Reviewerly/exHarmony huggingface dataset:
A Low-Cost Prototype for an Online High-Impedance Vegetation Fault Detection Module

This paper presents a low-cost feasibility prototype for vegetation High-Impedance Fault (HIF) detection. The method relies on high-frequency sweep sampling based signal processing for classifying high-dimensional network signals. The prototype is set to perform signal processing calculations for feature extraction and classify signals with an ensemble of boosted decision trees. A card-sized single board computer was used to sample and classify analog voltage signals online. The demonstrated performance proved that at least two implementation concerns could be addressed with this low-cost prototype. These are the effects of environmental noise on detection accuracy and computational complexity (cost) in high-dimensional data processing. Evaluation of the experimental set-up was accomplished by different experiments and their respective results were used as evidence of a successful feasibility prototype. Results show that sampling only one power cycle at 50 Hz can be sufficient for an accurate HIF detection. A classification accuracy of 95.23% was achieved in hardware for split data tests compared to 97.53% accuracy in desktop analysis of the same dataset.
Self-Regulated Learning Siswa SMP N 1 Lubuk Alung

This study aims to describe the self-regulated learning of SMP Negeri 1 Lubuk Alung students. This study uses a quantitative approach with descriptive research methods with a sample size of 268 students. Sampling using proportional random sampling technique. The instrument used was a self-regulated learning questionnaire, a Likert scale model. This study measures self-regulated learning which includes: cognitive, motivation and behavior. The findings of this study reveal: (1) on average the overall students of SMP Negeri 1 Lubuk Alung have self-regulated learning in the high category (69.81%), (2) the overall average grade VII students of SMP Negeri 1 Lubuk Alung have self-regulated learning. -regulated learning with a high category (70.41%), (3) the overall average grade VIII students of SMP Negeri 1 Lubuk Alung have self-regulated learning with a high category (69.09%), (4) the overall average grade students IX SMP Negeri 1 Lubuk Alung has a high category of self-regulated learning (69.92%)
Empirical Studies on the Convergence of Feature Spaces in Deep Learning

While deep learning is effective to learn features/representations from data, the distributions of samples in feature spaces learned by various architectures for different training tasks (e.g., latent layers of AEs and feature vectors in CNN classifiers) have not been well-studied or compared. We hypothesize that the feature spaces of networks trained by various architectures (AEs or CNNs) and tasks (supervised, unsupervised, or self-supervised learning) share some common subspaces, no matter what types of DNN architectures or whether the labels have been used in feature learning. To test our hypothesis, through Singular Value Decomposition (SVD) of feature vectors, we demonstrate that one could linearly project the feature vectors of the same group of samples to a similar distribution, where the distribution is represented as the top left singular vector (i.e., principal subspace of feature vectors), namely P-vectors. We further assess the convergence of feature space learning using angles between P-vectors obtained from the well-trained model and its checkpoint per epoch during the learning procedure, where a quasi-monotonic trend of convergence to small angles has been observed. Finally, we carry out case studies to connect P-vectors to the data distribution, and generalization performance. Extensive experiments with practically-used MLP, AE and CNN architectures for classification, image reconstruction, and self-supervised learning tasks on MNIST, CIFAR-10 and CIFAR-100 datasets have been done to support our claims with solid evidences.
Topic-aware Contextualized Transformers

Training on disjoint fixed-length segments, Transformers successfully transform static word embeddings into contextualized word representations. However, they often restrict the context of a token to the segment it resides in and hence neglect the flow of contextual information across segments, failing to capture longer-term dependencies beyond the predefined segment length. This paper uses a probabilistic deep topic model to provide contextualized embeddings at both the token and segment levels. It also introduces topic self-attention and a contextual next-word embedding guided topic select-attention, injecting contextualized topic information into Transformer-based architectures. Moving beyond conventional Transformers that ignore longer-range word dependencies and contextualize their word representations at the segment level, the proposed method not only captures global semantic coherence of all segments and global word concurrence patterns, but also enriches the representation of each token by adapting it to its local context, which is not limited to the segment it resides in and can be flexibly defined according to the task. Experiments on various corpora show that adding only a few extra parameters, the proposed topic-aware contextualized transformers consistently outperform their conventional counterparts, and can be used to generate coherent sentences and paragraphs.
Performance Tuning Techniques for Face Detection Algorithms on GPGPU

Face detection algorithms varies in speed and performance on GPUs. Different algorithms can report different speeds on different GPUs that are not governed by linear or near-linear approximations. This is due to many factors such as register file size, occupancy rate of the GPU, speed of the memory, and speed of double precision processors. This paper studies the most common face detection algorithms LBP and Haar-like and study the bottlenecks associated with deploying both algorithms on different GPU architectures. The study focuses on the bottlenecks and the associated techniques to resolve them based on the different GPUs specifications.
Integration and Feature Identification inMulti-layer Networks using a Heat Diffusion Approach

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Data-Efficient Reinforcement and Transfer Learning in Robotics

In the past few years, deep reinforcement learning (RL) has shown great potential in learning action selection policies for solving different tasks.Despite its impressive success in games, several ...
АЛГОРИТМЫ ФОРМИРОВАНИЯ ПРОЕКТНОГО МЫШЛЕНИЯ У СТУДЕНТОВ ВУЗОВ В ПРОЦЕССЕ РАЗРАБОТКИ СОЦИАЛЬНОГО ПРОЕКТА

The article considers project thinking as a strategy for developing the life trajectory of University students taking into account the integration of professional competencies with flexible personal skills. The study is a description of the design stages in the context of analyzing the features of cognitive operations that underlie the implementation of a particular stage. Cognitive operations are understood as functions of various thinking types: visual-figurative, subject-effective and abstract-logical, and styles of thinking: synthetic, pragmatic, ideological, analytical, algorithmic and realistic. A special place in the research is occupied by the results of the analysis of youth value orientations as well as the role of pragmatic and constructive components in the distribution of material and intellectual resources in a social project.
Text length adaptation in sentiment classification

Can a text classifier generalize well for datasets where the text length is different? For example, when short reviews are sentiment-labeled, can these transfer to predict the sentiment of long reviews (i.e., short to long transfer), or vice versa? While unsupervised transfer learning has been well-studied for cross domain/lingual transfer tasks, Cross Length Transfer (CLT) has not yet been explored. One reason is the assumption that length difference is trivially transferable in classification. We show that it is not, because short/long texts differ in context richness and word intensity. We devise new benchmark datasets from diverse domains and languages, and show that existing models from similar tasks cannot deal with the unique challenge of transferring across text lengths. We introduce a strong baseline model called BaggedCNN that treats long texts as bags containing short texts. We propose a state-of-the-art CLT model called Length Transfer Networks (LeTraNets) that introduces a two-way encoding scheme for short and long texts using multiple training mechanisms. We test our models and find that existing models perform worse than the BaggedCNN baseline, while LeTraNets outperforms all models.
Face Recognition and Face Detection Using Open Computer Vision Classifiers and Python

Identification of a person by looking at the image is really a topic of interest in this modern world. There are many different ways by which this can be achieved. This research work describes various technologies available in the open-computer-vision (OpenCV) library and methodology to implement them using Python. To detect the face Haar Cascade are used, and for the recognition of face eigenfaces, fisherfaces, and local binary pattern, histograms has been used. Also, the results shown are followed by a discussion of encountered challenges and also the solution of the challenges.
Neonatology Today is Committed to Improving the Digital Experience of the Readers

took over the production of Neonatology Today in March 2018, there has been a steady increase in the number of articles in each issue, thanks to the expansion of the contracted column articles (Figure 1).We want to
A Cognitive Pedagogical Grammar of Directive Speech Acts II: Activities and Practice Materials

Chapter 5 draws on the description of the meaning and form of directive speech acts reported in Chapter 4 in order to offer a collection of practical activities for their teaching. Activities will be grouped attending to the semantic, formal, or contrastive aspects that need to be taught. Some of them will be designed to improve the students recognition and production of those illocutionary constructions and linguistic realisation procedures which allow the communication of the different illocutionary forces.
University Students’ Readiness towards Mobile Learning: The Stages of Change Model

Information seeking modes are changing rapidly due to advancements in portable devices such as smartphones. The functionality of smartphones is helping individuals to enhance their online learning experiences. The study was conducted to assess the readiness level among students towards the adoption of mobile learning at The Islamia University of Bahawalpur. A survey method of research was used to collect data for this study. A self-administered questionnaire was distributed through convenience sampling among the enrolled students of The Islamia University of Bahawalpur. Of the 183 respondents, the majority of 118 (64.5%) were female. Overwhelming, 170 (92.9%) respondents' usage frequency was 'daily' for accessing information through mobile. Stages of change model indicated that most of the respondents were at the 'maintenance stage' indicating that they were regularly using mobile for learning. The main barriers to mobile learning included: 'mobile learning causes sore eyes', and 'mobile screen size makes it difficult to concentrate on contents. The study concluded that a high level of readiness exists among students towards adopting mobile learning as they are regularly using smartphones to access the information from online resources to fulfill the requirements of formal learning. A statistically significant difference was found in the readiness level as female students are more likely to adopt m-learning than male students. The study's findings are useful for librarians, faculty members, IT experts, and policymakers of the participating university in designing mobile-related services for students to enhance learning  communications and collaborations.
KETG: A Knowledge Enhanced Text Generation Framework

Embedding logical knowledge information into text generation is a challenging NLP task. In this paper, we propose a knowledge enhanced text generation (KETG) framework, which incorporates both the knowledge and associated text corpus to address logicality and diversity in text generation. Specifically, we validate our framework on rhetorical text generation from our newly built rhetoric knowledge graph. Experiments show that our framework outperforms baseline models such as Transformer and GPT-2, on rhetorical type control, semantic comprehensibility and diversity.
Selection of Non-Pharmacological Treatments for mild Cognitive Impairment in older Adults with Neutrosophic-AHP

The aging of population includes a social group that has increased in recent years. Properly attending to the conditions of the elderly represents a problem to be addressed by Public Health institutions. A pathology that occurs very frequently is the cognitive impairment. Selecting the correct treatment for this type of condition is subject to the set of criteria that characterize the patient. Problems of this nature have been addressed by science from the implementation of artificial intelligence. The present research aims to develop a method for the selection of non-pharmacological treatments for mild cognitive impairment in older adults. The method operates using the neutrosophic AHP to model indeterminacy. The result is a case study with the implementation of the proposed method as an example of applicability.
EMPOWERMENT OF VILLAGE LIBRARY IN INCREASING COMMUNITY READING INTEREST AS IMPLICATION OF DECREE OF THE MINISTER OF HOME AFFAIRS AND REGIONAL AUTONOMY NUMBER 3 OF 2001

The study intends to describe: (1). Empowerment of village libraries as an implication of the implementation of the formation of village libraries. (2). The role of village libraries in increasing people's interest in reading (3). Constraints in increasing reading interest in rural communities. The research object is based on the village library empowerment content. The scope of research focuses on the realization of implementation management and the role of village libraries to increase people's interest in reading. This study uses a historical approach as a critical thinking analyst on social reality. Primary data as a primary source of information, data retrieval methods use a research library. The analysis uses descriptive qualitative analysis, relying on sources of analysis from various studies of literature containing theoretical, research and non-research studies. Observation is used to support theoretical constructs and content analysis to analyze the meaning of the concept of implementing the library formation in increasing reading interest in rural communities. The research results include; (1) Empowerment of Village Libraries is obliged to carry out as much as possible towards: (a). human Resource Management. (b). collection management. (c). service management. (d). marketing Management. (2). The role of village libraries in increasing people's interest in reading includes: (a). increase reading interest evenly in various regions. (b). become a place for learning activities. (c). support the improvement of literacy and reading skills (d). support increased information literacy. (3). Barriers to increasing reading interest in rural communities include: (a). access to information is still low. (b). the level of public education is still below standard. (c). generally low socioeconomic conditions. (d). library services are not evenly distributed. (e). appreciation and public response is still low.
Study of hierarchical learning and properties of convolution layer using sign language recognition model

Convolution Neural Network (CNN) as a technique improves research minds to overcome the challenges of handcrafted feature extraction and classification. CNN be a part of the representation learning methods in deep learning architecture to discover the representation needed for detection and classification automatically. So far this technique has been thought as black boxes, meaning that their inner working principles are mysterious and inscrutable. In order to understand the internal behavior of CNN, a model is developed on sign language recognition with 99.81%, 94.69%, 92.60% accuracy in train, test, and validation. While developing a model the inner principles of automatic feature extraction and the unique properties of convolution operations available in hierarchical CNN architecture are also learned. CNN is a multilayered network leading to feature learning and classification, it is necessary to understand how the features are learned from each layer and how it is transformed and fed into the next higher level layers without any human interventions.
Hyperparameter Tuning of the Shunt-murmur Discrimination Algorithm Using Bayesian optimization

Patients undergoing hemodialysis generally have shunts implanted in their bodies; a number of other problems, such as vascular stenosis, can be encountered. Patients undergoing hemodialysis can inspect the effective functioning of their shunts by listening to the shunt murmur. However, this manual inspection is difficult and requires experience. In this paper, we propose a method of exploring the hyperparameters of the shunt-murmur discrimination algorithm using Bayesian optimization. The resistance index(RI) obtained from the ultrasound system is used as a class label. The normalized crosscorrelation coefficients, Mel frequency cepstrum coefficients, and frequency power percentage were the features to be trained by a random forest (RF). Bayesian optimization was used to explore the hyperparameters of the RF, achieving a significant accuracy improvement.
Partial Rejection Control for Robust Variational Inference in Sequential Latent Variable Models

Effective variational inference crucially depends on a flexible variational family of distributions. Recent work has explored sequential Monte-Carlo (SMC) methods to construct variational distributions, which can, in principle, approximate the target posterior arbitrarily well, which is especially appealing for models with inherent sequential structure. However, SMC, which represents the posterior using a weighted set of particles, often suffers from particle weight degeneracy, leading to a large variance of the resulting estimators. To address this issue, we present a novel approach that leverages the idea of \emph{partial} rejection control (PRC) for developing a robust variational inference (VI) framework. In addition to developing a superior VI bound, we propose a novel marginal likelihood estimator constructed via a dice-enterprise: a generalization of the Bernoulli factory to construct unbiased estimators for SMC-PRC. The resulting variational lower bound can be optimized efficiently with respect to the variational parameters and generalizes several existing approaches in the VI literature into a single framework. We show theoretical properties of the lower bound and report experiments on various sequential models, such as the Gaussian state-space model and variational RNN, on which our approach outperforms existing methods.
How Did the Data Extraction Business Model Come to Dominate? Changes in the Web Use Ecosystem Before Mobiles Surpassed Personal Computers

It is widely believed that the spread of data extraction technologies on the Internet has led to the erosion of traditional professional content providers and the transformation of the online media ecosystem. To investigate this shift in media ecology, we conduct relational analyses of actual user behavior, departing from existing research that primarily focuses on business institutions and designs of technology. We assess the prevalence of the data extraction business model by grouping websites along two architectural traits that afford data extraction — user content generation and curation — and analyzing how some website architectures get privileged in the web use ecosystem. Since data extraction is relational, we advocate a network measure to capture shared usage in addition to individual popularity of websites. Our analyses of world's 850 most popular websites in 2009, 2011, and 2013 reveal that data extraction fostered a two-tier hierarchical web use ecosystem, marked by interdependence between professional content producers and data extractors. Our study thereby shows that the dynamics in play are more complicated than what is captured by explanations centered on either capabilities of platform giants or the decline of traditional journalism and media organizations.
The advent of Tight Frame-Based Time-frequency Representations on Speech Reconstruction Stability

The main advantage of tight frame-based time-frequency representations is to warrant a robust and stable signals reconstruction. In this paper we propose to study the speech reconstruction stability, in distortion sense, of three tight frame-based representations: the tight framelet transform, the higher density tight framelet transform and the tight framelet packet transform. The reconstruction stability is assessed using objective criteria. The results are compared with the critically wavelet packet transform. We perceive that the representations satisfying the frame theory conditions are more stable and resistant to distortions.
Phraseology: Where Lexicon and Syntax Conjoin

This article discusses the definitions and scope of phraseology as a domain which is primarily lexical but which very much connects with grammatical issues. We discuss the gradual widening of the scope of phraseology, resulting from the emergence of data-driven, bottom-up analyses of large language corpora. We also note that statistical models of learning and usage-based models of language use converge to provide theoretical support for the claim that language use is to a considerable extent formulaic. An overview of empirical research which supports this claim is provided. We also examine the various schools of linguistic thought which blur the distinction between lexis and grammar, such as the Hallidayan concept of lexicogrammar, valency grammar, pattern grammar, and construction grammar. Our conclusion is that while these and other developments in the field of syntax acknowledge the role of lexis and even attach great importance to it, academic publications which cover typically lexical issues, especially with respect to second language acquisition and use, rarely acknowledge the crucial interconnection between lexis and grammar.
Time Based Imaging

The science, the art, and the interest in time-lapse photography comes from the need to speed-up time processes using imaging. The longer the time between exposures, the more sped up the event will be when the final movie is displayed. When longer times are chosen between exposures, it becomes more difficult to record the event. Events that last for long times create challenges in managing all of the variables that might be present during an event’s life cycle. Since the shutter is open more than it is closed, there is a high probability of capturing some unique images. This technique can turn stormy seas into a magical mist and cars moving in traffic at night into tracers of only red lights. This is also useful for capturing meteor displays, the Aurora Borealis movements, and lightning storms. Time-lapse videos create the compression of time which allows high-speed events to be viewed using an expansion of time.