Reviewerly/exHarmony huggingface dataset:
The Role of Medical Educational Ecosystems in the Development of “Flexible Skills” According to Doctors, Students and Teachers of a Medical University

The chapter is devoted to the study of the role of educational ecosystems and medical educational ecosystems in the development of «soft skills», according to students, doctors and teachers of a medical university. The topic is very relevant in view of the fact that in recent years there has been an active development of educational ecosystems. And, despite the fact that approaches to the educational process and many requirements for specialists change from time to time, the concept of «flexible skills» remains very relevant, including for educators and medicine, and even more so for doctors involved in the training of young professionals. The objects of the study were 150 people, men and women, divided into 3 groups of 50 subjects, depending on belonging to a particular community. Group 1 included 50 medical students enrolled in the 4th year of the medical faculty of VGMU N.N. Burdenko. The second group included 50 practicing physicians of the VRCH №1 of various specialties (both surgical and therapeutic). Group 3 included employees of various departments from among the teaching staff of the VGMU N.N. Burdenko. The basis of the study was an anonymous questionnaire developed by the authors, which includes both some personal questions – age and work experience, and specifically aimed at determining the level of awareness of the respondents about educational ecosystems and medical educational ecosystems, about the level of knowledge about «soft skills», with ranking from more significant to less significant for their practical activities, as well as including issues of satisfaction with the current education system and opportunities for improved mastery of «soft skills» through educational ecosystems. The data obtained can significantly help in understanding the importance of modern educational ecosystems and medical educational ecosystems for better and faster mastering of soft skills.
Revising the Assessment of Creative Competence of Journalism Students

This study proposes an effective development methodology for evaluating the creativity of journalism students. Within the framework of university education, it is proposed to focus the attention of teachers on creating a special educational environment (media park), where progressive forms and methods of teaching, game methods are used systematically, in the context of the ecology of media processes. The peculiarity of the methodology is that at the end of the educational process (the creation of a specific media product by the students), its effectiveness is measured by comparing the initial and final indicators of the creative competence of student journalists. The author proposes to use S. Mednik’s speech-thinking creativity test, which is based on an assessment of the level of development of the student’s thinking and speech in their relationship, which is extremely important for future journalists. The article presents the results of the practical application of this type of assessment. The relevance and novelty of the problematic are due to the following circumstances. On the one hand, active forms and methods of teaching, including project activities, are widely used in universities, on the other hand, the methodology for assessing the creative competence of students is currently not well developed and is practically not used in the curriculum. It should also be recognized that the assessment of this competence and the products of creativity in general, which, of course, includes a journalistic text, is complex and ambiguous, since it is influenced by a large number of subjective factors. This article proposes one of the ways to resolve this contradiction.
NVIF: Neighboring Variational Information Flow for Large-Scale Cooperative Multi-Agent Scenarios

Communication-based multi-agent reinforcement learning (MARL) provides information exchange between agents, which promotes the cooperation. However, existing methods cannot perform well in the large-scale multi-agent system. In this paper, we adopt neighboring communication and propose a Neighboring Variational Information Flow (NVIF) to provide efficient communication for agents. It employs variational auto-encoder to compress the shared information into a latent state. This communication protocol does not rely dependently on a specific task, so that it can be pre-trained to stabilize the MARL training. Besides. we combine NVIF with Proximal Policy Optimization (NVIF-PPO) and Deep Q Network (NVIF-DQN), and present a theoretical analysis to illustrate NVIF-PPO can promote cooperation. We evaluate the NVIF-PPO and NVIF-DQN on MAgent, a widely used large-scale multi-agent environment, by two tasks with different map sizes. Experiments show that our method outperforms other compared methods, and can learn effective and scalable cooperation strategies in the large-scale multi-agent system.
Continuous Sign Language Recognition via Temporal Super-Resolution Network

Aiming at the problem that the spatial-temporal hierarchical continuous sign language recognition model based on deep learning has a large amount of computation, which limits the real-time application of the model, this paper proposes a temporal super-resolution network(TSRNet). The data is reconstructed into a dense feature sequence to reduce the overall model computation while keeping the final recognition accuracy loss to a minimum. The continuous sign language recognition model(CSLR) via TSRNet mainly consists of three parts: frame-level feature extraction, time series feature extraction and TSRNet, where TSRNet is located between frame-level feature extraction and time-series feature extraction, which mainly includes two branches: detail descriptor and rough descriptor. The sparse frame-level features are fused through the features obtained by the two designed branches as the reconstructed dense frame-level feature sequence, and the connectionist temporal classification(CTC) loss is used for training and optimization after the time-series feature extraction part. To better recover semantic-level information, the overall model is trained with the self-generating adversarial training method proposed in this paper to reduce the model error rate. The training method regards the TSRNet as the generator, and the frame-level processing part and the temporal processing part as the discriminator. In addition, in order to unify the evaluation criteria of model accuracy loss under different benchmarks, this paper proposes word error rate deviation(WERD), which takes the error rate between the estimated word error rate (WER) and the reference WER obtained by the reconstructed frame-level feature sequence and the complete original frame-level feature sequence as the WERD. Experiments on two large-scale sign language datasets demonstrate the effectiveness of the proposed model.
All about Phishing Attack, Danger and Its Prevention

Abstract: Phishing is a well-known cybersecurity attack that has rapidly increased in recent years. It poses legitimate risks to businesses, government agencies, and all users due to sensitive data breaches, subsequent financial and productivity losses, and social and personal inconvenience. Often, these attacks use social engineering techniques to deceive end-users, indicating the importance of user focused studies to help prevent future attacks. We provide a detailed overview of phishing research that has focused on users by conducting a systematic literature review of peer- reviewed academic papers published in ACM Digital Library. Although published work on phishing appears in this data set as early as 2004, we found that of the total number of papers on phishing (N = 367) only 13.9% (n= 51) focus on users by employing user study methodologies such as interviews, surveys, and in-lab studies. Even within this small subset of papers, we note a striking lack of attention to reporting important information about methods and participants (e.g., the number and nature of participants), along with crucial recruitment biases in some of the research.
Informed Learning by Wide Neural Networks: Convergence, Generalization and Sampling Complexity

By integrating domain knowledge with labeled samples, informed machine learning has been emerging to improve the learning performance for a wide range of applications. Nonetheless, rigorous understanding of the role of injected domain knowledge has been under-explored. In this paper, we consider an informed deep neural network (DNN) with over-parameterization and domain knowledge integrated into its training objective function, and study how and why domain knowledge benefits the performance. Concretely, we quantitatively demonstrate the two benefits of domain knowledge in informed learning - regularizing the label-based supervision and supplementing the labeled samples - and reveal the trade-off between label and knowledge imperfectness in the bound of the population risk. Based on the theoretical analysis, we propose a generalized informed training objective to better exploit the benefits of knowledge and balance the label and knowledge imperfectness, which is validated by the population risk bound. Our analysis on sampling complexity sheds lights on how to choose the hyper-parameters for informed learning, and further justifies the advantages of knowledge informed learning.
A New Index for Clustering Evaluation Based on Density Estimation

A new index for internal evaluation of clustering is introduced. The index is defined as a mixture of two sub-indices. The first sub-index $ I_a $ is called the Ambiguous Index; the second sub-index $ I_s $ is called the Similarity Index. Calculation of the two sub-indices is based on density estimation to each cluster of a partition of the data. An experiment is conducted to test the performance of the new index, and compared with six other internal clustering evaluation indices -- Calinski-Harabasz index, Silhouette coefficient, Davies-Bouldin index, CDbw, DBCV, and VIASCKDE, on a set of 145 datasets. The result shows the new index significantly improves other internal clustering evaluation indices.
Resource Constrained Multi-phase Project Scheduling Problem

In this study, in order to divide the project schedule into phased problems, it is divided into phases and modules, In other words, the schedule problem was converted into a step-by-step format, In the existing RCPSP research, a study to find a solution by dividing the entire project schedule into several stages has not been conducted yet, Therefore, in this study, the overall project schedule is divided into several stages to present an optimization algorithm for establishing a schedule that can maximize the value of the module, In such a case, it is important to decide which stage to assign each module to, and to determine the execution mode and start time of the activities belonging to each module, In general, in the case of RCPSP, heuristic techniques are used in most studies due to physical limitations related to the optimal solution calculation time, However, in the case of the heuristic method, although its utility is high, it is still a method of finding a good solution among feasible solutions, not a method of finding an optimal solution, In this regard, it can be said that the study of algorithms for optimization techniques is important, Therefore, this study proposes an optimization solution to the 'Resource constrained multi-phase project scheduling problem' using the multi-step division method that was not dealt with in previous studies, using the quantified value maximization of the values of the modules belonging to the stage as the objective function, can be said to be meaningful, In addition, the scope of research was expanded by presenting an algorithm to find the likelihood using the neighbor search method among the heuristic widely used in RCPSP.
USHER: Unbiased Sampling for Hindsight Experience Replay

Dealing with sparse rewards is a long-standing challenge in reinforcement learning (RL). Hindsight Experience Replay (HER) addresses this problem by reusing failed trajectories for one goal as successful trajectories for another. This allows for both a minimum density of reward and for generalization across multiple goals. However, this strategy is known to result in a biased value function, as the update rule underestimates the likelihood of bad outcomes in a stochastic environment. We propose an asymptotically unbiased importance-sampling-based algorithm to address this problem without sacrificing performance on deterministic environments. We show its effectiveness on a range of robotic systems, including challenging high dimensional stochastic environments.
A Multi-Task BERT Model for Schema-Guided Dialogue State Tracking

Task-oriented dialogue systems often employ a Dialogue State Tracker (DST) to successfully complete conversations. Recent state-of-the-art DST implementations rely on schemata of diverse services to improve model robustness and handle zero-shot generalization to new domains [1], however such methods [2, 3] typically require multiple large scale transformer models and long input sequences to perform well. We propose a single multi-task BERT-based model that jointly solves the three DST tasks of intent prediction, requested slot prediction and slot filling. Moreover, we propose an efficient and parsimonious encoding of the dialogue history and service schemata that is shown to further improve performance. Evaluation on the SGD dataset shows that our approach outperforms the baseline SGP-DST by a large margin and performs well compared to the state-of-the-art, while being significantly more computationally efficient. Extensive ablation studies are performed to examine the contributing factors to the success of our model.
Anomaly Detection with Adversarially Learned Perturbations of Latent Space

Anomaly detection is to identify samples that do not conform to the distribution of the normal data. Due to the unavailability of anomalous data, training a supervised deep neural network is a cumbersome task. As such, unsupervised methods are preferred as a common approach to solve this task. Deep autoencoders have been broadly adopted as a base of many unsupervised anomaly detection methods. However, a notable shortcoming of deep autoencoders is that they provide insufficient representations for anomaly detection by generalizing to reconstruct outliers. In this work, we have designed an adversarial framework consisting of two competing components, an Adversarial Distorter, and an Autoencoder. The Adversarial Distorter is a convolutional encoder that learns to produce effective perturbations and the autoencoder is a deep convolutional neural network that aims to reconstruct the images from the perturbed latent feature space. The networks are trained with opposing goals in which the Adversarial Distorter produces perturbations that are applied to the encoder's latent feature space to maximize the reconstruction error and the autoencoder tries to neutralize the effect of these perturbations to minimize it. When applied to anomaly detection, the proposed method learns semantically richer representations due to applying perturbations to the feature space. The proposed method outperforms the existing state-of-the-art methods in anomaly detection on image and video datasets.
Creative Abilities of Students with Dominant Cognitive Style

The article examines the problem of creativity development, taking into account students' cognitive styles.We studied theory assumptions for cognitive styles when providing psychological and educational support for the development of a person's creativity, and presented the analysis of parameters of cognitive styles.We presented arguments that a person's cognitive style has a set of invariable constituents that manifest themselves in perception, processing, and use of information, as well as in variable ways to use the information space.We revealed the content characteristics of the cognitive style.We found that the majority of participants were characterized by field dependence and integrity and proved that parameters of cognitive styles are interrelated with selfregulation and creativity.In case of field independence and differentiation, the biggest changes were observed in the development of modeling, flexibility and curiosity.Prevailing field dependence and integrity had a positive effect on the development of emotional sensitivity (empathy), intuition and a creative attitude towards a profession.Students with different parameters demonstrated differences in the development of creativity components.The research provides empirical evidence for using psychological and educational support to develop students' creative abilities.The authors highlighted the possibilities of psychological and educational support for creativity development, taking into account students' cognitive styles.
Immersive Therapy for Improving Anxiety in Health Professionals of a Regional Hospital During the COVID-19 Pandemic: A Quasi-Experimental Pilot Study

Background: Immersive therapy through virtual reality represents a novel strategy used in psychological interventions, but there is still a need to strengthen the evidence on its effects on health professionals&amp;rsquo; mental health. Objective: To analyze the results of immersive therapy through virtual reality in the levels of anxiety and secondly, well-being of the health professionals working in a regional hospital in Olot (Spain). Methods: Pilot quasi-experimental study including a group of 35 women (mean age=45.7, SD=8.43) health professionals who undertook immersive therapy for 8 weeks. The intervention was implemented through virtual reality, and its effect on anxiety levels and well-being was evaluated through the Hamilton and Eudemon scales, respectively. Data on age, gender, active pharmacological or psychological treatment, mental health disorders and number of sessions were also collected. Results: Statistically significant (p&amp;lt;0.001) improvement in anxiety and well-being was found, with large and moderate effect sizes (0.90 and 0.63 respectively). In addition, these changes were clinically significant. No significant associations were found between the improvements and the different variables, but a greater trend was identified among the group of professionals with untreated or unidentified levels of anxiety. Conclusion: This group of health professionals showed statistically and clinically significant improvement in anxiety and well-being after the application of immersive therapy using virtual reality. Further studies with a control group are necessary to further analyze this novel intervention.
Latent Image Animator: Learning to Animate Images via Latent Space Navigation

Due to the remarkable progress of deep generative models, animating images has become increasingly efficient, whereas associated results have become increasingly realistic. Current animation-approaches commonly exploit structure representation extracted from driving videos. Such structure representation is instrumental in transferring motion from driving videos to still images. However, such approaches fail in case the source image and driving video encompass large appearance variation. Moreover, the extraction of structure information requires additional modules that endow the animation-model with increased complexity. Deviating from such models, we here introduce the Latent Image Animator (LIA), a self-supervised autoencoder that evades need for structure representation. LIA is streamlined to animate images by linear navigation in the latent space. Specifically, motion in generated video is constructed by linear displacement of codes in the latent space. Towards this, we learn a set of orthogonal motion directions simultaneously, and use their linear combination, in order to represent any displacement in the latent space. Extensive quantitative and qualitative analysis suggests that our model systematically and significantly outperforms state-of-art methods on VoxCeleb, Taichi and TED-talk datasets w.r.t. generated quality.
SISO System Model Reduction and Digital Controller Design using Nature Inspired Heuristic Optimisation Algorithms

This research article explores an algorithm to reduce the order of a SISO system and thereby to design a digital controller. The reduced order modelling of a large complex system eases out the analysis of the system. AGTM (Approximate Generalised Time Moments) method was implemented wherein the responses were matched at different time instants to achieve the reduced system. This research work devises a new method, Ensemble Framework for Optimized System (EFOS), resulting into a reduced system with better performance as compared to conventional techniques. The research also efforts towards effective utilization of various heuristic algorithms like Genetic Algorithm, Particle Swarm Optimization and Luus Jaakola Algorithm, their implementation and a comparison with other techniques based on relative mean square error and time complexity. It was observed that the proposed transfer learning based approach, EFOS, combining the advantages of Luus Jaakola and Genetic algorithms depicted better results than their individual counterparts on diverse performance parameters like speed of convergence and optimal convergence to global minima. The percentage improvement achieved in the time taken for design of the digital controller was 85.3%, with no change in delta value.
Multipartite Entanglement in Rabi Driven Superconducting Qubits

Exploring highly connected networks of qubits is invaluable for implementing various quantum algorithms and simulations as it allows for entangling qubits with reduced circuit depth. Here, we demonstrate a multi-qubit STAR (Sideband Tone Assisted Rabi driven) gate. Our scheme is inspired by the ion qubit M{\o}lmer-S{\o}rensen gate and is mediated by a shared photonic mode and Rabi-driven superconducting qubits, which relaxes restrictions on qubit frequencies during fabrication and supports scalability. We achieve a two-qubit gate with maximum state fidelity of 0.95 in 310 ns, a three-qubit gate with state fidelity 0.905 in 217 ns, and a four-qubit gate with state fidelity 0.66 in 200 ns. Furthermore, we develop a model of the gate that show the four-qubit gate is limited by shared resonator losses and the spread of qubit-resonator couplings, which must be addressed to reach high-fidelity operations.
NEED ANALYSIS OF MANAGEMENT STUDENTS TOWARDS THE ENGLISH LEARNING MATERIAL

To achieve effectiveness and alignment between the needs and the ability of students, it is necessary to conduct research about the needs of management students in learning English, especially English for Specific Purposes. Before designing the material for language teaching, the teacher needs to understand the nature of the material development and the process that is typically involved if quality materials are to be created. This research was designed as a descriptive qualitative design. In collecting the data, researchers gave questionnaires and interview the first semester of Management students at the University of Pasir Pengaraian. There are 80 students as a sample to conduct this research. The finding of this research there is six topics that students need to study in English there was an introduction about ourselves, introducing people, how to be a presentation, how to make the application letter, how to explain something, how to make something