Reviewerly/exHarmony huggingface dataset:
Human activity recognition and classification using of convolutional neural networks and recurrent neural networks

Recently, by using the deep learning models, it has become easier to recognize the human activity with more accuracy than before by categorizing the activities that people are doing daily. Nowadays, with the extensive use of modern smartphones that have sensors, it has become easier to capture the data in raw format that has the movement details in three dimensions (X-Y-Z). In this paper, we utilized the open source WIreless Sensor Data Mining (WISDM) dataset which has six activities that are walking, jogging, standing, sitting, upstairs and downstairs. Each type of those activities consists of values in terms of (X, Y and Z) axes. We employed two types of deep learning algorithms that are Convolutional Neural Network (CNN) and Recurrent Neural Network - Long Short-Term Memory (RNN-LSTM). Our objective is to make a comparison between accuracy and loss after implementing the two models. We discovered that, when using the Convolutional Neural Network (CNN), the accuracy was 81%. However, the accuracy was 91% when using Recurrent Neural Network - Long Short-Term Memory (RNN-LSTM) and applying it on the same database. As a result, the Recurrent Neural Network - Long Short-Term Memory (RNN-LSTM) model outperformed the Convolutional Neural Network (CNN) model.
Phonetic variability constrained bottleneck features for joint speaker recognition and physical task stress detection

Normalizing intrinsic variabilities (e.g., variability in speech production brought on by aging, physical or cognitive task stress, Lombard effect, etc.) in speech and speaker recognition models is essential for system robustness. This study focuses on analysis of speech under physical task stress with its application for speaker recognition and physical task stress detection. An innovative framework using deep neural networks (DNNs) for joint text-independent speaker recognition and physical task stress detection is proposed. Instead of processing consecutive feature frames like d-vectors (i.e., average of frame-level speaker specific features extracted with a speaker discriminative trained DNN), phonetic variability constrained feature vectors as inputs to train deep bottleneck neural networks is proposed. More specifically, a universal background model (UBM) with a small number of mixtures is employed to align the acoustic features. The innovative feature representation is then generated by selecting and concatenating frames according to the alignments from the UBM. The benefit of feature rearrangement is twofold. First, phonetic variability is largely constrained in the front-end feature vector. Second, by sampling a determined number of representations for each speaker/utterance, the issue of data imbalance and over-fitting is alleviated. Experiments for speaker recognition and physical stress detection are conducted on the UTScope-Physical Task Stress Corpus. Improved performance in terms of accuracy (for identification/detection task) and Equal Error Rate (for verification task) over a strong i-vector probabilistic linear discriminant analysis system confirms the effectiveness of this proposed method.
The Design of GIFTED Motivation Module Using the ADDIE Model Approaches Among the Gifted and Talented Students

This study aims explore the process of development of GIFTED motivational module using the Design and Development Research (DDR) approach using the ADDIE Model.The development of the GIFTED motivational module for social issues involves the issues of perfectionism, social anxiety and overexcitabilities.Based on the analysis report on the need for perfectionism issues, social anxiety and overexcitabilities are three most common issues experienced by talented students.It is important to address the socio-emotional gap that occurs among these gifted and talented students by developing an intervention module to address this issue.The design and development of this module used the ADDIE approach because the ADDIE model is a systematic model based on a well-defined implementation strategy that can provide good input to the instruction module.The ADDIE model has also been widely used and is ideal as a basis for the development of the GIFTED module.The ADDIE model consists of five phases namely; a) analysis, b) design, c) development, d) implementation e) evaluation.The results of this five-phase study have established a strong motivational module with expert consent as well as a high validity and reliability.Furthermore, in each phase there is an enhancement process for modifying the module to achieve the planned objectives.The final evaluation results of this module prove that this module can assist the process of socio-emotion competence for gifted and talented students.
Survey and Analysis of Content-Based Image Retrieval Systems

Content-based image retrieval (CBIR) systems find a lively application in various fields like medical diagnosis, crime prevention, art collection, textile industry, etc. CBIRs continue to be an active domain of research due to increasing image databases and complex user queries being generated. The major challenge faced by such systems is the existence of a semantic gap between low-level features and human perception of the object’s images. Many CBIR systems exist in literature which aims to reduce this gap so as to produce precise search results for complex user queries. So this article aims to analyze and survey such systems based on the preprocessing, feature extraction, and classification techniques they employ for image retrieval while simultaneously focussing on the associated advantages and disadvantages for each of the discussed schemes.
Robust Watermarking Using Inverse Gradient Attention.

Watermarking is the procedure of encoding desired information into an image to resist potential noises while ensuring the embedded image has little perceptual perturbations from the original image. Recently, with the tremendous successes gained by deep neural networks in various fields, digital watermarking has attracted increasing number of attentions. The neglect of considering the pixel importance within the cover image of deep neural models will inevitably affect the model robustness for information hiding. Targeting at the problem, in this paper, we propose a novel deep watermarking scheme with Inverse Gradient Attention (IGA), combing the ideas of adversarial learning and attention mechanism to endow different importance to different pixels. With the proposed method, the model is able to spotlight pixels with more robustness for embedding data. Besides, from an orthogonal point of view, in order to increase the model embedding capacity, we propose a complementary message coding module. Empirically, extensive experiments show that the proposed model outperforms the state-of-the-art methods on two prevalent datasets under multiple settings.
Pig Breed Detection Using Faster R-CNN

In this paper, convolutional neural network object detection technology has been used to detect pig breeds with high precision from images captured through mobile cameras. The pretrained model is retrained on several images of 6 different pure breed pigs obtained from organized farms. The Faster R-CNN Inception-ResNet-v2 model has been used in transfer learning fashion for the above task. The training accuracy of this model is 100%, and the testing accuracy of this model is 91% with a confidence level of 94%. From the results achieved, it is noted that this model has produced better results compared to detection accuracy on other datasets like dog dataset, flower dataset, etc.
AutoDis: Automatic Discretization for Embedding Numerical Features in CTR Prediction

Learning sophisticated feature interactions is crucial for Click-Through Rate(CTR) prediction in recommender systems. Various deep CTR models follow anEmbedding & Feature Interaction paradigm. The majority focus on designingnetwork architectures in Feature Interaction module to better model featureinteractions while the Embedding module, serving as a bottleneck between dataand Feature Interaction module, has been overlooked. The common methods fornumerical feature embedding are Normalization and Discretization. The formershares a single embedding for intra-field features and the latter transformsthe features into categorical form through various discretization approaches.However, the first approach surfers from low capacity and the second one limitsperformance as well because the discretization rule cannot be optimized withthe ultimate goal of CTR model. To fill the gap of representing numericalfeatures, in this paper, we propose AutoDis, a framework that discretizesfeatures in numerical fields automatically and is optimized with CTR models inan end-to-end manner. Specifically, we introduce a set of meta-embeddings foreach numerical field to model the relationship among the intra-field featuresand propose an automatic differentiable discretization and aggregation approachto capture the correlations between the numerical features and meta-embeddings.Comprehensive experiments on two public and one industrial datasets areconducted to validate the effectiveness of AutoDis over the SOTA methods.
Systematic Optimization of Image Processing Pipelines Using GPUs

Real-time computer vision systems require fast and efficient image processing pipelines. Experiments have shown that GPUs are highly suited for image processing operations, since many tasks can be processed in parallel. However, calling GPU-accelerated functions requires uploading the input parameters to the GPU’s memory, calling the function itself, and downloading the result afterwards. In addition, since not all functions benefit from an increase in parallelism, many pipelines cannot be implemented exclusively using GPU functions. As a result, the optimization of pipelines requires a careful analysis of the achievable function speedup and the cost of copying data. In this paper, we first define a mathematical model to estimate the performance of an image processing pipeline. Thereafter, we present a number of micro-benchmarks gathered using OpenCV which we use to validate the model and which quantify the cost and benefits for different classes of functions. Our experiments show that comparing the function speedup without considering the time for copying can overestimate the achievable performance gain of GPU acceleration by a factor of two. Finally, we present a tool that analyzes the possible combinations of CPU and GPU function implementations for a given pipeline and computes the most efficient composition. By using the tool on their target hardware, developers can easily apply our model to optimize their application performance systematically.
Character-to-Word Attention for Word Segmentation

Although limited effort has been devoted to exploring neural models in Japanese word segmentation, much effort has been actively applied to Chinese word segmentation because of the ability to minimize effort in feature engineering. In this work, we propose a character-based neural model that makes joint use of word information useful for disambiguating word boundaries. For each character in a sentence, our model uses an attention mechanism to estimate the importance of multiple candidate words that contain the character. Experimental results show that learning attention to proper words leads to accurate segmentations and that our model achieves better performance than existing statistical and neural models on both in-domain and cross-domain Japanese word segmentation datasets.
AI for Covid-19: Conduits for Public Health Surveillance

The spread of SARS-Covid-19 virus has impacted the world as it continues to raise questions on the long-term impacts. With the absence of historic/big data sets, digital public health surveillance measures are informed via modelling using real-time data, which is collected and validated by public health agencies; and also aggregated/merged with self/open reported data by the public, via mobile apps and social media channels. This chapter informs on such conduits (using two case studies: Australia and Canada) that enabled AI-based solutions for informing public health strategies. Blue tooth technology used in contact tracing apps seems to allay privacy concerns to an extent, in both countries. Real-time streamed data collection to train predictive models and combining AI methods with active learning seems to be the way forward.
AI Poem Case Analysis

More and more writing robots are fully upgraded and launched, entering the intelligent stage of the entire process of selecting, writing, adapting, editing, and publishing, and human-machine collaboration has reached a new level. The IQ of writing robots is constantly improving in self-evolution. As an AI tool that assists in content creation, it subverts the traditional writing mode, industrializes and intelligentizes the article production mode, and helps content producers increase their creative efficiency tenfold. This article uses ancient Chinese poetry as an example to explore the mystery of intelligent writing.
Development of the reading culture in pre-service teachers amid digitalization of education

The article deals with the problem of developing a pre-service teacher’s reading culture as a way to build professional competence in the context of digital education in the Republic of Kazakhstan. The results of the questionnaire demonstrated that more than a half of the study pool of pre-service teachers made a conscious choice of a teaching profession, and the same number indicated difficulties in organizing their independent work with different texts. The results have also shown that around 90% of pre-service teachers do not consider books as a professional tool, and indicated major factors impeding reading: the Internet; difficulties in linear and nonlinear reading; lack of attention; financial struggles. The obtained results of the pedagogical experiment, carried out at the West Kazakhstan State University named after M. Utemisov suggest that using pedagogical experiment, with its collaborative workshops in conjunction with the university library, problem-based tasks, and educational role plays, afford beneficial ways of developing the reading culture among pre-service teachers. The materials of the article can be used in the context of higher education for delivering courses on digital pedagogy, as well as in organizing the teaching practice of students at school, etc.
Impact of Agile Methodology Use on Project Success in Organizations - A Systematic Literature Review

Agile methodology has become the most used software development methodology in different software communities. Besides the extensive benefits that agile methods offer, it also possesses several critical issues. Most studies have reported that when comparing structured methodologies with agile methodologies, agile does improve software quality, other studies have contradicted and questioned the claimed benefits, while others argued that there is not enough empirical evidence. This study aims at investigating the impact that agile method use has on project success in organizations. A systematic literature review to assess the latest evidence of the agile method use impact on project success was conducted. The selected studies were published between 2008 and 2019. Our keyword search found 1507 papers. Thirty publications were selected as our primary studies. The results show that when agile is adopted correctly, the organization can reap its benefits. It positively impacts the success of the project and that makes the customers happy.
A Comparison between Generic Algorithm and Particle Swarm Optimization

We compare Generic Algorithm and Particle Swarm Optimization algorithm by several benchmark functions. The result indicates that Particle Swarm Optimization is better than Generic Algorithm in optimum value and searching speed.
The role of artificial intelligence in breast cancer screening: how can it improve detection?

Breast cancer is the most common cancer in the WHO Europe region with an incidence of 522,513 in 2018 [1]. It accounted for 26.4% of all new cancer cases in women in 2018. In 2018, 137,707 European...
Phase space theory for open quantum systems with local and collective dissipative processes

In this article we investigate driven dissipative quantum dynamics of an ensemble of two-level systems given by a Markovian master equation with collective and non-collective dissipators. Exploiting the permutation symmetry in our model, we employ a phase space approach for the solution of this equation in terms of a diagonal representation with respect to certain generalized spin coherent states. Remarkably, this allows to interpolate between mean-field theory and finite system size in a formalism independent of Hilbert-space dimension. Moreover, in certain parameter regimes, the evolution equation for the corresponding quasiprobability distribution resembles a Fokker-Planck equation, which can be efficiently solved by stochastic calculus. Then, the dynamics can be seen as classical in the sense that no entanglement between the two-level systems is generated. Our results expose, utilize and promote techniques pioneered in the context of laser theory, which we now apply to problems of current theoretical and experimental interest.
Lyapunov, Perron and upper-limit stability properties of autonomous differential systems

For a singular point of an autonomous differential system, the natural concepts of its Perron and upper-limit stability are defined, reminiscent of Lyapunov stability. Numerous varieties of them are introduced: from asymptotic and global stability to complete and total instability. Their logical connections with each other are investigated: cases of their coincidence are revealed and examples of their possible differences are given. The invariance of most of these properties with respect to the narrowing of the phase region of the system is established.
Characterizing the Evasion Attackability of Multi-label Classifiers

Evasion attack in multi-label learning systems is an interesting, widely witnessed, yet rarely explored research topic. Characterizing the crucial factors determining the attackability of the multi-label adversarial threat is the key to interpret the origin of the adversarial vulnerability and to understand how to mitigate it. Our study is inspired by the theory of adversarial risk bound. We associate the attackability of a targeted multi-label classifier with the regularity of the classifier and the training data distribution. Beyond the theoretical attackability analysis, we further propose an efficient empirical attackability estimator via greedy label space exploration. It provides provably computational efficiency and approximation accuracy. Substantial experimental results on real-world datasets validate the unveiled attackability factors and the effectiveness of the proposed empirical attackability indicator.
TELEMONITORAMENTO: ANÁLISE DA PERCEPÇÃO DOS ACADÊMICOS DE ENFERMAGEM FRENTE À PANDEMIA DA COVID- 19

Experience report of scholarship scholars during the pandemic period caused by COVID-19 in the State of Rio de Janeiro. The City of Rio, through the Municipal Secretary of State Administration and in accordance with Law No. 11,788 / 2008, publishes every year end of the public notice that regulates the selection process for granting non-mandatory internship grants to university students' different areas of health. In 2020, scholarship scholars faced an atypical situation, caused by COVID-19, in which there was a need for a new formulation for the allocation of academics, which were approved according to the notice to start activities in 2020. The objective was to report the experience lived by scholarship scholars, through telemonitoring during the period of social distance caused by COVID-19 in 2020. In Rio de Janeiro, the call center made by academics from the Shared Services Secretariat of the Municipal Secretariat of the Civil House as effective support in several scenarios, such as contact by phone with the patient in treatment of various pathologies and health problems.
Making the Best Use of Review Summary for Sentiment Analysis

Sentiment analysis provides a useful overview of customer review contents.Many review websites allow a user to enter a summary in addition to a full review.Intuitively, summary information may give additional benefit for review sentiment analysis.In this paper, we conduct a study to exploit methods for better use of summary information.We start by finding out that the sentimental signal distribution of a review and that of its corresponding summary are in fact complementary to each other.We thus explore various architectures to better guide the interactions between the two and propose a hierarchically-refined review-centric attention model.Empirical results show that our review-centric model can make better use of user-written summaries for review sentiment analysis, and is also more effective compared to existing methods when the user summary is replaced with summary generated by an automatic summarization system.
Personalisation of University E-Learning Environment as Linguodidactic Issue

The purpose of the study is to make a case for personalisation of university e-learning environment taking into consideration individual abilities and needs of students with prospective Bachelor’s degrees in Language Studies. The authors put emphasis on the fact that digital technologies are an effective means of improving foreign language teaching quality owing to their relevance, flexibility, informational saturation, which strengthen motivation to learn in multi-level differentiation setting. The research is novel in that it provides a rationale for principles of personalised teaching as a gradual promotion of a learner to the position of a self-learner. As a result, the authors emphasise specific nature of modern digital educational space, which makes it possible to project different variants of professional interaction.
New Occurrence of Gold Mineralization at the Eastern Part of Wadi El Gemal, South Eastern Desert, Egypt

The basement rock units of the study area are represented by metasedimentsmetavolcanics association, diorite rocks, granodiorites and syenogranites.The whole sequence is cut by basic dykes, pegmatites and different quartz (jasper) veins.Metasediments-metavolcanics association is represented by hornblende schist and metadacite respectively.There is visible sulfides mineralization in hornblende schist, metadacite and diorite rocks at their contacts at the wadi level.So, polished sections were prepared to study opaque minerals under the ore microscope, which reveals the presence of gold, pyrite and goethite.The radioactivity of this area is weak, where the average eU contents in metasediments-metavolcanics association, diorites, granodiorites, syenogranites, pegmatites and jasper veins are 2.8 ppm, 1.9 ppm, 3.26 ppm, 4.91 ppm, 4.32 ppm and 6.7 ppm respectively.The intrusion of granodiorite rocks may act as a source for hydrothermal solutions ascending along fractures of all older rocks.During this stage, a chemical reaction of hydrothermal fluids with metasediments-metavolcanics association and diorite rocks lead to forming sericite and enrichment of silica content.The enrichment of silica content led to the formation of quartz-diorite.Also, iron derived from the host rocks and sulfur from the hydrothermal fluids could have formed pyrite while the gold was precipitated at the contact between metasediments-metavolcanics association and diorite rocks.