Reviewerly/exHarmony huggingface dataset:
Cognitive Computing-Based CDSS in Medical Practice

Importance . The last decade has witnessed the advances of cognitive computing technologies that learn at scale and reason with purpose in medicine studies. From the diagnosis of diseases till the generation of treatment plans, cognitive computing encompasses both data-driven and knowledge-driven machine intelligence to assist health care roles in clinical decision-making. This review provides a comprehensive perspective from both research and industrial efforts on cognitive computing-based CDSS over the last decade. Highlights . (1) A holistic review of both research papers and industrial practice about cognitive computing-based CDSS is conducted to identify the necessity and the characteristics as well as the general framework of constructing the system. (2) Several of the typical applications of cognitive computing-based CDSS as well as the existing systems in real medical practice are introduced in detail under the general framework. (3) The limitations of the current cognitive computing-based CDSS is discussed that sheds light on the future work in this direction. Conclusion . Different from medical content providers, cognitive computing-based CDSS provides probabilistic clinical decision support by automatically learning and inferencing from medical big data. The characteristics of managing multimodal data and computerizing medical knowledge distinguish cognitive computing-based CDSS from other categories. Given the current status of primary health care like high diagnostic error rate and shortage of medical resources, it is time to introduce cognitive computing-based CDSS to the medical community which is supposed to be more open-minded and embrace the convenience and low cost but high efficiency brought by cognitive computing-based CDSS.
The Effect of Transfer Learning on Turkish Text Classification

Text classification is one of the most important issues in natural language processing. In this study, texts belonging to different problems were classified using classical machine learning and deep learning methods. Additionally, transformer-based classifiers using transfer learning were also used, and the effects of transfer learning on classification success were examined. As a result of the experiments, it was seen that higher performance was obtained from the transfer learning based Bert classifier compared to other methods. With the study, transfer learning effect in Turkish text classification was examined in detail.
COVID‐Apps: Misdirecting Public Health Attention in a Pandemic

When there is no vaccine for a disease, 'Test, Trace, Treat/Isolate' is the public health go-to directive. During the COVID-19 pandemic, mobile phone apps are designed to improve on this. But COVID-apps have not been effective as a public health tool. Countries spend millions to develop them, yet they have been shown to have terrible return on investment. This commentary explores why COVID-apps are generally championed and provides three brief case studies (Germany, Sierra Leone, Canada) of non-app public health success. In conclusion, I argue that we need to get our public health care priorities straight: Better and more testing; increased investment in manual contact tracing and treatments; hospitalization when necessary; and wrap-around care - assistance with groceries, cleaning, child- or eldercare responsibilities, telehealth doctor appointment hookups - for sick people in home isolation.
A Retargetable Model-Driven Framework for the Development of Mobile User Interfaces

Since the emergence of mobile devices, the architecture of mobile applications has been transformed significantly. In mobile applications, the User Interface (UI) is one of the major elements, but its development is complex and time-consuming. Existing practices do not support various presentation issues of the UI at a higher abstraction level, in a retargetable fashion, with complete tool support. Therefore, it is critical to develop a simple and automated framework for the development of mobile UIs by exploiting model-driven engineering concepts. In this paper, a Unified Modeling language (UML) profile for Mobile User Interfaces (UMMUI) has been proposed, which employs some standard UML notations for representing the mobile UI requirements at a higher abstraction level. Subsequently, a complete open-source transformation engine has been developed to automatically transform the high-level source models (in UMMUI) into the target low-level React Native implementation. Finally, the applicability of the proposed framework is validated through two benchmark case studies, i.e., Patient Management System and Library Application. The results verify that the proposed framework allows the modeling of UIs with simplicity and generates the target code automatically with minimum transformation losses.
Fuzzy based approach to ontology relations alignment

Ontology alignment, although widely researched and discussed, has still some issues that need addressing. One of those is designating alignments of relations defined in two independent ontologies. According to the found literature, this topic is frequently omitted by many state-of-the-art ontology alignment solutions, that focus mainly on aligning concepts. In this paper, we propose a novel approach to designating mappings of ontology relations, which is based on fuzzy logic. It is used to combine several different similarity measures calculated between elements that are used to define relations. The approach has been experimentally verified using the widely accepted datasets provided by Ontology Alignment Evaluation Initiative, yielding promising results.
Development of human motion prediction strategy using inception residual block

Human Motion Prediction is a crucial task in computer vision and robotics. It has versatile application potentials, such as human-robot interactions, human action tracking for airport security systems, autonomous car navigation, and computer gaming, to name a few. However, predicting human motion based on past actions is extremely challenging due to the difficulties in correctly detecting spatial and temporal features. We propose an Inception Residual Block(IRB) to detect temporal features in human poses due to its inherent capability of processing multiple kernels to capture salient features. Here, we propose to use multiple 1-D Convolution Neural Networks (CNN) with different kernel sizes and input sequence lengths and concatenate them to get proper embedding. As kernels stride over different receptive fields, they detect smaller and bigger salient features at multiple temporal scales. Our main contribution is to propose a residual connection between input and the output of the inception block to have a continuity between the previously observed pose and the next predicted pose. With this proposed architecture, it learns prior knowledge much better about human poses, and we achieve much higher prediction accuracy as detailed in the paper. Subsequently, we further propose to feed the output of the IRB as an input to the Graph Convolution Neural Network (GCN) due to its better spatial feature learning capability. We perform a parametric analysis for a better design of our model. Subsequently, we evaluate our approach on the Human 3.6M dataset and CMU MoCap dataset and compare our short-term and long-term predictions with the state-of-the-art papers, where our model outperforms most of the pose results, the detailed reasons of which have been elaborated in the paper.
Optimal quantum discrimination of single-qubit unitary gates between two candidates

We analyze a discrimination problem of a single-qubit unitary gate with two candidates, where the candidates are not provided with their classical description, but their quantum sample is. More precisely, there are three unitary quantum gates---one target and one sample for each of the two candidates---whose classical description is unknown except for their dimension. The target gate is chosen equally among the candidates. We obtain the optimal protocol that maximizes the expected success probability, assuming the Haar distribution for the candidates. This problem is originally introduced in Ref. [5] which provides a protocol achieving 7/8 in the expected success probability based on the ``unitary comparison'' protocol of Ref. [6]. The optimality of the protocol has been an open question since then. We prove the optimality of the comparison protocol, implying that only one of the two samples (one for each candidate) is needed to achieve an optimal discrimination. The optimization includes protocols outside the scope of quantum testers due to the dynamic ordering of the sample and target gates within a given protocol.
Directionally Decomposing Structured Light for Projector Calibration

Intrinsic projector calibration is essential in projection mapping (PM) applications, especially in dynamic PM. However, due to the shallow depth-of-field (DOF) of a projector, more work is needed to ensure accurate calibration. We aim to estimate the intrinsic parameters of a projector while avoiding the limitation of shallow DOF. As the core of our technique, we present a practical calibration device that requires a minimal working volume directly in front of the projector lens regardless of the projector's focusing distance and aperture size. The device consists of a flat-bed scanner and pinhole-array masks. For calibration, a projector projects a series of structured light patterns in the device. The pinholes directionally decompose the structured light, and only the projected rays that pass through the pinholes hit the scanner plane. For each pinhole, we extract a ray passing through the optical center of the projector. Consequently, we regard the projector as a pinhole projector that projects the extracted rays only, and we calibrate the projector by applying the standard camera calibration technique, which assumes a pinhole camera model. Using a proof-of-concept prototype, we demonstrate that our technique can calibrate projectors with different focusing distances and aperture sizes at the same accuracy as a conventional method. Finally, we confirm that our technique can provide intrinsic parameters accurate enough for a dynamic PM application, even when a projector is placed too far from a projection target for a conventional method to calibrate the projector using a fiducial object of reasonable size.
A Class Practice Study of Intervention Effect of Interactive Assessment on Learning Goal Orientation

While focusing on the moderating effects of initial performance-approach goal orientation and performance-avoidance goal orientation, this study aimed to examine the effects of self- and peer-assessment on the growth of learning goal orientation. We set up a control group and two experimental groups (self-assessment and peer-assessment groups) and conducted experimental lessons. The responses of the 63 subjects (control group: n = 14; self-assessment group: n = 25; peer-assessment group: n = 24) who attended these lessons were analyzed. The following observations were made: (1) the effect of peer-assessment on the growth of learning goal orientation may change depending on the initial performance-approach goal orientation or performance-avoidance goal orientation; (2) to increase learning goal orientation for students who have high performance-approach goal orientation or low performance-avoidance goal orientation, peer-assessment is effective; and (3) to increase learning goal orientation for students who have low performance-approach goal orientation or high performance-avoidance goal orientation, peer-assessment appears to be counterproductive.
Serial Manipulator Control Optimization Using Ant Colony Algorithm

Abstract Serial manipulator is one of robot type that commonly used on industry. The high Degree of Freedom (DoF) of serial manipulator lead to more flexibility on manipulation but at the same time it also lead to more complex mathematical model of serial manipulator. Because of this complexity, we cannot use the common Proportional-Integral-Derivative (PID) tuning method such as Ziegler-Nichols and Cohen-Coon while the parameter on controller is need to be optimal in order to reduce the error during position control and avoid instability which may cause hazard to the operator. Ant colony algorithm proposed to optimize the controller of serial manipulator and result of optimization evaluated through experiment.
Revisiting Research Methods in Language Learning Psychology From a Complexity Dynamic System Theory Perspective

Language learning is a dynamic and complex process in which different factors and variables are constantly interacting. However, many studies done on constructs related to teacher-learner psychology have used one-shot quantitative research designs, while it is impossible to capture the complexity and dynamism of such variables via one-time measurements. Against this gap, complexity dynamic system theory (CDST) has recently been applied to explore processes and changes that a construct may undergo. To shed more light, the present study examined the current research methods used in this research domain and presented the contributions and different conceptualizations that can be made through CDST. In the end, some implications and future directions are suggested for passionate scholars.
SCIDA: Self-Correction Integrated Domain Adaptation From Single- to Multi-Label Aerial Images

Most publicly available datasets for image classification are with single labels, while images are inherently multi-labeled in our daily life. Such an annotation gap makes many pre-trained single-label classification models fail in practical scenarios. This annotation issue is more concerned for aerial images: Aerial data collected from sensors naturally cover a relatively large land area with multiple labels, while annotated aerial datasets, which are publicly available (e.g., UCM, AID), are single-labeled. As manually annotating multi-label aerial images would be time/labor-consuming, we propose a novel self-correction integrated domain adaptation (SCIDA) method for automatic multi-label learning. SCIDA is weakly supervised, i.e., automatically learning the multi-label image classification model from using massive, publicly available single-label images. To achieve this goal, we propose a novel Label-Wise self-Correction (LWC) module to better explore underlying label correlations. This module also makes the unsupervised domain adaptation (UDA) from single- to multi-label data possible. For model training, the proposed model only uses single-label information yet requires no prior knowledge of multi-labeled data; and it predicts labels for multi-label aerial images. In our experiments, trained with single-labeled MAI-AID-s and MAI-UCM-s datasets, the proposed model is tested directly on our collected Multi-scene Aerial Image (MAI) dataset.
Click to Move: Controlling Video Generation with Sparse Motion

This paper introduces Click to Move (C2M), a novel framework for video generation where the user can control the motion of the synthesized video through mouse clicks specifying simple object trajectories of the key objects in the scene. Our model receives as input an initial frame, its corresponding segmentation map and the sparse motion vectors encoding the input provided by the user. It outputs a plausible video sequence starting from the given frame and with a motion that is consistent with user input. Notably, our proposed deep architecture incorporates a Graph Convolution Network (GCN) modelling the movements of all the objects in the scene in a holistic manner and effectively combining the sparse user motion information and image features. Experimental results show that C2M outperforms existing methods on two publicly available datasets, thus demonstrating the effectiveness of our GCN framework at modelling object interactions. The source code is publicly available at https://github.com/PierfrancescoArdino/C2M.
Probabilistic resumable quantum teleportation in high dimensions

Teleportation is a quantum information processes without classical counterparts, in which the sender can disembodied transfer unknown quantum states to the receiver. In probabilistic teleportation through a partial entangled quantum channel, the transmission is exact (with fidelity 1), but may fail in a probability and the initial state is destroyed simultaneously. We propose a scheme for nondestructive probabilistic teleportation of high-dimensional quantum states. With the aid of an ancilla in the hands of the sender, the initial quantum information can be recovered when teleportation fails. The ancilla acts as a quantum apparatus to measure the sender's subsystem. Erasing the information recorded in it can resume the initial state.
THE EDUCATION SYSTEM IN THE CONTEXT OF SOCIO-ECONOMIC TRANSFORMATIONS

The modern world is entering a new state of its development associated with the digitalization process. It affects all aspects of society, changes the traditional foundations and imposes new requirements for the educational process. National educational systems cannot remain aloof from this process. The purpose of this article is to study the digitalization process and its impact on the education system of the world and the Russian Federation. The methodology of the article is based on a systemic and structural-functional approach. The validity of the results of the study is based on the use of popular scientific and special methods, including analysis, synthesis, historical and comparative analysis. On the basis of the analysis of scientific literature, the authors formed an overall picture of the problem under consideration. The work analyzes the processes occurring in the world associated with the beginning of a new historical period. The new time also imposes new requirements on various branches of knowledge, updates approaches to the use of modern and future information technologies. A particular place is given to the synergistic approach, which is an optimistic alternative to mastering the knowledge and competencies of the digital education. In the article the authors point to the lag in the theoretical base of the digitalization process, the lag in infrastructure, shortcomings in the organization of the educational process, reveal cognitive contradictions and, at the same time, show possible ways to solve them.
Data Type Inference for Logic Programming

In this paper we present a new static data type inference algorithm for logic programming. Without the need for declaring types for predicates, our algorithm is able to automatically assign types to predicates which, in most cases, correspond to the data types processed by their intended meaning. The algorithm is also able to infer types given data type definitions similar to data definitions in Haskell and, in this case, the inferred types are more informative, in general. We present the type inference algorithm, prove it is decidable and sound with respect to a type system, and, finally, we evaluate our approach on example programs that deal with different data structures.
Improving Robustness of One-Shot Voice Conversion with Deep Discriminative Speaker Encoder

One-shot voice conversion has received significant attention since only one utterance from source speaker and target speaker respectively is required. Moreover, source speaker and target speaker do not need to be seen during training. However, available one-shot voice conversion approaches are not stable for unseen speakers as the speaker embedding extracted from one utterance of an unseen speaker is not reliable. In this paper, we propose a deep discriminative speaker encoder to extract speaker embedding from one utterance more effectively. Specifically, the speaker encoder first integrates residual network and squeeze-and-excitation network to extract discriminative speaker information in frame level by modeling frame-wise and channel-wise interdependence in features. Then attention mechanism is introduced to further emphasize speaker related information via assigning different weights to frame level speaker information. Finally a statistic pooling layer is used to aggregate weighted frame level speaker information to form utterance level speaker embedding. The experimental results demonstrate that our proposed speaker encoder can improve the robustness of one-shot voice conversion for unseen speakers and outperforms baseline systems in terms of speech quality and speaker similarity.
VIRFIM: an AI and Internet of Medical Things-driven framework for healthcare using smart sensors

After affecting the world in unexpected ways, the virus has started mutating which is evident with the insurgence of its new variants. The governments, hospitals, schools, industries, and humans, in general, are looking for a potential solution in the vaccine which will eventually be available, but its timeline for eradicating the virus is yet unknown. Several researchers have encouraged and recommended the use of good practices such as physical healthcare monitoring, immunity boosting, personal hygiene, mental healthcare, and contact tracing for slowing down the spread of the virus. In this article, we propose the use of smart sensors integrated with the Internet of Medical Things to cover the spectrum of good practices in an automated manner. We present hypothetical frameworks for each of the good practice modules and propose the VIrus Resistance Framework using the Internet of Medical Things (VIRFIM) to tie all the individual modules in a unified architecture. Furthermore, we validate the realization of VIRFIM framework with two case studies related to physical activity monitoring and stress detection services. We envision that VIRFIM would be influential in assisting people with the new normal for current and future pandemics as well as instrumental in halting the economic losses, respectively. We also provide potential challenges and their probable solutions in compliance with the proposed VIRFIM.
Interpreting Connexive Principles in Coherence-Based Probability Logic

We present probabilistic approaches to check the validity of selected connexive principles within the setting of coherence. Connexive logics emerged from the intuition that conditionals of the form If \(\mathord {\thicksim }A\), then A, should not hold, since the conditional's antecedent \(\mathord {\thicksim }A\) contradicts its consequent A. Our approach covers this intuition by observing that for an event A the only coherent probability assessment on the conditional event \(A|\bar{A}\) is \(p(A|\bar{A})=0\). Moreover, connexive logics aim to capture the intuition that conditionals should express some "connection" between the antecedent and the consequent or, in terms of inferences, validity should require some connection between the premise set and the conclusion. This intuition is covered by a number of principles, a selection of which we analyze in our contribution. We present two approaches to connexivity within coherence-based probability logic. Specifically, we analyze connections between antecedents and consequents firstly, in terms of probabilistic constraints on conditional events (in the sense of defaults, or negated defaults) and secondly, in terms of constraints on compounds of conditionals and iterated conditionals. After developing different notions of negations and notions of validity, we analyze the following connexive principles within both approaches: Aristotle's Theses, Aristotle's Second Thesis, Abelard's First Principle and selected versions of Boethius' Theses. We conclude by remarking that coherence-based probability logic offers a rich language to investigate the validity of various connexive principles.