Reviewerly/exHarmony huggingface dataset:
Neural Architecture Search Using Metaheuristics for Automated Cell Segmentation

Deep neural networks give successful results for segmentation of medical images. The need for optimizing many hyper-parameters presents itself as a significant limitation hampering the effectiveness of deep neural network based segmentation task. Manual selection of these hyper-parameters is not feasible as the search space increases. At the same time, these generated networks are problem-specific. Recently, studies that perform segmentation of medical images using Neural Architecture Search (NAS) have been proposed. However, these studies significantly limit the possible network structures and search space. In this study, we proposed a structure called UNAS-Net that brings together the advantages of successful NAS studies and is more flexible in terms of the networks that can be created. The UNAS-Net structure has been optimized using metaheuristics including Differential Evolution (DE) and Local Search (LS), and the generated networks have been tested on Optofil and Cell Nuclei data sets. When the results are examined, it is seen that the networks produced by the heuristic methods improve the performance of the U-Net structure in terms of both segmentation performance and computational complexity. As a result, the proposed structure can be used when the automatic generation of neural networks that provide fast inference as well as successful segmentation performance is desired.
HDR Production - Tone Mapping Techniques and Round-Trip Conversion Performance for Mastering with SDR and HDR Sources

Where content producers had to make drastic choices when allocating the limited contrast and colors in SDR, HDR offers them the possibility to show more - meaning telling stories with greater freedom and flexibility. — However, while future-proof, HDR content presents a challenge of its own: The wide variation in capabilities among HDR displays conflicts with the desire to have any HDR or SDR production play optimally on any display, HDR or SDR, regardless. — The general solution to this issue is tone mapping, either tone compression, which maps from HDR to a lower-luminance HDR or SDR, or tone expansion, which maps from SDR or HDR to a higher-luminance HDR. — Though the trend is to produce the highest quality content in HDR, legacy content is ever-present, and lots of new content is still produced in SDR. Mixing feeds in production requires tone expansion for SDR content to match the HDR. Distribution paths can then require tone compression back to SDR without degradation. In this context, a perfect SDR - HDR - SDR round-trip conversion is required to preserve artistic intent for all content. Further, in a live context, such tools need to be automatic and low latency. — This paper discusses various tone mapping techniques, including static solutions such as look-up tables and dynamic solutions based on metadata. The relative advantages are considered and performance of each is analyzed. Particular attention is paid to the round-trip performance, which is important to content producers mastering both SDR and HDR sources yet wanting to exploit the full range of storytelling capabilities an HDR stream can offer.
Static hand gesture recognition using multi-dilated DenseNet-based deep learning architecture

Hand Gesture Recognition (HGR), a Human-Robot Interaction (HRI) create user interfaces to enhance safety and control. Several approaches are introduced to recognize static hand gestures effectively, however the classification efficiency suffered due to various lightings like natural light, artificial light and dark rooms increases the difficulty of accurate HGR. To tackle these problems, a Multi-Dilated Convolution-based DenseNet (MDCDN) architecture, a combination of multi-dilated convolution and DenseNet is proposed which extracts the features automatically. Finally, the benefits of high-level deep learning techniques are leveraged for the gesture recognition from hand. Python is used for architecture evaluation. The outcome of proposed is estimated in terms of accuracy, recall, F-measure, precision, etc, using ASL, ISL, Massey and HSR real datasets. Each dataset contains huge amount of gesture classes and their pictures have an equal amount of uniform and complex backgrounds. The proposed results are promising and provide better performance than existing methods.
Robust and Scalable Network Monitoring System Using Apache Spark

Monitoring and restricting students' online activities have a lot of possibilities. A robust state-of-the-art network monitoring system and a proxy server architecture can be built to restrict access, monitor activity, reduce latency and increase throughput. In this paper, we have proposed a monitoring system and proxy servers that are built with scalable technologies. A prototype is built with four proxy servers and a central system that includes a Spark cluster and a Kafka cluster hosted on a single node with three Kafka brokers and one Topic with three partitions. The proposed architecture is very user-friendly, with both the API and the Dashboard hosted on the same server using application dispatching.
Stain color translation of multi-domain OSCC histopathology images using attention gated cGAN

Oral Squamous Cell Carcinoma (OSCC) is the most prevalent type of oral cancer across the globe. Histopathology examination is the gold standard for OSCC examination, where stained histopathology slides help in studying and analyzing the cell structures under a microscope to determine the stages and grading of OSCC. One of the staining methods popularly known as H&E staining is used to produce differential coloration, highlight key tissue features, and improve contrast, which makes cell analysis easier. However, the stained H&E histopathology images exhibit inter and intra-variation due to staining techniques, incubation times, and staining reagents. These variations negatively impact computer-aided diagnosis (CAD) and Machine learning algorithm’s accuracy and development. A pre-processing procedure called stain normalization must be employed to reduce stain variance’s negative impacts. Numerous state-of-the-art stain normalization methods are introduced. However, a robust multi-domain stain normalization approach is still required because, in a real-world situation, the OSCC histopathology images will include more than two color variations involving several domains. In this paper, a multi-domain stain translation method is proposed. The proposed method is an attention gated generator based on a Conditional Generative Adversarial Network (cGAN) with a novel objective function to enforce color distribution and the perpetual resemblance between the source and target domains. Instead of using WSI scanner images like previous techniques, the proposed method is experimented on OSCC histopathology images obtained by several conventional microscopes coupled with cameras. The proposed method receives the L* channel from the L*a*b* color space in inference mode and generates the G(a*b*) channel, which are color-adapted. The proposed technique uses mappings learned during training phases to translate the source domain to the target domain; mapping are learned using the whole color distribution of the target domain instead of one reference image. The suggested technique outperforms the four state-of-the-art methods in multi-domain OSCC histopathological translation, the claim is supported by results obtained after assessment in both quantitative and qualitative ways.
PROFORM - BEST PRACTICES IN PROFESSIONAL AND TRANSVERSAL COMPETENCES DEVELOPMENT FOR TEACHING IN INCLUSIVE SCHOOLS

The main objective of PROFORM is to develop the professional and transversal competences of the teaching staff and the school managers at the level of the counties, to increase their motivation and stability in their position, in order to ensure the quality of the education. This paper describes a selection of the best practices and opened sessions proposed in order to fulfill the main objective of PROFORM. The main tracks of the material are connected to the following topics: information technologies to access, the impact of new IC technologies for teaching, learning and evaluation, STE(A)M educational requirements and performability, open educational resources developed according to the ADDIE-SAMR methodology, and different aspects of computational intelligence (fuzzy reasoning, nature inspired computing, spatial reasoning, connectionism and distributed computing, neutrosophic computing etc.). It is important to mention that educational items refer to z-generation and new challenge is described for xy-generation. There are no results available on z-teachers for z-students. Such new aspects will be considered for the next 10 years when more projects will be developed for Z-teachers. As case study, results are provided for educational programs in Giurgiu County, along six modules selected from the list presented below and full education stages. The main modules are: (1) Information Processing, Communication (ICT) and e-Learning technologies in education; (2) Methods and techniques for optimizing teaching activities; (3) Innovative methods and techniques for evaluating school results; (4) Cognitive behavioral coaching to optimize professional performance; (5) School-family-community partnership for school inclusion; (6) Career management through personal and professional development; (7) Student's knowledge and assessment of learning progress; (8) Differentiated training - the theory of multiple intelligences; (9) Evaluation adapted to the individual particularities of the beneficiaries of education; (10) School management for institutional capacity development; (11) Educational projects and programs; (12) Performance and motivation in the school organization.
E-LEARNING PROCESS THROUGH CLOUD FACILITIES

e-Learning platforms help users (students / learners, tutors / instructors and administrators) aiming to offer them the information they need in order to develop their educational activities at the right time, following an appropriate format and in the location they desire. Along with the development of Internet, the need for virtual systems that would allow users to migrate data, e-mails, applications etc. has emerged, these virtual systems becoming the Cloud Computing technology. The current paper highlights the results of implementation of a Cloud system dedicated to citizens, public institutions and academia. ICIPRO project (Cloud Infrastructure for Public Institutions in Romania) was implemented at ICI Bucharest with Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Open Data and Virtual Library. So far, 32 institutions have been affiliated and are using this Cloud infrastructure, including 4 libraries and 5 universities. The logical architecture of the ICIPRO Cloud system presented in this paper meets the highest business requirements, such as security, availability, ease of administration and resource optimization. In order to accomplish this, the system has been divided into several subsystems, such as: the Infrastructure Management Subsystem that relies on a suite of applications required to manage and monitor the virtualization platform, the dedicated server subsystem where powerful servers can meet requirements specific to mission critical environments, with on-the-fly activation / deactivation of Predictive Failure Analysis (PFA) enabled processing kernels, 2 high-capacity subsystems consisting of high-density servers designed for Cloud Computing environments, that rack up to a high processing capacity and provide high performances, compared to power consumption.
Sensitivity analysis of criteria weights in geodiversity assessment of the Karkonosze National Park, Poland

The geodiversity assessment is particularly important in the case of areas belonging to critical zones, especially in the mountain regions. Recognizing the parts of a territory that are the most diversified and vulnerable to changes is a crucial issue for management and planning of protected and conserved areas (PCAs). Karkonosze National Park (KNP) located in south-western Poland in the border area between Poland and the Czech Republic was chosen as a research area. KNP covers the northern slopes of the Karkonosze Mountains, the largest range of the Sudetes.The geodiversity assessment based on spatial multicriteria analysis (S-MCA) with crowdsourced data was conducted. The geodiversity of KPN was evaluated with weighted linear combination (WLC) technique basis on selected criteria: 1) lithology, 2) relief energy, 3) geomorphology, 4) land use/land cover, 5) soils, 6) mesoclimate, and 7) hydrography. The assessment input data comprised of seven environmental factor ratings and weights were obtained from 57 Earth science researchers worldwide. These data served as the bases for a joint assessment of geodiversity and then spatially explicit global sensitivity analysis (GSA). The Monte Carlo simulation was used to sweep through criteria weight space, where weights are expressed using probability distributions. Multiple output suitability maps were generated and summarized using: an average suitability map, a standard deviation uncertainty map, and a number of sensitivity maps. The results helped to identify highly geodiverse areas that are burdened by high uncertainty and then to investigate which specific abiotic component contribute to the uncertainty the most. This could be valuable in monitoring and management of PCAs and significantly contribute to improving the existing results of geodiversity assessments and some savings resulting from field work.In the case of the mountainous area - KNP, the geodiversity value is the most sensitive to the lithological and the geomorphological criteria map. None of the weightings proved influential, suggesting a high consensus in weighting the factors among the geo-questionnaire respondents. In the future, it is worth conducting further simulations, considering another S-MCA technique, such as Ordered Weighted Averaging (OWA).
Marketing in the Metaverse: Emerging Risks

Over the next decade, virtual and augmented worlds are expected to significantly impact global markets. Often called “the metaverse” by industry players, these technologies will significantly impact how consumers engage their digital life, shifting mainstream computing from an ecosystem of mostly flat media to one that largely employs immersive media. This shift into the metaverse will impact marketing professionals and their industry in profound ways, transforming basic tools and methods from flat images and videos to fully immersive content experienced in the 1st person. These tactics will likely include widespread use of Virtual Spokespeople (VSPs) and Virtual Product Placements (VPPs). These emerging methods have the potential to be extremely persuasive vehicles, for they target users through organic, intuitive, and deeply personal interactions. At the same time, these methods pose real dangers, for they can be misused on consumers in highly manipulative ways. This paper reviews the immersive marketing techniques expected to be employed in the metaverse, outlines their potential risks to consumers, and makes recommendations for regulating immersive marketing tactics in largescale platforms.
Chaotic mode-competition dynamics in a multimode semiconductor laser with optical feedback and injection

Photonic computing has attracted increasing interest for the acceleration of information processing in machine learning applications. The mode-competition dynamics of multimode semiconductor lasers are useful for solving the multi-armed bandit problem in reinforcement learning for computing applications. In this study, we numerically evaluate the chaotic mode-competition dynamics in a multimode semiconductor laser with optical feedback and injection. We observe the chaotic mode-competition dynamics among the longitudinal modes and control them by injecting an external optical signal into one of the longitudinal modes. We define the dominant mode as the mode with the maximum intensity; the dominant mode ratio for the injected mode increases as the optical injection strength increases. We deduce that the characteristics of the dominant mode ratio in terms of the optical injection strength are different among the modes owing to the different optical feedback phases. We propose a control technique for the characteristics of the dominant mode ratio by precisely tuning the initial optical frequency detuning between the optical injection signal and injected mode. We also evaluate the relationship between the region of the large dominant mode ratios and the injection locking range. The region with the large dominant mode ratios does not correspond to the injection-locking range. The control technique of chaotic mode-competition dynamics in multimode lasers is promising for applications in reinforcement learning and reservoir computing in photonic artificial intelligence.
Ground-level Post-Disaster Image Classification using DenseNet201 for Disaster Damage Assessment

Damage assessment is a quick way for emergency management agencies to Figure out the effects of a natural disaster or other remarkable events so that resources can be sent immediately to help with response and recovery. Local officials analyze damage to public and private property after an incident. During the assessment, information is gathered to see if expenses and losses caused by the incident are eligible for help. Volume and degree of building damage are crucial for rescue and recovery; thus, locating damaged areas quickly and correctly. Damage assessment after a natural disaster is time-consuming. This research aims to determine and assess the damage caused by a disaster by developing a model to classify the level of damage (partially or totally damaged) in a structure. The model was based on the DenseNet201 architecture, and different image datasets were used to test it. The model correctly predicts classes with an accuracy of 90%. This classification model was enhanced further through processes called feature extraction and fine-tuning. Fine-tuning resulted in a significant improvement, as evidenced by a gain in training and validation accuracy and a decline in training and validation losses, as demonstrated in the model’s learning curve.
Automated carcinoma classification using efficient nuclei-based patch selection and deep learning techniques

Breast cancer can be successfully treated if diagnosed at its earliest, though it is considered as a fatal disease among women. The histopathology slide turned images are the gold standard for tumor diagnosis. However, the manual diagnosis is still tedious due to its structural complexity. With the advent of computer-aided diagnosis, time and computation intensive manual procedure can be managed with the development of an automated classification system. The feature extraction and classification are quite challenging as these images involve complex structures and overlapping nuclei. A novel nuclei-based patch extraction method is proposed for the extraction of non-overlapping nuclei patches obtained from the breast tumor dataset. An ensemble of pre-trained models is used to extract the discriminating features from the identified and augmented non-overlapping nuclei patches. The discriminative features are further fused using p-norm pooling technique and are classified using a LightGBM classifier with 10-fold cross-validation. The obtained results showed an increase in the overall performance in terms of accuracy, sensitivity, specificity, and precision. The proposed framework yielded an accuracy of 98.3% for binary class classification and 95.1% for multi-class classification on ICIAR 2018 dataset.
Combination of Object Tracking and Object Detection for Animal Recognition

While methods for object detection and tracking are well-developed for the purposes of human and vehicle identification, animal identification and re-identification from images and video is lagging behind. There is no clarity as to which object detection methods will work well on animal data. Here we compare two state-of-the art methods which output bounding boxes: the MMDetector and the UniTrack video tracker. Both methods were chosen for their high ranking on benchmark data sets. Using a bespoke pre-annotated database of five videos, we calculated the Average Precision (AP) of the outputs from the two methods. We propose a combination method to fuse the outputs of MMDetection and UniTrack and demonstrate that the proposed method is capable of outperforming both.
Means of gamification in the formation of media literacy as an element of information and digital competence of participants in the educational process

International scientific conference «Information technologies and management in higher education and sciences» : conference proceedings (November 28, 2022. Fergana, the Republic of Uzbekistan). Riga, Latvia : “Baltija Publishing”, 2022. Part 2. 388 pages.
Topological invariants in quantum walks

Discrete-time quantum walks (DTQWs) provide a convenient platform for a realisation of many topological phases in noninteracting systems. They often offer more possibilities than systems with a static Hamiltonian. Nevertheless, researchers are still looking for DTQW symmetries protecting topological phases and for definitions of appropriate topological invariants. Although majority of DTQW studies on this topic focus on the so called split-step quantum walk, two distinct topological phases can be observed in more basic models. Here we infer topological properties of the basic DTQWs directly from the mapping of the Brillouin zone to the Bloch Hamiltonian. We show that for translation symmetric systems they can be characterized by a homotopy relative to special points. We also propose a new topological invariant corresponding to this concept. This invariant indicates the number of edge states at the interface between two distinct phases.
Graph over-parameterization: Why the graph helps the training of deep graph convolutional network

Recent studies show that gradient descent can train a deep neural network (DNN) to achieve small training and test errors when the DNN is sufficiently wide. This result applies to various over-parameterized neural network models including fully-connected neural networks and convolutional neural networks. However, existing theory does not apply to graph convolutional networks (GCNs), as GCNs is built according to the topological structures of the data. It has been empirically observed that GCNs can outperform vanilla neural networks when the underlying graph captures geometric information of the data. However, there is few theoretical justification of such observation. In this paper, we establish theoretical guarantees of the high-probability convergence of gradient descent for training over-parameterized GCNs. Specifically, we introduce a novel measurement of the relation between the graph and the data, called the “graph disparity coefficient”, and show that the convergence of GCN is faster when the graph disparity coefficient is smaller. Our analysis provides novel insights into how the graph convolution operation in a GCN helps training, and provides useful guidance for GCN training in practice.