Reviewerly/exHarmony huggingface dataset:
Breast Cancer Analysis using Convolutional Neural Network

Breast cancer is one of the crucial reasons for deaths in females. It affects one out of eight females worldwide. Breast cancer can be detected in early stages with the help of mammography. It is possible due to advancement of science and medical field; more reliable and accurate techniques have emerged to fight against this disease. Nowadays deep learning approach is being used by radiologists which help them make accurate diagnosis of breast cancer. This research contains a novel way of breast cancer detection using convolutional neural network and mammogram imaging system, to accurately classify mammogram image of tumor into benign (noncancerous) and malignant (cancerous). A proposed custom model is created which Is in resemblance with the VGG 16 model. Several mammogram images are used to carry out preprocessing. In order to get good results, we use preprocessing methods such as shearing, enlargement and equalizing image data are used. Feature extraction is done through CNN and classification is performed in fully connected network. The outcome described here demonstrates that the accuracy rate of the proposed automated method is better than other existing methods. Experimental results show the accuracy of the proposed method is 99.45% on training data. Classification report gives the prediction accuracy of 99% with good precision, recall and Fl score.
Central Bank Digital Currency and Cryptocurrency in Emerging Markets

Blockchain technology has opened up the possibility of digital currency, smart contracts and much more applications including the launch of central bank digital currencies (CBDC). However, literature about the effect of CBDC with the presence of cryptocurrency for an emerging market economy seems to be left behind. In this paper, we introduce a New Keynesian - Dynamic Stochastic General Equilibrium (NK-DSGE) model to examine the implications of CBDC and cryptocurrency in an open economy for emerging markets. In our model, cryptocurrency is implemented as a form of deposit in banks where bankers can also receive deposits from abroad. Lastly, CBDC is introduced as a payment and saving instrument. We find that cryptocurrency has a crucial role in banking sectors and a significant effect on the dynamic of foreign debt which is deeply important for emerging markets. We also conduct optimal monetary policy under different scenarios. Hence, we uncover that a flexible rate in CBDC can affect the responses of the monetary rate and can reinforce the conventional monetary policy to achieve the central bank's targets.
Geology and Geochemistry of Alkaline Syenite Arrays of the Sydo-Erba Depression (Minusinsk Trough)

This research work is devoted to the study of the chemical composition of the alkalinesyenite massifs of the Sydo-Erba depression based on the available geological and geochemical data. For about 100 years, the massifs have attracted the attention of various scientists not only from the practical point of view, as possible sources of aluminum raw materials, but also as objects for research, in particular, to determine the shape of the bodies of nepheline syenites occurring in them and models of the formation of alkaline magmas. The studied intrusions are composed mainly of syenites and alkaline syenites, as well as a few bodies of nepheline syenites. The paper will present and compare the geochemical data of alkaline nepheline syenites (foyaites) and nepheline syenites (tensbergites), which compose such massifs as Saibarsky, Burovsky, Vysokoy, Kosmatoy, in order to identify the influence of granite-diorite complexes on their composition. To achieve this goal, it is supposed to study the geological and petrographic features of the structure of the massifs, as well as to analyze the chemical composition of rocks of geochemical data presented in the form of contents of petrogenic and rare elements.
What Do You See? Transforming Fault Injection Target Characterizations

In fault injection attacks, the first step is to evaluate the target behavior for various fault injection parameters. Showing the results of such a characterization (commonly known as target cartography) is informative and allows researchers to assess the target’s behavior better. Additionally, it helps understand the performance of new search methods or attacks. Thus, publishing obtained results is essential to provide relevant information for reproducibility and benchmarking, improving state-of-the-art results and general security. Unfortunately, publishing the results also allows malicious parties to reverse engineer the information and potentially mount an attack easier. This work discusses how various transformations can be used to occlude sensitive information but, at the same time, still be useful for interested researchers. Our results show that even simple 2D transformations, such as rotation, scaling, and shifting, significantly increase the effort required to reverse engineer the transformed data but maintain the interesting data distribution. Consequently, this work provides a method to allow publishers to share more data in a confidential setting.
Video Deraining via Temporal Discrepancy Learning

Learning-based video deraining has attracted much attention and shown superior performance. However, existing methods mostly depict the temporal correspondence between the consecutive frames implicitly, leading to insufficient temporal exploration so that performing badly in unseen real-world scenarios. To settle these issues, we develop a novel Temporal Discrepancy Learning (TDL) framework to provide interpretability in modeling temporal correspondence and improve robustness in real-world scenarios. To be specific, we define a new explicit spatio-temporal video deraining model by reformulating the task from a set representation perspective. The model describes the intuitive correspondences between different frames in terms of rain regions. Inspired by this model, we further construct a TDL, that firstly learns the temporal correlation under temporal discrepancy sequences and attention mechanism, then recovers the clear frame by performing a spatial-aware ensemble deraining process. Performance evaluations on various scenarios verify our excellence compared with advanced video deraining networks.
Unconditional Multicolor Teleportation

We demonstrate the first evidence of teleportation of a coherent state between distinct wavelengths of the field, using a protocol for unconditional teleportation in the continuous variable domain.
Handling Exceptions

When you write code for an application, you expect that it will execute as per your plan, but you will often encounter sudden surprises. These surprises may occur through some careless mistakes. For example, you have implemented the wrong logic, or you have ignored some loopholes in the code paths of the program. However, many of the failures are beyond the control of a programmer. Developers call these unwanted situations exceptions. Handling these exceptions is essential when you write an application. This chapter focuses on exception handling and covers the following topics:
Secure access control for cloud-based tele medical system

Recent technological advancements are offering users new and easier ways to access healthcare services. With the advent of high-speed networks, low cost storage, inexpensive telecommunication systems, patient monitoring systems, cloud computing, telecare medical information system (TMIS) is becoming a reality. Due to the advantage of tele-healthcare medical system, we are reaching directly to the patients' home over internet or mobile networks and data is stored as electronic medical records (EMR). The major challenge here is ensuring secure access of communication data by patients and doctors, for this we need a secure and efficient way of user access control, so that attacker cannot impersonate the user or medical server. The confidentiality, integrity and availability need to be ensured. Also, the authentication process should be user friendly so that even elderly patients can use it easily. Medical data includes medical images which might be tampered by intruders. Our aim is to overcome the limitation of present market scenario and provide the efficient way of communication between user and doctor with help of cloud-based system.
Research on Detection Method of Daily Staff Work Management Violation based on Convolutional Neural Network and SSD Algorithm

Due to high costs and low coverage, manual inspectors cannot meet the needs of normalized daily staff work management. This paper proposes a detection method for daily staff work management violations based on human behavior recognition in the video to deal with this problem. Based on the convolutional neural network and SSD algorithm, the method detects the clothing of personnel, the use of items, and the possession of police equipment in the video to achieve the purpose of daily staff work management violations. The core of the research uses the VGG16 network as the basic network structure of the SSD algorithm and conducts training on violation samples. The loss function is further replaced by focal loss, and a better network model is obtained to improve the detection performance. This paper provides the technical basis for establishing a scalable technical framework for violation detection and technical support for the intelligence and normalization of daily staff work management.
Research on the application of computer artificial intelligence technology in feedback teaching of English

Artificial intelligence technology has begun to enter the field of education in recent years. Artificial intelligence can promote English teaching in many ways, artificial intelligence provides relatively objective and instructive feedback, artificial intelligence helps teachers teach, artificial intelligence helps students improve English skills, and can promote the enthusiasm of teachers and students in teaching and learning. In the process of second language acquisition teaching, it is inevitable for learners to make mistakes in grammar, vocabulary and even language communication. English learners are easily affected by the negative influence of their mother tongue thinking, and learners are prone to negatively transfer the acquired rules to English usage, resulting in deviations in English comprehension and usage errors. We must fully recognize the practical significance of computer artificial intelligence technology for feedback teaching in English second language acquisition for teachers to correctly guide students in language learning.
Tools for Activating Data Marketplace (2)

Not only the workshop-styled discussion environment is necessary but also a mechanism to support efficient communication for data utilization by systematizing it. We developed the Web systems for activating the data marketplace. In this chapter, we present five Web applications: DJ Store, VQ, TEEDA, Web-based IMDJ, and HRF, and explain their use cases. In addition, we develop an integrated system to reduce workshop preparation load and support data collaboration for users in remote areas on the Web and discuss data utilization cases regarding the interoperability of the different systems and report the results of practical use with the workshop’s operation scheme.
Using innovative teaching methods in computer science lessons

The purpose of the study is to determine the features and prospects for the use of innovative teaching methods in computer science classes. In the process of research it was found that the value of innovative technologies is that they simultaneously reflect not only a practical problem, but also actualize a certain set of knowledge, which is important to learn in solving this problem, as well as successfully combine learning and analytical activities. Scientific novelty of the received results consists in a detailed description and formalization of the possibilities of modern digital technologies, advanced pedagogical practices and communication techniques for teaching schoolchildren at the lessons of computer science and the formation of their actual skills of the XXI century. As a result, it was found that innovative teaching methods allow students to accumulate experience of using computer technology, as well as develop information culture and information competence.
ACOUSMA: UBIQUITOUS &amp; INTELLIGENT AUDITORY DISPLAYS

ACOUSMA is a platform based on micro-services architecture developed to allow effortless enhancement of Ambient Intelligence (AmI) environments with personalized auditory displays.ACOUSMA consists of two system packages designed for interaction with three groups of users: auditory display experts, AmI application developers and AmI environment inhabitants.Auditory display experts can generate, upload and share their designed auditory representations using an intuitive Content Management System (CMS).AmI application developers with no prior knowledge on the field can integrate personalized auditory displays in any AmI environment with minimum effort using a simple application programming interface.An intelligent module was developed to adapt and grow to user preferences in real time, enabling conversion of semantic information into meaningful, user-tailored auditory displays.Finally, AmI environment inhabitants experience auditory displays that occur in the right place at the right time and are adjustable to their preferences.Usability evaluation from both experts and final users has shown that the target user groups can benefit from using ACOUSMA as a platform to store and share auditory representations, as well as to provide personalized, meaningful and context-sensitive auditory displays.Moreover, users could successfully and efficiently communicate their needs and adjust auditory displays using the intelligent module.
Legislative Prevention of New Financial Technologies Threats

The subject of the study is the problem of legal prevention of the use of computer and telecommunication technologies by intruders in new financial remote services in Russia. An increase in the variety and volume of attacks is inevitable, given the desire of scammers to obtain personal and confidential information. In recent years, Russia has made significant progress in improving its infrastructure responsible for information security. The article is a comprehensive analysis of Russian legislation. The analytical review of various directions of development of the Russian federal legislation in recent years aimed at preventive counteraction, elimination of a number of conditions and prerequisites of cybercrime in the financial sphere is presented. Particular attention is paid to the jurisdictional aspects of Russian legislation. The government needs to make thorough preparations to counter a range of unwanted cyber events, both accidental and intentional. There are significant risks of local attacks and losses as a result of compromising computer and telecommunications services. The conclusions contain final proposals for further improvement of legislation taking into account foreign and international experience. The main conclusions of the study are the productivity of identifying the strategic prevention direction in preventive activities – preventive identification and elimination of gaps in the regulatory framework, as well as technical and organizational vulnerabilities that make possible various types of attacks and "schemes" of cybercriminals in the financial sphere.
Search-Based Exploration and Diagnosis of TOAD-GAN

Generative Adversarial Networks (GANs) have been used with great success to generate images. They have also been applied to the task of Procedural Content Generation (PCG) in games, particularly for level generation, with various approaches taken to solving the problem of training data. One of those approaches, TOAD-GAN (Token-Based One-Shot Arbitrary Dimension Generative Adversarial Network) (Awiszus, Schubert, and Rosenhahn 2020), can generate levels based on a single training example and has been able to closely reproduce token patterns found in the training sample. While TOAD-GAN is an impressive achievement, questions remain about what exactly it has learned. Can the generator be made to produce levels that are substantially different from the level it has been trained on? Can it reproduce specific level segments? How different are the generated levels? We investigate these questions and others by using the CMA-ES algorithm for Latent Space Evolution. To make the search space feasible, we use a random projection in latent space. We propose the investigation undertaken here as a paradigm for studies into what machine-learned generators have actually learned, and also as a test of a new method for projecting from a smaller search space to a larger latent space.
Design and Features of Advanced Decision Support Systems

The design of systems important to the safety of nuclear power plants (NPPs) is based on compliance with regulatory documents’ requirements and ensuring the object's reliable operation throughout its entire life cycle. One of the aspects of increased requirements for the reliable and safe operation of NPP process control systems is perfecting human-operator information decision support systems. In the paper, an approach is considered in which the operator support information system kernel is a flexible modeling program complex, which, depending on the tasks being solved, can be used as a digital twin or a digital shadow. The digital twin furnishes the past state of the product, the current state, and probable future states, which is especially significant for its use. The paper also discusses methods for assessing the proximity of a physical object and its digital twin based on a consistent and normalized measure of dependence of random vectors.
Augmented Reality and 3D Balance Learning

The augmented reality is presented as an opportunity to help understand students with attention problems. The objective of this research is to determine the incidence of augmented reality with academic performance and the satisfaction of learning 3D (Third dimension) balance in the training project of Vector Mechanics of the Industrial Engineering career at the Indoamerica Technological University. The problem identified in this investigation was the loss of interest in students when solving problems with three-dimensional systems, unfortunately the textbooks present their printed diagrams in two dimensions, which means, the student cannot locate himself correctly in the system, making errors in his analysis and calculations. This research has a quantitative approach with a positivist and socioformative paradigm. The applied methodology obeys to create didactic material of three-dimensional systems with Computer-Aided Design (CAD), and presented in augmented reality with collaborative work of the students. We used the Pearson's correlation analysis to verify the correlation, obtaining a low coefficient of 0.204 between the variables analyzed. As a result, the hypothesis “The use of augmented reality relates academic performance with the satisfaction of learning Equilibrium 3D in students” was verified.
Pengaruh Transformasi Media Pembelajaran Siswa pada Masa Pandemi Covid-19

The orientation of this research is to explain and analyze changes in learning media in learning practices before and after the covid19 pandemic. Media is one of the supports in the learning process. This study uses a qualitative field survey method. Observation guidelines were enforced by implementing strict health protocols at the study site. And the interview method used in this research is an unstructured interview technique (detailed interview). The results of the study, the success or failure of the learning process is very dependent on the media used. Education is one aspect of being used as a forum to shape the character of the nation's children. Therefore, in the educational process an educator must be able to master various learning media, at SMP Negeri 07 Seluma in the learning process before the covid-19 pandemic, using focus media, whiteboards and a set of learning equipment commonly found in classrooms, using the learning media has greater effectiveness. In conclusion, the use of learning media before the covid-19 pandemic provided greater learning effectiveness because teachers were able to freely provide assessments that were assessed from cognitive, affective and psychomotor aspects to students than the learning media used during the current covid-19 pandemic, which used media social media as online learning media.&#x0D; &#x0D; Keywords: Learning Media, Covid-19 Pandemic
On Social Consensus Mechanisms for Federated Learning Aggregation

The possibility of training Machine Learning models in a decentralized way has always been a challenge when maintaining data privacy and accuracy. Federated Learning stands out for allowing accurate inference by combining multiple models trained on their data locally. It uses the strategy of local learning, where each node learns from its individual data, and then groups the learned models into a single, unified one, thus preserving data privacy. However, this raises some points of attention, such as ensuring security, the accuracy of the aggregate model, and communication optimization between federated nodes. This article analyzes aggregation techniques based on Game Theory in the aggregation stage in federated learning networks, aiming to validate the exploration of new concepts and contribute to the evolution of future research. We implemented three mechanisms of the decision by consensus in the aggregation of the models, including the well-known majority voting, as well as two other mechanisms never previously used in the context of FL, namely weighted majority voting and Borda count. To properly validate, we proposed a reference pipeline based on the CIFAR-10 dataset. The proposed benchmark partitions and allocates the dataset into a number of clients, and sets up a common pipeline for them. Such a pipeline allows one to train multiple clients and then test different aggregations in a fairly, reproducible way. Moreover, the proposal increased the precision of individual inference by more than 50%, showing efficiency in using non-trivial consensus mechanisms, such as weighted majority voting.
Systematic Review and Meta-Analysis of Tuberculosis in Animals in Nigeria

Animal tuberculosis (TB) is a contagious and chronic disease caused by mycobacteria belonging to the Mycobacterium tuberculosis complex (MTBC) in domestic and wild animals. MTBC strains infection has been confirmed in many animal species in Nigeria, including captive wildlife, cattle, dromedary camels, goats, and pigs. Despite widespread infection and the potential impact of the disease on public health, active surveillance and control strategies are absent in Nigeria. This study aimed to conduct the first comprehensive meta-analysis to assess the distribution of tuberculosis and analyze the potential moderators of infection in animals in Nigeria. Eligible studies (sixty-one [61] prevalence and seven [7] case reports) were retrieved and included in the analysis. The analyses showed an overall pooled TB prevalence of 7.0% (95% CI: 6.0 – 8.0) comprising of infection distributed in cattle (8.0%, 95% CI: 7.0 – 8.0), goats (0.47%, 95% CI: 0 – 1.2), sheep (0.27%, 95% CI: 0.14 – 0.46), camels (13.0%, 95% CI: 0 – 47), and wildlife (13.0%, 95% CI: 9 – 16) respectively. The occurrence of infection was significantly moderated by the publication periods, geographical location, sample size, and detection methods. TB prevalence was heterogeneous across several predictors, with the year of publication exhibiting a higher rate (46%) of the detected heterogeneity. These findings should provide policy-relevant information to guide the design and establishment of prevention and control measures amenable to the local situations in Nigeria.Funding Information: None.Declaration of Interests: The authors declare that there is no conflict of interest regarding the publication of this paper.