Reviewerly/exHarmony huggingface dataset:
Validation and Feasibility of Differentially Private Local Aggregation of Real-Time Data Streams from Resource-Constrained Healthcare IoT Edge Devices

Differential privacy (DP) techniques provide important mathematical guarantees of privacy and in particular local DP mechanisms used to protect individual privacy without needing to trust any external entity. However, validation of these techniques is usually carried out using static datasets since IoT devices generating real-time streaming data pose additional difficulties. Hence, current work aims to validate the effectiveness of one such scheme, Privacy-Preserving Endpoint Aggregation (PPEA), on real-time private data obtained from resource-constrained edge devices by measuring utility metrics for the average operation aggregate function. This paper aims to study the feasibility of implementing PPEA for periodic real-time heart rate collection from fitness trackers, which are pervasive IoT devices within the personal healthcare domain capable of recording individual's private data, by considering factors like memory consumption, execution time, and power consumption. We address challenges concerning resource limitations on edge devices regarding lacking out-of-the-box provisions for implementing randomization techniques to achieve DP on streaming data.
A Novel Method For Reducing Risk With Optimized Anomaly Classifier

Deciding on an uncertain event may lead to risk. Uncertainty occurs due to the lack of knowledge of a particular event or a situation. The only way to avoid this is to analyze the Risk. The risk analyzed properly will reduce the impacts to a great extent. Here the risk management dealt with the anomaly detection mechanism, which is carried out by clustering the data to find the outlier as the anomaly.The proposed method employs the PCA mechanism for dimensionality reduction which is further clustered with K means algorithm and classified with decision tree algorithm.
Care Bundle for Medical Device-Related Pressure Injuries in Covid-19 times

The patients affected by COVID-19 have variable clinical symptoms, with some individuals with asymptomatic infection, going from mild illness with signs and nonspecific symptoms of acute respiratory illness to severe or fatal illness characterized by pneumonia, with severe respiratory impairment, which may result in hospitalization in intensive care units, orotracheal intubation, use of vasoactive drugs and septic shock (3).The elderly population and individuals with some chronic disease are vulnerable to presenting symptoms more severely, as aging and illness tend to decrease immunity against infections in general (4).In view of the severity of the symptoms triggered by COVID-19, many patients require intensive care, where the use of medical devices is essential for maintaining life.However, some critical patients have significant pressure and respiratory lability, where minimal movements can result in clinical decompensation (3).One of the approaches adopted for the treatment of acute respiratory distress syndrome is the prone position, which must be applied early (first 48 hours), once pronated, the patient must be kept for at least 16 hours in this position, triggering the risk of develop Pressure Injuries (PI), especially those related to medical devices (5).In the face of the pandemic, the risk of developing Medical device-related Pressure Injuries (MDRPI) is
Adversarially Adaptive Normalization for Single Domain Generalization

Single domain generalization aims to learn a model that performs well on many unseen domains with only one domain data for training. Existing works focus on studying the adversarial domain augmentation (ADA) to improve the model's generalization capability. The impact on domain generalization of the statistics of normalization layers is still underinvestigated. In this paper, we propose a generic normalization approach, adaptive standardization and rescaling normalization (ASR-Norm), to complement the missing part in previous works. ASR-Norm learns both the standardization and rescaling statistics via neural networks. This new form of normalization can be viewed as a generic form of the traditional normalizations. When trained with ADA, the statistics in ASR-Norm are learned to be adaptive to the data coming from different domains, and hence improves the model generalization performance across domains, especially on the target domain with large discrepancy from the source domain. The experimental results show that ASR-Norm can bring consistent improvement to the state-of-the-art ADA approaches by 1.6%, 2.7%, and 6.3% averagely on the Digits, CIFAR-10-C, and PACS benchmarks, respectively. As a generic tool, the improvement introduced by ASR-Norm is agnostic to the choice of ADA methods.
THE SIGNIFICANCE OF PROJECT METHOD FOR THE PROFESSIONAL DEVELOPMENT OF ARTISTS : HISTORICAL PERSPECTIVE AND PRESENT CONTEXT

The article highlights the current trends in the development and reformation of the higher education system, associated with changes in the competence approach in the training of future fine arts specialists. The issues of origin and formation of the project method in the pedagogical practice of higher educational institutions are revealed; the periodization of the development and dissemination of the project method globally and in domestic practice is specified. Scientific research comprehensively and thoroughly reveals the concept of creative project activity, its application in the training of fine arts specialists in higher educational institutions. The author analyzes a series of examples of the application of project activities in the training of specialists in various fields. The updating of the project method in the modern training of specialists in higher educational institutions is noted. It makes us look at project activity from a different angle regarding it as a means of forming the professional competence of future fine arts specialists. It is emphasized that these days project training is relevant and promising as it creates conditions for discovering the creative potential of students, increasing their motivation, promotes the acquisition of necessary skills and knowledge, development of their intellectual abilities and professional competences of a competitive specialist of the XXI century. Project activity will ensure the acquisition of universally applicable and professional competencies, socially significant knowledge, skills and abilities that meet the modern demand for education and the paradigm of student-centered learning, because such knowledge and skills enable young professionals to learn throughout life and succeed in professional activity. Basing on the results of the analysis of the achievements of international and Ukrainina scientists in the field of training specialists of creative specialties, it is noted that the application of project activity, including creative project activity, in professional training of future fine arts specialists can significantly improve the quality of their future professional activity, providing the necessary knowledge, skills and experience relevant for successful implementation in the modern world.
Latent Space Energy-Based Model of Symbol-Vector Coupling for Text Generation and Classification

We propose a latent space energy-based prior model for text generation and classification. The model stands on a generator network that generates the text sequence based on a continuous latent vector. The energy term of the prior model couples a continuous latent vector and a symbolic one-hot vector, so that discrete category can be inferred from the observed example based on the continuous latent vector. Such a latent space coupling naturally enables incorporation of information bottleneck regularization to encourage the continuous latent vector to extract information from the observed example that is informative of the underlying category. In our learning method, the symbol-vector coupling, the generator network and the inference network are learned jointly. Our model can be learned in an unsupervised setting where no category labels are provided. It can also be learned in semi-supervised setting where category labels are provided for a subset of training examples. Our experiments demonstrate that the proposed model learns well-structured and meaningful latent space, which (1) guides the generator to generate text with high quality, diversity, and interpretability, and (2) effectively classifies text.
Not All Knowledge Is Created Equal.

Mutual knowledge distillation (MKD) improves a model by distilling knowledge from another model. However, not all knowledge is certain and correct, especially under adverse conditions. For example, label noise usually leads to less reliable models due to the undesired memorisation [1, 2]. Wrong knowledge misleads the learning rather than helps. This problem can be handled by two aspects: (i) improving the reliability of a model where the knowledge is from (i.e., knowledge source's reliability); (ii) selecting reliable knowledge for distillation. In the literature, making a model more reliable is widely studied while selective MKD receives little attention. Therefore, we focus on studying selective MKD and highlight its importance in this work. Concretely, a generic MKD framework, Confident knowledge selection followed by Mutual Distillation (CMD), is designed. The key component of CMD is a generic knowledge selection formulation, making the selection threshold either static (CMD-S) or progressive (CMD-P). Additionally, CMD covers two special cases: zero knowledge and all knowledge, leading to a unified MKD framework. We empirically find CMD-P performs better than CMD-S. The main reason is that a model's knowledge upgrades and becomes confident as the training progresses. Extensive experiments are present to demonstrate the effectiveness of CMD and thoroughly justify the design of CMD. For example, CMD-P obtains new state-of-the-art results in robustness against label noise.
Learning and Planning in Average-Reward Markov Decision Processes

We introduce improved learning and planning algorithms for average-reward MDPs, including 1) the first general proven-convergent off-policy model-free control algorithm without reference states, 2) the first proven-convergent off-policy model-free prediction algorithm, and 3) the first learning algorithms that converge to the actual value function rather than to the value function plus an offset. All of our algorithms are based on using the temporal-difference error rather than the conventional error when updating the estimate of the average reward. Our proof techniques are based on those of Abounadi, Bertsekas, and Borkar (2001). Empirically, we show that the use of the temporal-difference error generally results in faster learning, and that reliance on a reference state generally results in slower learning and risks divergence. All of our learning algorithms are fully online, and all of our planning algorithms are fully incremental.
Neighborhood Contrastive Learning Applied to Online Patient Monitoring

Intensive care units (ICU) are increasingly looking towards machine learning for methods to provide online monitoring of critically ill patients. In machine learning, online monitoring is often formulated as a supervised learning problem. Recently, contrastive learning approaches have demonstrated promising improvements over competitive supervised benchmarks. These methods rely on well-understood data augmentation techniques developed for image data which do not apply to online monitoring. In this work, we overcome this limitation by supplementing time-series data augmentation techniques with a novel contrastive learning objective which we call neighborhood contrastive learning (NCL). Our objective explicitly groups together contiguous time segments from each patient while maintaining state-specific information. Our experiments demonstrate a marked improvement over existing work applying contrastive methods to medical time-series.
Cascaded Head-colliding Attention

Transformers have advanced the field of natural language processing (NLP) on a variety of important tasks. At the cornerstone of the Transformer architecture is the multi-head attention (MHA) mechanism which models pairwise interactions between the elements of the sequence. Despite its massive success, the current framework ignores interactions among different heads, leading to the problem that many of the heads are redundant in practice, which greatly wastes the capacity of the model. To improve parameter efficiency, we re-formulate the MHA as a latent variable model from a probabilistic perspective. We present cascaded head-colliding attention (CODA) which explicitly models the interactions between attention heads through a hierarchical variational distribution. We conduct extensive experiments and demonstrate that CODA outperforms the transformer baseline, by $0.6$ perplexity on \texttt{Wikitext-103} in language modeling, and by $0.6$ BLEU on \texttt{WMT14 EN-DE} in machine translation, due to its improvements on the parameter efficiency.\footnote{Our implementation is publicly available at \url{https://github.com/LZhengisme/CODA}.}
Randomized Search on a Grid of CNN Networks with Simplified Search Space

One of the prime issues in Convolutional Neural Networks (CNN) is the design of the architecture, which is mainly human crafted, requiring significant time and resources, including expert knowledge, as the number of design choices for CNN is quite large given the number of choices in the parameters of the CNN. In this paper, we analyze the different neural architecture search (NAS) approaches that have been used in recent times, and their issues, and propose a novel method of performing neural architecture search. Our proposed model uses a simplified search space, with a randomized search strategy. We utilize a cell-based architecture search method, with a cell having multiple CNN operations, along with the multiple link options within the operation nodes of a cell. The proposed model is then tested on the MNIST dataset, with significant comparable performance with state of art architecture for MNIST.
The Effectiveness Of Communication Technology, Ipad In Delivering Messages For School Students

iPads are a device designed through the combination of computers and tablets.The use of the iPad is widespread and it can cover all aspects of usages regardless of whether it is in the office for official tasks or for casual activities such as playing video games and surfing the internet.As a form of communication technology, the iPad is designed to facilitate communication through browsing the internet, receiving and sending emails, connecting to social media and for anything else on the go.This device is also a medium to gain knowledge in schools; as early as 2010, the iPad was used as a learning and teaching tool in selected schools in Malaysia.This study was conducted to examine the effectiveness of iPad usages among high school students.The qualitative method of semi-structural interviews was selected for this study.The researcher interviewed four teachers from secondary school who were using iPads as part of their learning and teaching.This method of data collection continued to interview eight students, face-toface to ensure the flexibility of using an iPad in the learning environment.The results of this study are to achieve the objective, which is to determine if the iPad, as a communication technology device, has a positive impact in terms of helping to acquire knowledge amongst the students.As the conclusion, the article is about the efficacy of iPad usages in schools, and the challenges of acquiring knowledge of the latest communication technology devices.
Low-Light Image and Video Enhancement Using Deep Learning: A Survey

Low-light image enhancement (LLIE) aims at improving the perception or interpretability of an image captured in an environment with poor illumination. Recent advances in this area are dominated by deep learning-based solutions, where many learning strategies, network structures, loss functions, training data, etc. have been employed. In this paper, we provide a comprehensive survey to cover various aspects ranging from algorithm taxonomy to open issues. To examine the generalization of existing methods, we propose a low-light image and video dataset, in which the images and videos are taken by different mobile phones' cameras under diverse illumination conditions. Besides, for the first time, we provide a unified online platform that covers many popular LLIE methods, of which the results can be produced through a user-friendly web interface. In addition to qualitative and quantitative evaluation of existing methods on publicly available and our proposed datasets, we also validate their performance in face detection in the dark.This survey together with the proposed dataset and online platform could serve as a reference source for future study and promote the development of this research field. The proposed platform and dataset as well as the collected methods, datasets, and evaluation metrics are publicly available and will be regularly updated.
Secured Health Using Machine Learning Adoption In Blockchain

As a promising candidate for the creation of internet offers Block chain is growing. A decentralized, secure and auditable way to alter and authenticate data by transactions is kilometers away, involving a trustworthy third celebration. As a result, fitness treatment domain was newly combined with block chain technologies. The BT decentralized database illustrates stability and privacy. It also guarantees that figures are protected and valid by the consensus process. Nevertheless, modern security issues, including big threats and double spending, are rising. Facts are encrypted in sophisticated encryption for handling the above problems Collection of guidelines for standards (AES). Analyzing these facts raises the value of computer education (ML). ML provides the rational number of records for making precise decisions. In order to improve the precision of effects, knowledge reliability and their sharing are very important in ML. The synthesis of these two methods (ML and BT) may have very special implications. On this text, we present a detailed review of ML adoption to make clever applications primarily BT-based additionally vulnerable to attacks. Various traditional ML methods exist, for example, support Vector Machines (SVM), clustering, bagging and the creation of (DL) information. Includes CNN and Long Speed Time Reminiscence (LSTM) can be used to evaluate attacks on a block chain based network. If it wishes to be calm, medium happy, small healthy material, we are using naive bayes algorithms for classifying fitness data.
A Hybrid Approach to Scalable and Robust Spoken Language Understanding in Enterprise Virtual Agents

Ryan Price, Mahnoosh Mehrabani, Narendra Gupta, Yeon-Jun Kim, Shahab Jalalvand, Minhua Chen, Yanjie Zhao, Srinivas Bangalore. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers. 2021.
Aula virtual interactiva para la enseñanza y el aprendizaje de la asignatura de Estequiometría a través de las tecnologías de la información y la comunicación

espanolSe describe la incorporacion de un modelo Blended Learning a la asignatura de Estequiometria del programa de Ingenieria Quimica de la Universidad Industrial de Santander, traves de un aula virtual interactiva basada en el software libre Moodle, que integra las bondades de la instruccion presencial con funcionalidades de e-Learning. El modelo se diseno con el proposito de dar una vision general de las operaciones y procesos quimicos, desde los principios de conservacion de la materia. El curso de Estequiometria fue escogido para la implementacion de este modelo, debido a la preocupacion que se ha generado en torno al bajo nivel de comprension del curso por parte de los estudiantes, quienes lo asocian solamente al desarrollo matematico en temas como reactivo limite, conversion, entre otros. Las principales dificultades de comprension que se hallaron durante la fase de exploracion fueron el punto de partida para el diseno del aula virtual y la seleccion de las estrategias pedagogicas pertinentes. Posteriormente, se elaboraron y organizaron los recursos que abordan los contenidos y se crearon las actividades para el seguimiento del proceso de aprendizaje. Una vez terminada el aula virtual, los estudiantes y profesores validaron la herramienta para incorporarla en la asignatura. EnglishThe present work intends to propose the incorporation of a Blended Learning model in the educational processes currently carried out at the Universidad Industrial de Santander using an interactive virtual learning classroom based on free software (Moodle) with the objective of integrate the best of classroom instruction with functionalities of e-learning. For the development of this work was selected the Stoichiometry course, being this subject the pillar on which is based the chemical engineer work. The Stoichiometry course has generated concern for the level of understanding of students, who associate it only to exclusively mathematical development in topics as limiting reactive, conversion, among others. To build this proposal, the main difficulties founds in the students during the exploration phase were taken as a starting point and, based on these, the relevant pedagogical strategies were determined. Subsequently, the materials and resources that address the contents established in the program of the subject were elaborated and organized and created activities corresponding to the monitoring of the learning process. Once the virtual classroom was finished, students and professors validated the tool to incorporate it in the teaching and learning process of stoichiometry.
Aplikasi Propas (Program Pengarsipan Surat) Pada Kantor Desa Cihambulu-Subang

The process of archiving mails at the Cihambulu Village office the writing is still conventional. there are some deficiency including the process of writing that is not neat, archiving still using the ledger. Therefore required a program that can solving the problem, so the problems that occur at this Cihambulu Village office can be resolved. The concept of designing this system refers to the activities of data processing incoming mail, data processing outgoing mail, disposition making, to the manufacture of incoming mail reports and outgoing mail. some stages in the development or design of this program are software requirements analysis, program design, program code generation, program testing. The making this program using JAVA programming language, MySQL as database and windows 7 as operating system. Research methods used in the design of this system is the method of interviewing, observation, and literature study. While the purpose of designing this system is to produce a mail archiving program so that it can assist employees in data processing incoming mail, data processing outgoing mail, disposition making, making reports incoming mails and outgoing mail.
Encrypted Databases: Progresses and Challenges

In recent years, we have witnessed an upsurge in cyber-attacks and data breach incidents that put tremendous data at risk, affect millions of users, and cause severe economic losses. As an in-depth defence to counter the persistent and pervasive security threats, maintaining data in always encrypted form is becoming a trend and even a regulatory requirement. Satisfying the demand is particularly challenging in the context of databases, which, as a pillar in modern computing infrastructure, provide indispensable means to organize, store and retrieve data at different scales. The difficulty lies in how to perform the database query processing over encrypted data while meeting the requirements of security, performance, and complex query functions.