifics of the business landscape and its breadth.
Use of Internal Communication Channels and Employees Communication during COVID-19 Pandemic and Afterwards

The situation at the time of the pandemic COVID-19 and after­wards posed great challenges for companies as they had to adjust to a “new normal”. This situation posed a challenge to the use of internal communica­tion channels, employee engagement and satisfaction with the company. It is argued that the use of internal communication channels is an important factor in increasing employee engagement and satisfaction with the com­pany. The study aimed to investigate the use of communication channels and their impact on employees’ satisfaction with their jobs, the meaning­fulness of their work and their commitment to their company during the cri­sis COVID-19. An explorative qualitative and qualitative research approach was used to determine the use of communication channels and employee engagement during the COVID-19 pandemic and afterwards by Slovenian employees. The results of the statistical analysis show that there is a sig­nificant relationship between the use of communication channels and the satisfaction level of Slovenian employees. Finally, suggestions are made for organisational communication strategies in times of the pandemic.
Enabling statistics undergrads of developing countries to take their first steps toward data science through mobile applications

Data Science is a catchword today. Whereas developed countries have advanced significantly in this field, the developing countries are far behind. Keeping in view the importance of this discipline in today’s data-driven world, efforts are required to enable the statistics communities of developing countries to take their first steps toward data science. This paper focuses on undergraduate students and proposes the introduction of two 3 credit-hour courses on introductory data science in the BS Statistics programs of developing countries. As non-trivial proportions of undergraduate students in these countries do not possess laptops but do have access to smartphones and the internet, it is proposed that these courses use freely downloadable mobile applications that will need to be developed for this purpose. Such an initiative could be a significant first step toward the inculcation of data-science-related knowledge and insight in statistics undergrads of developing countries.
Analytical Study of Block Chain Testing and Tools used for Testing

This paper will explain the technology Blockchain in detail. It is a new concept which is being used in various fields to save information in less time and cost effective. This paper will also explain the types of block chain, advantages, testing strategies and tools used for block chain testing. It is a form of system in which the information or data is stored using blocks where each block has its unique data. The block will have all the information about that particular data.
Corner Point Detection for The Map of Kariah Kg. Bukit Kapar

Corner point detection are the important technique for many image processing applications including image enhancement, object detection and pattern recognition. The purpose of this study is to detect the corner points of a map of Kariah Kampung Bukit Kapar image by using Harris Corner Detector. Corner points in an image represents a lot of important information of the image. Detection of corner points accurately is significant to image processing, which can reduce much of the calculations. In this study, the initial technique is smoothing the image and extract the boundary of the image. Then, Harris Corner Detector is used to detect the corner points by considering the amount of corner point detection and run time processing. This study proposed the Harris Corner Detector which can detect 154 points with 12.9552 second.
A deep-learning approach at automated detection of electron-dense immune deposits in medical renal biopsies

Introduction: Identification of electron-dense immune deposits in electron microscopy (EM) images is integral to the diagnosis of medical renal disease. Deep learning has the potential to augment this process, especially in areas with limited resources. Objectives: Our study explores the feasibility of applying deep learning to detect electron dense immune deposits in electron microscopy images from medical renal biopsies. Patients and Methods: EM images (N=900) from native and transplant kidney biopsies were processed into 4530 tiles (512 x 512 pixels). These tiles were reviewed and classified into one of three categories: deposits absent, deposits present, and indeterminate. This classification resulted in 1255 images with consensus agreement for deposits present and deposits absent. These 1255 images were then used to train a machine learning model, using 1006 images for training, and 249 images for testing. Results: The overall accuracy on the test data was a competitive 78%, and the F1 scores for deposits absent and present was 0.76 and 0.79, respectively. Conclusion: This study demonstrated the feasibility of creating and applying a machine learning model that performs competitively in identifying electron dense deposits in EM images.
Design and Development Prototype of Indigenous Robotic Radio Pharmaceutical Dispenser

Working in the nuclear medicine department leads to a possibility of long term low level radiations exposure. Imaging technologies like positron emission tomography (PET) and single photon computer tomography (SPECT) requires radiopharmaceuticals to be administered into the patients. There is radiation exposure to the staff especially when they dispense the required radioactive medicine doses with their hands into the syringes from the source vial. Also, during the process there is a possibility of spillage of the radioactive pharmaceuticals which could lead to radioactive contamination. This project aims at automating the steps involved in dispensing the unit doses into the respective syringes from the source vial and placing it in a lead shielded container. This will be achieved by developing a robotic application to carry out the activities. This will reduce the time for which the nuclear medicine staffs is exposed to the radioactive syringe. The only time when he will be exposed will be when it is injected into the patients.
CosmoSys-Req: a free open-source requirements management tool

Text-based requirements management tools are widely used in engineering today. The concept behind it is quite simple, but this simplicity does not mean that these tools are affordable. In most cases, the cost of a requirements management tool license is similar to the cost of a CAD software license, the latter pertaining to a much more complex software tool. At cosmoBots.eu we have developed a plugin for a free and open project management tool (Redmine) that turns it into a powerful requirements management tool, including automatic and instant hierarchy and dependency diagrams, import/export from/to spreadsheets, full interoperability with other tools using the REST API, also including role-based lifecycle management and reporting. Several projects in IAC (EST, MICAL, NRT...) are officially using cosmoSys-Req to manage their requirements, and other projects or institutions (GTC, IACTEC...) are currently evaluating their use.
Solar spectral irradiance reconciliation of SORCE and TSIS1 SIM

The Solar Radiation and Climate Experiment (SORCE) recorded daily Solar Spectral Irradiance (SSI) measurements from 2003 to 2020, overlapping with the Total and Spectral Irradiance Sensor (TSIS1) for ∼2 years (Mar. 2018 - Feb. 2020). We use data from the Spectral Irradiance Monitors (SIM), spanning 704 days and 554 time-matched observations, to compare absolute irradiance calibrations in a truly unique space-based UV-IR spectroscopic overlap study. This 200–2400 nm comparison was conducted during the Solar Cycle 24 minimum, so observed differences are likely instrumental. We find peak-to-peak (maximum) absolute scale differences of 12% with a mean fractional difference of 0.7 ± 2.9%. A multiplicative scale correction factor (STICR) has been developed to reconcile the TSIS1 and SORCE SIM irradiance differences. Applying this correction to the SORCE-SIM V27 dataset, we provide a re-calibrated dataset known as the TSIS1-SIM Adjusted Values (TAV). We discuss the challenges in the creation of STICR and TAV, and lessons learned about using temporally overlapping space observatories to provide a continuous solar observation record.
FRAUD PREVENTION AND DETECTION SYSTEM IN NIGERIA BANKING INDUSTRIES

Fraud is on the rise as a result of the advent of modern technology and the global superhighways of banking transactions, resulting in billions of dollars in losses worldwide each year. Although fraud prevention technologies are the most effective method of combating fraud, fraudsters are flexible and will usually find a way around them over time. We need fraud detection approaches if we are to catch fraudsters after fraud prevention has failed. Statistics and machine learning are effective fraud detection technologies that have been used to detect money laundering, e-commerce credit card fraud, telecommunications fraud, and computer intrusion, to name a few. The program is simple to use, and anyone with permission can use it. The importance of computer technology has expanded as it has advanced in all areas of human endeavor. &#x0D; Keywords: Fraud Detection, Fraud Prevention, Banking Industries, Telecommunications.
Le forme della solitudine: isolamento, co-isolamento, noia e sonno

Man avoids solitude through divertissement because he is afraid of what staying alone entails: thinking about himself (Pascal). Furthermore, man seeks isolation in order to gain ambition and reputation (Montaigne). However, solitude is really different from isolation and it is a fundamental emotional condition. Heidegger’s analysis of the forms of boredom adapts to and relates to the specific dynamic of solitude, showing that it is a part of human being’s structure despite his experience appearing controversial. Even if man lives in modern apartments and one-room apartments, he is in a condition of co-isolation (Sloterdijk) that differs from solitude. There is a space solitude (Foucault) that involves – or can involve – human isolation, but we only experience essential solitude in sleep (Merleau-Ponty, Blanchot, Perec) where man detached himself from himself and he is really alone, from himself and the others. The forms of solitude lead the analysis towards impersonal activity.
The Effect of Accountability and Transparency on Effectiveness of Government School Operational Assistance Funds Management

Implementation the management of the School Operational Assistance fund from the Government at SMAN 1 Marga still encounters obstacles. The problem that occurs is the lack of implementation of accountability and transparency which results in a lack of participation, and involvement of educational stakeholders, including supervision from the community. This study aims to empirically prove the effect of accountability and transparency on the effectiveness of School Operational Assistance funds management from the Government at SMAN 1 Marga with 60 respondents. The analysis technique used is multiple linear regression analysis. Based on the results of the analysis, it is found that accountability and transparency have a positive and significant effect on the effectiveness of managing School Operational Assistance funds from the Government.
General Optimization Framework for Recurrent Reachability Objectives

We consider the mobile robot path planning problem for a class of recurrent reachability objectives. These objectives are parameterized by the expected time needed to visit one position from another, the expected square of this time, and also the frequency of moves between two neighboring locations. We design an efficient strategy synthesis algorithm for recurrent reachability objectives and demonstrate its functionality on non-trivial instances.
Automatic Recognition of Emotional Subgroups in Images

Both social group detection and group emotion recognition in images are growing fields of interest, but never before have they been combined. In this work we aim to detect emotional subgroups in images, which can be of great importance for crowd surveillance or event analysis. To this end, human annotators are instructed to label a set of 171 images, and their recognition strategies are analysed. Three main strategies for labeling images are identified, with each strategy assigning either 1) more weight to emotions (emotion-based fusion), 2) more weight to spatial structures (group-based fusion), or 3) equal weight to both (summation strategy). Based on these strategies, algorithms are developed to automatically recognize emotional subgroups. In particular, K-means and hierarchical clustering are used with location and emotion features derived from a fine-tuned VGG network. Additionally, we experiment with face size and gaze direction as extra input features. The best performance comes from hierarchical clustering with emotion, location and gaze direction as input.