Reviewerly/exHarmony huggingface dataset:
Evolving Embryonic Cell for Combinational Circuits using Cartesian Genetic Programming

This research aims to explore the possibility to implement concepts of embryonics with potential of self-repair mechanism. As the field of embryonics (embryo electronics) is based on multi-cellular architecture, the concept of growth from single embryo cell into complete organism can be utilized for fault-tolerant digital circuit design. This paper proposes a novel embryonic fabric and cell architecture that can configure itself as per the circuit requirement. It consists of an embryonic architecture where the configuration data (genome data) is in the form of Cartesian Genetic Programming (CGP). A customized Evolutionary Algorithm (EA) is designed to generate an optimized CGP data for the circuit under design. The CGP data configuration provides the better control at node or gate level in case of circuit fault. The configuration data size in CGP form does not increase linearly with more number of inputs and outputs as in the case of conventional Look Up Table (LUT) form. The embryonic cell architecture proposed is demonstrated for adder and comparator cells. A 4-bit adder is designed using four 1-bit adder cells and a 8-bit comparator is designed using four 2-bit comparator cells by employing cloning mechanism. A 4-bit adder needs 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8</sup> bits in LUT form of configuration data, while 45 bits are needed in CGP form. Similarly a 8-bit comparator needs 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">16</sup> bits in LUT form, while 108 bits are needed in CGP configuration data form. The transfer of signals between cells is through embryonic switch boxes. The design is simulated and tested using Verilog,
Instance Segmentation of Multiple Myeloma Cells Using Deep-Wise Data Augmentation and Mask R-CNN

Multiple myeloma is a condition of cancer in the bone marrow that can lead to dysfunction of the body and fatal expression in the patient. Manual microscopic analysis of abnormal plasma cells, also known as multiple myeloma cells, is one of the most commonly used diagnostic methods for multiple myeloma. However, as it is a manual process, it consumes too much effort and time. Besides, it has a higher chance of human errors. This paper presents a computer-aided detection and segmentation of myeloma cells from microscopic images of the bone marrow aspiration. Two major contributions are presented in this paper. First, different Mask R-CNN models using different images, including original microscopic images, contrast-enhanced images and stained cell images, are developed to perform instance segmentation of multiple myeloma cells. As a second contribution, a deep-wise augmentation, a deep learning-based data augmentation method, is applied to increase the performance of Mask R-CNN models. Based on the experimental findings, the Mask R-CNN model using contrast-enhanced images combined with the proposed deep-wise data augmentation provides a superior performance compared to other models. It achieves a mean precision of 0.9973, mean recall of 0.8631, and mean intersection over union (IOU) of 0.9062.
Design of an Integrated Bell-State Analyzer on a Thin-Film Lithium Niobate Platform

Trapped ions are excellent candidates for quantum computing and quantum networks because of their long coherence times, ability to generate entangled photons as well as high fidelity single- and two-qubit gates. To scale up trapped ion quantum computing, we need a Bell-state analyzer on a reconfigurable platform that can herald high fidelity entanglement between ions. In this work, we design a photonic Bell-state analyzer on a reconfigurable thin film lithium niobate platform for polarization-encoded qubits. We optimize the device to achieve high fidelity entanglement between two trapped ions and find >99% fidelity. The proposed device can scale up trapped ion quantum computing as well as other optically active spin qubits, such as color centers in diamond, quantum dots, and rare-earth ions.
Hybrid categorical expert system for use in content aggregation

The subject of this research is the development of the architecture of an expert system for distributed content aggregation system, the main purpose of which is the categorization of aggregated data. The author examines the advantages and disadvantages of expert systems, a toolset for the development of expert systems, classification of expert systems, as well as application of expert systems for categorization of data. Special attention is given to the description of the architecture of the proposed expert system, which consists of a spam filter, a component for determination of the main category for each type of the processed content, and components for the determination of subcategories, one of which is based on the domain rules, and the other uses the methods of machine learning methods and complements the first one. The conclusion is made that an expert system can be effectively applied for the solution of the problems of categorization of data in the content aggregation systems. The author establishes that hybrid solutions, which combine an approach based on the use of knowledge base and rules with the implementation of neural networks allow reducing the cost of the expert system. The novelty of this research lies in the proposed architecture of the system, which is easily extensible and adaptable to workloads by scaling existing modules or adding new ones.
Normal saccades but decreased fixation stability in a population of children with dyslexia

Developmental dyslexia affects around 5-15% of the population and has a heterogeneous aetiology. Optometric disorders are more prevalent in dyslexic populations but the relationship be- tween eye movement control and dyslexia is not well established. In this study, we investigated whether children with dyslexia show saccadic or fixation deficits and whether these deficits are related to deficits in visual acuity and/or accommodation. Thirty-four children with and without dyslexia were recruited for the project. All participants had an optometric examination and performed a saccade and fixation experiment. We used two eye movement paradigms: the step and the gap task. Eye movements were recorded by an infrared eye-tracker and saccade and fixation parameters were analysed separately. Saccadic latencies, premature saccades, and directional errors were similar between children with dyslexia and typically developing children. In contrast, fixations were significantly less stable in the dyslexic group. Neither saccades nor fixations were associated with deficits in accommodation or visual acuity. Children with dyslexia showed no difficulties in saccadic performance, but their fixation stability was reduced compared to the control group. The reduced fixation stability can be explained by general deficits in the cognitive processes that underpin eye movement control, that have also been found in other neuro-developmental disorders.
Modified Deep Residual Quantum Computing Optimization Technique for IoT Platform

Internet of Things (IoT) is defined as millions of interconnections between wireless devices to obtain data globally.The multiple data are targeting to observe the data through a common platform, and then it becomes essential to investigate accuracy for realizing the best IoT platform.To address the growing demand for time-sensitive data analysis and real-time decision-making, accuracy in IoT data collecting has become critical.The Res-HQCNN is a hybrid quantum-classical neural network with deep residual learning.The model is qualified in an end-to-end analog method in a traditional neural network, backpropagation is used.To discover the Res-HQCNN efficiency to perform on the classical computer, there has been a lot of investigation into quantum data with or without noise.Then focus on the application of the artificial neural network to analyze the dangers to these IoT networks.For data recording purposes, to undertake in-depth analysis on the threat severity, kind, and source, a model is trained using recurrent and convolutional neural networks.The intrusion detection system (IDS) explored in this study has a success rate of 99% based on the empirical data supplied to the model.Due to irregularly distributed robust execution, larger affectability for the introduction of authority dimension, steadiness, and the extremely large crucial area, a quantum hash function work has been proposed as an amazing method for secure communication between the IoT and cloud.
Alternative Semantics for Normative Reasoning with an Application to Regret and Responsibility

We provide a fine-grained analysis of notions of regret and responsibility (such as agent-regret and individual responsibility) in terms of a language of multimodal logic. This language undergoes a detailed semantic analysis via two sorts of models: (i) relating models, which are equipped with a relation of propositional pertinence, and (ii) synonymy models, which are equipped with a relation of propositional synonymy. We specify a class of strictly relating models and show that each synonymy model can be transformed into an equivalent strictly relating model. Moreover, we define an axiomatic system that captures the notion of validity in the class of all strictly relating models.
What do post-editors correct? A fine-grained analysis of SMT and NMT errors

The recent improvements in neural MT (NMT) have driven a shift from statistical MT (SMT) to NMT. However, to assess the usefulness of MT models for post-editing (PE) and have a detailed insight of the output they produce, we need to analyse the most frequent errors and how they affect the task. We present a pilot study of a fine-grained analysis of MT errors based on post-editors corrections for an English to Spanish medical text translated with SMT and NMT. We use the MQM taxonomy to compare the two MT models and have a categorized classification of the errors produced. Even though results show a great variation among post-editors’ corrections, for this language combination fewer errors are corrected by post-editors in the NMT output. NMT also produces fewer accuracy errors and errors that are less critical.
Composite Feature Vector Assisted Human Action Recognition through Supervised Learning

Human Action Recognition is a key research direction and also a trending topic in several fields like machine learning, computer vision and other fields. The main objective of this research is to recognize the human action in image of video. However, the existing approaches have many limitations like low recognition accuracy and non-robustness. Hence, this paper focused to develop a novel and robust Human Action Recognition framework. In this framework, we proposed a new feature extraction technique based on the Gabor Transform and Dual Tree Complex Wavelet Transform. These two feature extraction techniques helps in the extraction of perfect discriminative features by which the actions present in the image or video are correctly recognized. Later, the proposed framework accomplished the Support Vector Machine algorithm as a classifier. Simulation experiments are conducted over two standard datasets such as KTH and Weizmann. Experimental results reveal that the proposed framework achieves better performance compared to state-of-art recognition methods.
Current Situation and Countermeasures of Higher Vocational Education in the Information Age

Informatization has a profound impact on education. Comprehensively promoting teaching modernization with informatization is not only a new topic of the times, but also the only way for the modernization of vocational education. This paper studies the current situation of Higher Vocational Education in the information age. This paper analyzes the development process, path, typical practices and innovative experience of Vocational Teachers' modernization. At the same time, this paper focuses on the development and effectiveness of the e-learning space renrentong with Chinese characteristics and the vocational teaching informatization competition, and puts forward some strategies for optimal development on this basis. Realizing the modernization of teachers is the premise of teaching modernization. Therefore, the discussion on promoting the modernization of vocational teachers with informatization is also the core of this paper.
FinTech Applications

Abstract The Financial Stability Board defines financial technology (FinTech) as “technology-enabled innovation in financial services.” At the 2015 World Economic Forum, experts proposed a taxonomy for financial services that can be classified into six major categories: payments, deposits and lending, market provisioning, capital raising, insurance, and investment management. Because financial opinion mining can be applied to many listed services, we survey various cases in this chapter and show that financial opinion mining is useful and crucial in many financial application scenarios.
Augmented Reality Humans: Towards Multisensorial Awareness

In augmented reality, we have a virtual layer superimposed on reality. This virtual layer can contain multisensory content, that is, content that stimulates each of our senses: sight, hearing, taste, touch, and smell, and many interoceptive senses such as pain, heat, time, and balance. The virtual layer can contain virtual humans that have virtual senses to explore the augmented reality environment and to interact with real humans. These virtual humans can take on a role in social, marketing, retail, training, education, and entertainment augmented reality applications. In the human-like behavior of virtual humans, we expect awareness, use, and display of awareness of these senses. We present a view on the existing literature on virtual humans in augmented reality with a focus on sense-related presence issues.
Study on Automatic 3D Facial Caricaturization: From Rules to Deep Learning

Facial caricature is the art of drawing faces in an exaggerated way to convey emotions such as humor or sarcasm. Automatic caricaturization has been explored both in the 2D and 3D domain. In this paper, we propose two novel approaches to automatically caricaturize input facial scans, filling gaps in the literature in terms of user-control, caricature style transfer, and exploring the use of deep learning for 3D mesh caricaturization. The first approach is a gradient-based differential deformation approach with data driven stylization. It is a combination of two deformation processes: facial curvature and proportions exaggeration. The second approach is a GAN for unpaired face-scan-to-3D-caricature translation. We leverage existing facial and caricature datasets, along with recent domain-to-domain translation methods and 3D convolutional operators, to learn to caricaturize 3D facial scans in an unsupervised way. To evaluate and compare these two novel approaches with the state of the art, we conducted the first user study of facial mesh caricaturization techniques, with 49 participants. It highlights the subjectivity of the caricature perception and the complementarity of the methods. Finally, we provide insights for automatically generating caricaturized 3D facial mesh.
Side-Channel Protections for Picnic Signatures

We study masking countermeasures for side-channel attacks against signature schemes constructed from the MPC-in-the-head paradigm, specifically when the MPC protocol uses preprocessing. This class of signature schemes includes Picnic, an alternate candidate in the third round of the NIST post-quantum standardization project. The only previously known approach to masking MPC-in-the-head signatures suffers from interoperability issues and increased signature sizes. Further, we present a new attack to demonstrate that known countermeasures are not sufficient when the MPC protocol uses a preprocessing phase, as in Picnic3.We overcome these challenges by showing how to mask the underlying zero-knowledge proof system due to Katz–Kolesnikov–Wang (CCS 2018) for any masking order, and by formally proving that our approach meets the standard security notions of non-interference for masking countermeasures. As a case study, we apply our masking technique to Picnic. We then implement different masked versions of Picnic signing providing first order protection for the ARM Cortex M4 platform, and quantify the overhead of these different masking approaches. We carefully analyze the side-channel risk of hashing operations, and give optimizations that reduce the CPU cost of protecting hashing in Picnic by a factor of five. The performance penalties of the masking countermeasures ranged from 1.8 to 5.5, depending on the degree of masking applied to hash function invocations.
Leveraging towards Privacy-preserving using Federated Machine Learning for Healthcare Systems

With the advent of artificial intelligence, healthcare industry has been successful in improving medical diagnosis, medical prognosis, and other clinical tasks using machine learning. The success of improving healthcare services is largely due to the amount of patients data acquired usually in privacy-invasive ways for the process of machine learning. The issue of privacy-preserving and utilizing the large and diverse private datasets is solved using federated learning. It can perform predictive tasks alongside maintaining privacy of medical data. In this paper, we have proposed cancer text classification system using federated learning to maintain the integrity of sensitive medical data. The acquisition of data from various healthcare institutions is used for detection of cancer in preliminary stage in the patient's body. The experiment has been conducted for various types of healthcare environmental settings for the process of federated learning including change in the number of health institutions involved, change in data distribution among numerous healthcare institutions involved, and performance in case of applying various machine learning models. Here, we utilize several machine learning models namely, Recurrent Neural Network (RNN), Bi-RNN, Gated Recurrent Unit (GRU), and Long short-term Memory (LSTM) for text classification that attains accuracy of 57%, 59%, 60%, and 61% respectively. The paper proposes a novel federated learning based system capable of text classification in the healthcare industry with the use of federated machine learning alongside maintaining data privacy.
E-learning as a modern format of educational activity

Based on the analysis of Russian literature, the article considers e-learning as a modern format of educational activity, as a modern pedagogical technology. The role of digital transformation of education is emphasized, which involves constant updating of the content of education and planned learning outcomes, methods, means and control of educational activities in a dynamically developing digital information and educational environment. The use of new types of educational activities based on the interpenetration of already traditional teaching models and modern digital technologies contributes to the quality training of students. Taking into account the prevailing factors, the most effective in this regard is the creation and use of educational e-courses, the active introduction of e-learning into the professional training of specialists. The implementation of educational activities based on e-learning is accompanied by a specially created information and educational environment, which takes over the provision of training through the use of information and communication technologies, network services for the development of educational programs. Electronic educational resources are presented in electronic form and include a certain structure, the main subject content and output data. Thanks to electronic resources, information interaction between participants in educational relations is ensured, the quality of formed competencies and the knowledge of information culture among students increases. The active introduction of e-learning requires not only the psychological and professional preparedness of all participants in the educational distance learning process, but also its theoretical development and methodological support. The development of e-learning stimulates the need for the formation of professional competencies among teachers for successful activities in the electronic information and educational environment. Electronic educational technologies make it possible to complement and expand existing forms of educational communication, and in this sense, the use of e-learning in education today is not only the use of information technologies and digital solutions in the interaction of the teacher and students, but also defines a new format of teaching.
A Linear Algebra Approach to Linear Metatheory

Linear typed $\lambda$-calculi are more delicate than their simply typed siblings when it comes to metatheoretic results like preservation of typing under renaming and substitution. Tracking the usage of variables in contexts places more constraints on how variables may be renamed or substituted. We present a methodology based on linear algebra over semirings, extending McBride's kits and traversals approach for the metatheory of syntax with binding to linear usage-annotated terms. Our approach is readily formalisable, and we have done so in Agda.
Content of Pedagogical Education in the Modern World: Meanings, Problems, Practices and Development Prospects

The article examines modern ideas about the nature of pedagogical activity, providing a critical analysis of the state of practice and scientific research on the problems of pedagogical education and reconstructing a set of methodological ideas, concepts and schemes related to ideas about the content and forms of pedagogical education. The authors present approaches to solving such problems as the modern understanding of the essence of pedagogical activity and the trends of its transformation in connection with the changed educational space, new functions of the teacher in the digital educational environment; crisis phenomena in pedagogical education and ways to overcome them. In this vein, the authors consider the concept of pedagogical education, substantiating its goals, content options and psychological mechanisms for mastering pedagogical activity. The proposed model of the process of professional socialization of teachers reveals the content, technologies and criteria for their effective professional training, dialectics of psychological, pedagogical and subject components in the structure of professional competence. Consideration is also given to the environment of a pedagogical university as a source of development of pedagogical experience and personal and professional qualities of teachers. The article may be of interest to researchers and organizers in the field of pedagogical education.