Reviewerly/exHarmony huggingface dataset:
Evaluating Transformers for Lightweight Action Recognition

In video action recognition, transformers consistently reach state-of-the-art accuracy. However, many models are too heavyweight for the average researcher with limited hardware resources. In this work, we explore the limitations of video transformers for lightweight action recognition. We benchmark 13 video transformers and baselines across 3 large-scale datasets and 10 hardware devices. Our study is the first to evaluate the efficiency of action recognition models in depth across multiple devices and train a wide range of video transformers under the same conditions. We categorize current methods into three classes and show that composite transformers that augment convolutional backbones are best at lightweight action recognition, despite lacking accuracy. Meanwhile, attention-only models need more motion modeling capabilities and stand-alone attention block models currently incur too much latency overhead. Our experiments conclude that current video transformers are not yet capable of lightweight action recognition on par with traditional convolutional baselines, and that the previously mentioned shortcomings need to be addressed to bridge this gap. Code to reproduce our experiments will be made publicly available.
Experimental FIA Methodology Using Clock and Control Signal Modifications under Power Supply and Temperature Variations

The security of cryptocircuits is determined not only for their mathematical formulation, but for their physical implementation. The so-called fault injection attacks, where an attacker inserts faults during the operation of the cipher to obtain a malfunction to reveal secret information, pose a serious threat for security. These attacks are also used by designers as a vehicle to detect security flaws and then protect the circuits against these kinds of attacks. In this paper, two different attack methodologies are presented based on inserting faults through the clock signal or the control signal. The optimization of the attacks is evaluated under supply voltage and temperature variation, experimentally determining the feasibility through the evaluation of different Trivium versions in 90 nm ASIC technology implementations, also considering different routing alternatives. The results show that it is possible to inject effective faults with both methodologies, improving fault efficiency if the power supply voltage decreases, which requires only half the frequency of the short pulse inserted into the clock signal to obtain a fault. The clock signal modification methodology can be extended to other NLFSR-based cryptocircuits and the control signal-based methodology can be applied to both block and stream ciphers.
Rethinking Neural Operations for Diverse Tasks

An important goal of neural architecture search (NAS) is to automate-away the design of neural networks on new tasks in under-explored domains. Motivated by this broader vision for NAS, we study the problem of enabling users to discover the right neural operations given data from their specific domain. We introduce a search space of neural operations called XD-Operations that mimic the inductive bias of standard multichannel convolutions while being much more expressive: we prove that XD-operations include many named operations across several application areas. Starting with any standard backbone network such as LeNet or ResNet, we show how to transform it into an architecture search space over XD-operations and how to traverse the space using a simple weight-sharing scheme. On a diverse set of applications--image classification, solving partial differential equations (PDEs), and sequence modeling--our approach consistently yields models with lower error than baseline networks and sometimes even lower error than expert-designed domain-specific approaches.
METHODOLOGY OF ORGANIZING SELF-LEARNING IN TRAINING SPECIALISTS

Key words: preparation for independent work, classroom work, seminars and practical classes, preparation for laboratory work, computational and graphic work, preparation of dissertations and master's theses, application of theoretical knowledge in practice. Annotation: the scientific article Methodology for organizing independent training in training specialists is intended for the formation of future specialists, in which the processes important for the development of society are independent thinking, the ability to work freely, and its expression is recognized as a factor in the training of mature specialists.
The Effects of Flipped Learning on the Self-Directed Learning Ability and Class Participation of the Nursing Students

This study was to compare and investigate the change in self-directed learning ability and class participation between before and after flipped learning in 69 nursing students taking a course in mental health nursing at D University. The SPSS 18.0 program was were used to conduct descriptive statistics, Cronbach’ ɑ and Paired t-test. The flipped learning is a teaching and learning method where an on-line video is provided to students for their pre-learning in pre-class stage and in-depth learning is performed through discussion with peer learners and aid from teacher in in-class stage. The flipped learning was applied in this study for three weeks from 15 week class. In the pre-class stage, the video lectures about these contents were created based on PARTNER model using doczoom program. In the in-class stage, the interests in the learning contents were induced by presenting study question in earlier time and the sufficient time for discussion was allowed. The comprehension level of students was checked through feedback. In the last stage, post-class, the reflection diary was used to summarize learning contents and check class participation. The results showed higher scores of self-directed learning ability and class participation after flipped learning than those before it, suggesting that the flipped learning had positive effect on self-directed learning ability and class participation. For development of more efficient and active nursing class, further studies are needed to develop contents for pre-learning that are helpful to utilize the characteristics of flipped learning class, to improve the classroom environment for efficient implementation of flipped learning, and to diversify the models for teaching and learning of flipped learning.
Back Cover: Coherent Ising Machines with Optical Error Correction Circuits (Adv. Quantum Technol. 11/2021)

The graph on the cover shows the evolution of average energies of many trajectories in a coherent Ising machine (CIM). In some cases, the final state of CIM reaches a ground state (solution), while in some other cases the CIM states are trapped in the first, second and third excited states (non-solution). The CIM is a noise-driven stochastic computing machine and this image shows such a random character. For further information see article number 2100077 by Yoshihisa Yamamoto and co-workers.
Paired Examples as Indirect Supervision in Latent Decision Models

Compositional, structured models are appealing because they explicitly decompose problems and provide interpretable intermediate outputs that give confidence that the model is not simply latching onto data artifacts. Learning these models is challenging, however, because end-task supervision only provides a weak indirect signal on what values the latent decisions should take. This often results in the model failing to learn to perform the intermediate tasks correctly. In this work, we introduce a way to leverage paired examples that provide stronger cues for learning latent decisions. When two related training examples share internal substructure, we add an additional training objective to encourage consistency between their latent decisions. Such an objective does not require external supervision for the values of the latent output, or even the end task, yet provides an additional training signal to that provided by individual training examples themselves. We apply our method to improve compositional question answering using neural module networks on the DROP dataset. We explore three ways to acquire paired questions in DROP: (a) discovering naturally occurring paired examples within the dataset, (b) constructing paired examples using templates, and (c) generating paired examples using a question generation model. We empirically demonstrate that our proposed approach improves both in- and out-of-distribution generalization and leads to correct latent decision predictions.
Step Wise Approach to Structural Equation Modelling (SEM)

The trend of Structural Equation Modelling (SEM) applications in various disciplines is growing tremendously that the conceptual understanding of SEM becomes paramount interest of statisticians in the academics. Creating functional applications space for educating the learners and the teachers of statistics and mathematics the rudiments of SEM using the stepwise approach is a major focus of this work. In addition, this paper critical discussed the SEM fundamentals-specification, identification, Estimation discussed skeletally, SEM representation in Mulaik’s convention and RAM diagram creation in R. It recommended further studies on the re-specification and modification of modelling in SEM.
Online Signature Verification Using Fully Connected Deep Neural Networks

Biometric systems have been used in a wide range of applications.In this paper, we have introduced an online signature verification system using deep neural network models.The proposed system is designed to be used in a production environment and has accuracies on par with the state-of-the-art signature verification methods.It authenticates much faster than most of the existing signature verification systems (less than 2 seconds).To achieve better accuracies and faster training times, a feature vector with 42 features, both static and dynamic, is obtained from the signature sample.This feature vector is fed into the user identification model, which predicts the identity of the user with about 99% accuracy and based on this prediction, the user authentication model predicts if the signature is genuine or forged for that recognized user, with about 98% accuracy.The best possible accuracy achieved by the proposed system for 40 users is 97.5% and EER about 2%.The dataset from the Signature Verification Competition 2004 (SVC2004) was used to assess the performance of the proposed system.The results show that the proposed system competes with and even outperforms existing methods.
Keyphrase Extraction Using Neighborhood Knowledge Based on Word Embeddings

Keyphrase extraction is the task of finding several interesting phrases in a text document, which provide a list of the main topics within the document. Most existing graph-based models use co-occurrence links as cohesion indicators to model the relationship of syntactic elements. However, a word may have different forms of expression within the document, and may have several synonyms as well. Simply using co-occurrence information cannot capture this information. In this paper, we enhance the graph-based ranking model by leveraging word embeddings as background knowledge to add semantic information to the inter-word graph. Our approach is evaluated on established benchmark datasets and empirical results show that the word embedding neighborhood information improves the model performance.
인포테인먼트 프로그램을 활용한 한국어 토의 수업 방안 연구 -최고급 한국어 학습자를 대상으로-

Since infotainment programs, which combine information and entertainment, were introduced in 1998, viewers in and out of Korea were showing increased interest and enthusiasm for such programs. Especially, as Korean cultures and productions, such as K-POP, dramas, and movies, are gaining more attention among the fans abroad, these media became valuable as teaching materials. These changes promoted Korean classes to develop the materials into a teaching method. This research targets the superior Korean learners that have completed 6th level to utilize the infotainment programs for Korean discussion class model. The research establishes and suggests actual class discussion examples, along with Korean class models and syllabus, that have bases on existing Korean language classes and researches and adapt the infotainment programs as teaching materials. Also it includes survey analysis of appropriateness of the media and satisfactory levels from participants that have actually attended the model classes. (Ewha Womans University)
Secure and Authenticate Indexing of Medical Images Using Region of Non-Interest

Medical images produced from different modalities are stored in image database. These databases should be safely moved over the public network with control viably and Medical images need to be transmitted with the patient's information without altering the image data. Medical image watermarking is a proper strategy utilized for improving security. Furthermore, validation of medical data, which is pivotal and utilized for additional deduction what's more, reference. The task centres on the investigation of medical image watermarking techniques for securing and validating medical data. Moreover, it covers calculation for utilization of water stamping procedure on. The objective of this chapter is to discuss Secured Indexing of Lung CT Image (SILI) which is a secured way of indexing the lung CT images with the patient information. Authentication is provided using the sender's logo information and the secret key is used for embedding the watermark into the host image. Watermark is embedded into the Region of Noninterest (RONI) of the lung CT image. RONI is identified by segmenting the lung tissue from the CT scan image.
Compound Nouns Found in The Jakarta Post Website

This study is aimed to identify the types of compound nouns and analyze the meanings of compound nouns conveyed on The Jakarta Post website. Two theories applied in this study, the theory of compound proposed by Katamba and the theory of meaning proposed by Palmer. Eight articles from The Jakarta Post website were chosen as the data source. The selected articles are related to technology, lifestyle, and business from December 2020 until February 2021. Documentation method and note-taking technique used in collecting the data. The collected data was analyzed by using a descriptive qualitative method and triangulation technique. Meanwhile, in presenting the data analysis, this study used formal and informal methods. The result shows that all types of compound nouns based on the theory applied are found in the data source. The types are: noun + noun, adjective + noun, and preposition + noun. In terms of meaning, there are transparent and opaque meanings.
Innovation in Education The role of institutions of higher education in promoting and supporting the system of creativity and innovation: Reality and challenges:

The present study attempts, using the descriptive-analytical approach, In the study and analysis of the current situation of innovation and entrepreneurship. To activate the university's innovation system and ways to strengthen the creativity system at Imam Abdulrahman bin Faisal University, the services provided by the, through general education of services provided by the University Agency for Innovation and Entrepreneurship. One of the most important findings of the study, weak activation of the university’s creativity system represented by the lack of sufficient attention to raise awareness among the faculty and students and to enhance their creative sense, lack of motivational factors to encourage creativity at the university, and carry out innovative work in all areas of its specializations.The study, therefore, recommends more financial and non-financial support and better integration of resources.To bridge the current gaps in the foster environment and address the innovation and entrepreneurship challenges faced by Imam Abdul Rahman University.
Modelo de aula invertida en el aprendizaje de cursos generales en el residentado médico: Una experiencia innovadora: Flipped classroom in general courses learning in the medical residence: An innovative experience

espanolComo parte del desarrollo de asignaturas generales en el programa de residentado medico y por las caracteristicas de la coyuntura adaptativa, se vuelve necesario el plantear modelos pedagogicos como el aula invertida (flipped classroom), sin dejar de considerar los estilos de aprendizaje de los medicos residentes. En el primer ano del programa de residentado medico de la Universidad Nacional de Trujillo (UNT), durante la asignatura general de administracion en salud, se aplico el modelo de aula invertida mediante la plataforma Google Classroom, a 88 participantes; en quienes, ademas, se administro un cuestionario para determinar su estilo de aprendizaje de acuerdo con el modelo de Honey y Mumford. Las sesiones virtuales se encontraban disponibles con 72 horas de anticipacion y el contenido era accesible desde los dispositivos moviles de los estudiantes. Se programaron sesiones presenciales que incluyeron una exposicion breve (20 minutos) por parte del facilitador y luego la conformacion de grupos de trabajo de maximo nueve estudiantes (90 minutos). Se logro que las sesiones presenciales culminen con la entrega de un producto de alta calidad, la participacion cooperativa presencial y virtual fue muy alta y la satisfaccion de los participantes al culminar la asignatura fue muy buena, independientemente del estilo de aprendizaje de cada estudiante. Algunos participantes sugirieron que esta estrategia educativa fuera aplicada a el resto de asignaturas del programa de segunda especialidad. EnglishAs part of the development of general subjects in the medical residency program and due to the characteristics of the adaptive conjuncture, it becomes necessary to propose pedagogical models such as the flipped classroom, without ceasing to consider the learning styles of resident physicians. In the first year of the medical residency program at the National University of Trujillo (UNT), during the general subject of health administration, the flipped classroom model was applied to 88 participants through the Google Classroom platform; in addition, a questionnaire was applied to determine their learning style according to the Honey and Mumford model. The virtual sessions were available 72 hours previously and the content was accessible from the student's mobile devices. Face-to-face sessions were scheduled that included a brief presentation (20 minutes) by the facilitator and then the formation of working groups of a maximum of 9 students (90 minutes). The sessions culminated with the delivery of a high-quality product, the face-to-face and virtual cooperative participation was very high, and the satisfaction of the participants at the end of the course was very good, regardless of the learning style of each student. Some participants suggested that this teaching strategy should be applied to the other subjects of the second specialty program.
RAST: Domain-Robust Dialogue Rewriting as Sequence Tagging

The task of dialogue rewriting aims to reconstruct the latest dialogue utterance by copying the missing content from the dialogue context. Until now, the existing models for this task suffer from the robustness issue, i.e., performances drop dramatically when testing on a different dataset. We address this robustness issue by proposing a novel sequence-tagging-based model so that the search space is significantly reduced, yet the core of this task is still well covered. As a common issue of most tagging models for text generation, the model’s outputs may lack fluency. To alleviate this issue, we inject the loss signal from BLEU or GPT-2 under a REINFORCE framework. Experiments show huge improvements of our model over the current state-of-the-art systems when transferring to another dataset.
Comprehensive Expansion in Big Data: Innovation and Technology

In recent year, the use of the internet and cloud-based application has increased tremendously. With this lot of internet apps and social network have been developed. This apps and networks are growing very rapidly. Data on this network belongs to different data sizes and structures. This data in simple language is called big data. With the increasing demand for services, all the big data services are now automated, which stores and retrieve consumer's data. This data is in a different structure which makes it difficult to handle. This can include an example of road traffic data, different vehicle or person or location or atmosphere can produce a different set of data. Sometimes this data is structured while some times it may contain repeated, null or noisy data. This data may contain images and videos. The traditional database cannot handle this type of unstructured data. The best to solve this dimensionality problem is by using a dimensionality reduction technique. This paper provides the concept of big data collection, its analysis, storage and handling issues, security challenges, and other data handling technique.
Generating Mammography Reports from Multi-view Mammograms with BERT

Writing mammography reports can be errorprone and time-consuming for radiologists.In this paper we propose a method to generate mammography reports given four images, corresponding to the four views used in screening mammography.To the best of our knowledge our work represents the first attempt to generate the mammography report using deep-learning.We propose an encoder-decoder model that includes an EfficientNet-based encoder and a Transformerbased decoder.We demonstrate that the Transformer-based attention mechanism can combine visual and semantic information to localize salient regions on the input mammograms and generate a visually interpretable report.The conducted experiments, including an evaluation by a certified radiologist, show the effectiveness of the proposed method.Our code is available at https://github.com/sberbank-ai-lab/mammo2text.
Task-Adaptive Neural Network Search with Meta-Contrastive Learning

Most conventional Neural Architecture Search (NAS) approaches are limited in that they only generate architectures without searching for the optimal parameters. While some NAS methods handle this issue by utilizing a supernet trained on a large-scale dataset such as ImageNet, they may be suboptimal if the target tasks are highly dissimilar from the dataset the supernet is trained on. To address such limitations, we introduce a novel problem of \emph{Neural Network Search} (NNS), whose goal is to search for the optimal pretrained network for a novel dataset and constraints (e.g. number of parameters), from a model zoo. Then, we propose a novel framework to tackle the problem, namely \emph{Task-Adaptive Neural Network Search} (TANS). Given a model-zoo that consists of network pretrained on diverse datasets, we use a novel amortized meta-learning framework to learn a cross-modal latent space with contrastive loss, to maximize the similarity between a dataset and a high-performing network on it, and minimize the similarity between irrelevant dataset-network pairs. We validate the effectiveness and efficiency of our method on ten real-world datasets, against existing NAS/AutoML baselines. The results show that our method instantly retrieves networks that outperform models obtained with the baselines with significantly fewer training steps to reach the target performance, thus minimizing the total cost of obtaining a task-optimal network. Our code and the model-zoo are available at https://github.com/wyjeong/TANS.