Reviewerly/exHarmony huggingface dataset:
Improving the sample-efficiency of neural architecture search with reinforcement learning

Designing complex architectures has been an essential cogwheel in the revolution deep learning has brought about in the past decade. When solving difficult problems in a datadriven manner, a well-tried approach is to take an architecture discovered by renowned deep learning scientists as a basis (e.g. Inception) and try to apply it to a specific problem. This might be sufficient, but as of now, achieving very high accuracy on a complex or yet unsolved task requires the knowledge of highly-trained deep learning experts. In this work, we would like to contribute to the area of Automated Machine Learning (AutoML), specifically Neural Architecture Search (NAS), which intends to make deep learning methods available for a wider range of society by designing neural topologies automatically. Although several different approaches exist (e.g. gradient-based or evolutionary algorithms), our focus is on one of the most promising research directions, reinforcement learning. In this scenario, a recurrent neural network (controller) is trained to create problem-specific neural network architectures (child). The validation accuracies of the child networks serve as a reward signal for training the controller with reinforcement learning. The basis of our proposed work is Efficient Neural Architecture Search (ENAS), where parameter sharing is applied among the child networks. ENAS, like many other RL-based algorithms, emphasize the learning of child networks as increasing their convergence result in a denser reward signal for the controller, therefore significantly reducing training times. The controller was originally trained with REINFORCE. In our research, we propose to modify this to a more modern and complex algorithm, PPO, which has demonstrated to be faster and more stable in other environments. Then, we briefly discuss and evaluate our results.
Alternative Therapy for Acrophobia Using Virtual Reality System

Arcophobia is classified as a specific type of phobia. One of the treatment methods used is behavior therapy which will allow the patient to confront the fear, starting with what the patient is afraid of and then increasing the level of fear. Virtual reality technology can make users feel like in a real environment of fear and that treatment with behavioral therapy is a safe and easy to control. The purposes of this research are to develop and evaluate the virtual reality system that is used as an alternative therapy tool for acrophobia. The system was developed with the Unreal Engine to create a simulated environment and interaction in a 3D simulation environment using Oculus Rift CV1. There were 20 subjects in the experiment, divided into two groups: 1) fear of height group and 2) no fear of height group. The tools used to evaluate the system are 3 sets of questionnaires: 1) AQ (Acrophobia Questionnaire) used to measure anxiety levels and avoidance when encountering height situations 2) BSQ (Body Sensations Questionnaire) used to measure the severity of fear at related to physical symptoms and 3) questionnaire to measure the appropriateness of system usage. The results showed that the fear of height group yields higher anxiety and avoidance levels, with the average AQ anxiety and AQ avoidance of 3.76 and 1.28 respectively, and higher level of severity of fear associated with physical symptoms, with the average BSQ score of 3.49. The appropriateness of the usability from the two groups are thought to be appropriate by the overall high level.
Dynamic Slimmable Denoising Network

Recently, tremendous human-designed and automatically searched neural networks have been applied to image denoising. However, previous works intend to handle all noisy images in a pre-defined static network architecture, which inevitably leads to high computational complexity for good denoising quality. Here, we present dynamic slimmable denoising network (DDS-Net), a general method to achieve good denoising quality with less computational complexity, via dynamically adjusting the channel configurations of networks at test time with respect to different noisy images. Our DDS-Net is empowered with the ability of dynamic inference by a dynamic gate, which can predictively adjust the channel configuration of networks with negligible extra computation cost. To ensure the performance of each candidate sub-network and the fairness of the dynamic gate, we propose a three-stage optimization scheme. In the first stage, we train a weight-shared slimmable super network. In the second stage, we evaluate the trained slimmable super network in an iterative way and progressively tailor the channel numbers of each layer with minimal denoising quality drop. By a single pass, we can obtain several sub-networks with good performance under different channel configurations. In the last stage, we identify easy and hard samples in an online way and train a dynamic gate to predictively select the corresponding sub-network with respect to different noisy images. Extensive experiments demonstrate our DDS-Net consistently outperforms the state-of-the-art individually trained static denoising networks.
On the Double Descent of Random Features Models Trained with SGD

We study generalization properties of random features (RF) regression in high dimensions optimized by stochastic gradient descent (SGD) in under-/over-parameterized regime. In this work, we derive precise non-asymptotic error bounds of RF regression under both constant and polynomial-decay step-size SGD setting, and observe the double descent phenomenon both theoretically and empirically. Our analysis shows how to cope with multiple randomness sources of initialization, label noise, and data sampling (as well as stochastic gradients) with no closed-form solution, and also goes beyond the commonly-used Gaussian/spherical data assumption. Our theoretical results demonstrate that, with SGD training, RF regression still generalizes well for interpolation learning, and is able to characterize the double descent behavior by the unimodality of variance and monotonic decrease of bias. Besides, we also prove that the constant step-size SGD setting incurs no loss in convergence rate when compared to the exact minimum-norm interpolator, as a theoretical justification of using SGD in practice.
Analysis Model for Identifying Negative Posts Based on Social Media

Cyberbullying is an act that violates where this crime is committed on social media, e.g. the Twitter application. This action is difficult to detect, thus someone has to report the case before detection. Identification of cyberbullying tweets aims to classify tweets containing the bullying content. Several studies gave output results in the identification of whether the tweet is positive or negative, or bully or not. It can be confusing when analyzing the classification results as it only results in two classes. In this research, by using the conception of text mining Naïve Bayes, the model that can categorize into more detail was developed. It does not only categorize the contents are bullying or not, however it can classify the contents into five detail categories. The classification process done based on the dataset and label where the schema to build dataset was proposed scientifically from this study. The contribution of this research is to offer the algorithm to collect and label the Indonesian language dataset and then classify the types of sarcasm, namely animal, psychology and stupidity, disabled person, attitude, and general bullying. The research hypothesis is that analysis from the classification results can be improved by classifying bully content into the five classes. Dataset was collected by the researcher and labelling was done manually based on study literature. The result proves the model can use to classify cyberbullying content in social media with 99.15% accuracy.
About One Algorithm For Generating Reference Data In Pattern Recognition

In this paper the issues like preprocessing of medical data, reclassification of the training sets and determining the importance of classes, formation of reference tables, selection of an informative features set that differentiate between class objects, formed by medical professionals are discussed and solved. Mainly in the most studied references [5-8, 11-13] the Fisher's criterion is used to obtain solutions to problems/tasks. Also for solving problems, the algorithms for an estimate calculation as well as the related software programs are used. For all cases, algorithms and software programs are suggested.The study consists of two important steps. The first step is to build a reference table, based on the importance of the features and objects as well as their contribution to the classes [1-4, 9, 10]; the second step is concerned with the choice of the most useful characteristic features set to be investigated. This corresponds to solving the issue of selection of set of informative features from a given table, their visualization, and the determination of the contribution of the features set to the formation of classes [1-13].
Sentiment Analysis-Enhancements and Applications

The concept of Natural Language Processing that deals with problems of identifying the sentiment from the voice or text of a speaker or writer and then use that analysis further for making predictions, market survey, customer service, product satisfaction, precision targeting etc. is called Sentiment analysis. From one viewpoint, it is an abstract evaluation of something dependent on close to home observational experience. It Is mostly established in target realities and incompletely governed by feelings. Then again, a sentiment can be deciphered as a kind of measurement in the information in regards to a specific subject. It is a lot of markers that mix present a point of view, i.e., perspective for the specific issue. So as to enhance the accuracy of sentiment analysis/classification, it is imperative to appropriately recognize the semantic connections between the various words and phrases that are describing the subject or aspect. This can be done by applying semantic analysis with a syntactic parser and supposition vocabulary. This research will discuss different sets of approaches for application or domain specific problems and then compare them to obtain the best possible approaches to the problem of sentiment analysis.
La formación docente en el estado de Chihuahua: Entre el desafío sanitario y la resiliencia docente

El artículo propone recuperar la respuesta de las Instituciones Formadoras y Actualizadoras de Docentes en el estado de Chihuahua ante los diversos desafíos impuestos por la pandemia del Coronavirus (COVID-19). El ejercicio cualitativo analiza la experiencia de 10 instituciones a partir de la voz de sus estudiantes, docentes y directivos en torno a los cambios en los procesos académicos, administrativos y organizativos. Se concluye que, la pandemia ha afectado de diferente manera y con distinta intensidad a cada institución. Pese a los logros, la experiencia analizada devela el relativo éxito del uso de las plataformas virtuales ante tres condiciones básicas: conectividad, competencias tecnológicas y habilidades socioemocionales del profesorado. Destaca la importancia de implementar acciones tutoriales, de resiliencia o de concienciación de las necesidades, sentimientos y malestares de docentes y estudiantes. Resulta oportuno recuperar las experiencias de otras instituciones e interpelar especialmente a estudiantes, tesistas y egresados. Teacher training in the state of Chihuahua: Between the health challenge and teacher resilience Abstract The article proposes to recover the response of the Teacher Training and Updating Institutions in the state of Chihuahua regarding the various challenges imposed by the Coronavirus Disease (COVID-19). The qualitative exercise analyzes the experience of 10 institutions based on the voice of their students, teachers, and principals regarding changes in academic, administrative, and organizational processes. It is concluded that the pandemic has affected each institution in different ways and with diverse intensity. Despite the achievements, the experience analyzed reveals the relative success of the using virtual platforms in the face of three basic conditions: connectivity, technological competencies, and socio-emotional skills of the teaching staff. It highlights the importance of implementing tutoring, resilience, or awareness actions of teachers and students' needs, feelings, and sufferings. It is opportune to recover the experiences of other institutions and to question especially students, thesis students and graduates. Keywords: Access to education, online learning, distance education, teacher education, educational technology.
Solving Optimization Problems over the Stiefel Manifold by Smooth Exact Penalty Function

In this paper, we present a novel penalty model called ExPen for optimization over the Stiefel manifold. Different from existing penalty functions for orthogonality constraints, ExPen adopts a smooth penalty function without using any first-order derivative of the objective function. We show that all the first-order stationary points of ExPen with a sufficiently large penalty parameter are either feasible, namely, are the first-order stationary points of the original optimization problem, or far from the Stiefel manifold. Besides, the original problem and ExPen share the same second-order stationary points. Remarkably, the exact gradient and Hessian of ExPen are easy to compute. As a consequence, abundant algorithm resources in unconstrained optimization can be applied straightforwardly to solve ExPen.
An Information-Theoretic Analysis of the Cost of Decentralization for Learning and Inference under Privacy Constraints

In vertical federated learning (FL), the features of a data sample are distributed across multiple agents. As such, inter-agent collaboration can be beneficial not only during the learning phase, as is the case for standard horizontal FL, but also during the inference phase. A fundamental theoretical question in this setting is how to quantify the cost, or performance loss, of decentralization for learning and/or inference. In this paper, we study general supervised learning problems with any number of agents, and provide a novel information-theoretic quantification of the cost of decentralization in the presence of privacy constraints on inter-agent communication within a Bayesian framework. The cost of decentralization for learning and/or inference is shown to be quantified in terms of conditional mutual information terms involving features and label variables.
Load Balance Optimization of Distributed Massive Database Information Acceptance and Processing in the Internet of Things Scenario

In order to make full use of the resources of each service node in a cluster to improve the overall performance of the cluster, it is necessary to select the appropriate load balancing technology and efficient load balancing algorithm to allocate client access requests. Focusing on the load imbalance problem of Spark, this paper proposes an adaptive task scheduling strategy based on Spark cluster to achieve the load balancing optimization of Spark clusters. This strategy uses the heuristic algorithm of the ant colony simulated annealing fusion algorithm to optimize the task scheduling strategy of the Spark cluster according to the node’s current load and computing resources. This achieves the appropriate allocation of tasks for the purpose of load balancing optimization, thereby improving the cluster’s task-processing efficiency. In order to achieve dynamic load balancing on the Reduce side, a dynamic and lightweight division strategy is adopted. This strategy combines the load information for the dynamic design of the sampling scale and the lightweight design of the sampling method, and combines the sampling data and node performance to determine the number of Reducers. In addition, a division strategy is formulated based on the partition analysis of the sampling results and load information. After experimentation and analysis, it is concluded that the optimization technology has significantly improved the parallel computing performance of the Map Reduce cluster.Keywords: load balancing; ant colony-simulated annealing algorithm; distributed massive database; Internet of Things (IoT) scenarioCite AsJ. Wang, Load Balance Optimization of Distributed Massive Database Information Acceptance and Processing in the Internet of Things Scenario, Engineering Intelligent Systems, vol. 29 no. 5, pp. 279-289, 2021.
Language Models As or For Knowledge Bases

Pre-trained language models (LMs) have recently gained attention for their potential as an alternative to (or proxy for) explicit knowledge bases (KBs). In this position paper, we examine this hypothesis, identify strengths and limitations of both LMs and KBs, and discuss the complementary nature of the two paradigms. In particular, we offer qualitative arguments that latent LMs are not suitable as a substitute for explicit KBs, but could play a major role for augmenting and curating KBs.
Generalization Techniques Empirically Outperform Differential Privacy against Membership Inference

Differentially private training algorithms provide protection against one of the most popular attacks in machine learning: the membership inference attack. However, these privacy algorithms incur a loss of the model's classification accuracy, therefore creating a privacy-utility trade-off. The amount of noise that differential privacy requires to provide strong theoretical protection guarantees in deep learning typically renders the models unusable, but authors have observed that even lower noise levels provide acceptable empirical protection against existing membership inference attacks. In this work, we look for alternatives to differential privacy towards empirically protecting against membership inference attacks. We study the protection that simply following good machine learning practices (not designed with privacy in mind) offers against membership inference. We evaluate the performance of state-of-the-art techniques, such as pre-training and sharpness-aware minimization, alone and with differentially private training algorithms, and find that, when using early stopping, the algorithms without differential privacy can provide both higher utility and higher privacy than their differentially private counterparts. These findings challenge the belief that differential privacy is a good defense to protect against existing membership inference attacks
Factors Influencing Medical Student’S Satisfaction on Online Learning During Covid-19 Pandemic

Education has been affected by the COVID-19 pandemic.The pandemic causes banned access to schools and universities.The students are moving into online learning from the conventional learning.This study aimed to analyze the factors influencing the satisfaction of medical students on online learning during the pandemic of COVID-19.This was a cross-sectional study at the Al-Azhar Islamic University, Mataram, West Nusa Tenggara.A total of 260 medical students was enrolled in this study.The dependent variable was online learning satisfaction.Independent variable were knowledge, attitude, and perception.Data were collected using questionnaire and analyzed using multiple logistic regression.High knowledge (OR= 1.94; 95%CI= 1.07 to 3.51; p= 0.029), positive attitude (OR=1.96;95%CI= 1.08 to 3.55; p= 0.026), and positive perception (OR= 7.02; 95%CI= 3.90 to 12.63; p< 0.001) increased the positive medical student's satisfaction towards online learning during pandemic COVID-19.Medical student's satisfaction during pandemic COVID-19 increases by high knowledge, positive attitude, and positive perception.The university needs to provide a good online learning environment to increase medical student's satisfaction.
The New Method and Mechanism for Determining Professional Stresses in the Service of Military Workers

This scientific article examines the research and empirical study conducted by the co-authors based on their service experience and develops practical proposals, recommendations and software based on a new mechanism for detecting occupational stresses in the military personnel.
ENHANCING STUDENTSâ€™ VOCABULARY MASTERY BY APPLYING DICTOGLOSS TECHNIQUE TO THE ELEVENTH GRADE STUDENTS OF SMA DELI MURNI BANDAR BARU IN THE ACADEMIC YEAR OF 2020/2021

This research was primarily conducted to prove that the application of Dictogloss technique could enhance the vocabulary mastery of the eleventh grade students of SMA Deli Murni Bandar Baru Bonaventura Class in the Academic Year of 2020/2021 and to describe their responses towards the application of Dictogloss technique in the process of learning vocabulary mastery. This research is Classroom Action Research which was perfomed in two cycles. Each cycle consisted of four meetings. The quantitative data analysis showed that the students mean score increased continually from 44,16 in pre-test, 59,17 in formative test, and 70,83 in post-test. And the percentage of the students who passed the mastery minimum criteria from pre-test to post-test is 60,39%. The qualitative data analysis showed that the students responses after being taught by using Dictogloss technique. It can be seen from observation sheets and field notes; they felt more enthusiastic and motivated to learn vocabulary mastery after Dictogloss technique was applied. The questionnaire data also showed that students are agree with the application of using Dictogloss technique and found it effective in teaching vocabulary mastery. It is suggested that English teachers using Dictogloss technique as an alternative media in teaching vocabulary to the students in Senior High School in order to enhance their studentsâ€™ vocabulary mastery.
Health & Healing in the School Library: A Return to Better?

After a year of turmoil and unsteadiness in people's lives, the country, and the world, taking steps to improve and increase self-care is one thing one can do to reach for better. Compassion fatigue, being overworked and underpaid, and the emotional stress that is added simply because educators care about each and every one of their students can take a toll on their capacity to care for themselves both physically and mentally. While there is an attempt to return to normal in 2021, Ashley Cooksey and Jennifer Luetkemeyer propose in this article, a better normal where people value their own mental and emotional well-being as much as their students. The authors provide some methods, such as mindfulness and visual journaling, to process and begin self-care.