Reviewerly/exHarmony huggingface dataset:
Cancer Diseases Diagnosis Using Deep Transfer Learning Architectures

Cancer is a lethal disease among the diseases in the world. It is clinically known as ‘Malignant Neoplasm’ which is a vast group of diseases that encompasses unmonitored cell expansion. It can begin anywhere in the body such as the breast, skin, liver, lungs, brain, and so on. As reported by the National Institutes of Health (NIH), the projected growth of new cancer cases is forecast at 29.5 million and cancer-related deaths at 16.4 million through 2040. There are many medical procedures to identify the cancer cell, such as mammography, MRI, CT scan, which are common methods for cancer diagnosis. The methods used above have been found to be ineffective and necessitate the development of new and smarter cancer diagnostic technologies. Persuaded by the phenomena of medical image classification using deep learning, our recommended initiative targets to analyze the performance of different deep transfer learning models for cancer cell diagnosis. In this paper, we have used VGG16, Inception V3 and MobileNet V2 deep architectures to diagnosis the breast cancer (KAU-BCMD dataset), lung cancer (IQ-OTH/NCCD dataset) and skin cancerHam10000). Experimental results demonstrate that VGG16 architecture shows comparatively higher accuracy by exhibiting 98.5% of accuracy for breast cancer, 99.90% for lung cancer and 93% for skin cancer dataset.
Improving The Ability of Students in Listening Comprehension by Using Authentic Materials

Listening is one of the receptive skills in English. Therefore, the problem is concerned with the lack of students’ listening skills. Thus, this research is aimed to prove that creative methods can help students to build their ability in listening. This research is conducted to the second semester of English Language Education Study Program Faculty of Teacher Training and Education Sciences, Pakuan University. In choosing the sample, the technique of total sampling is used. the quasi-experimental research design is applied in this research. The design of the research was pre-test and post-test that were given to the experimental and control group. The result of the data analysis showed a significant difference. By applying 0,05 level of significance and the degree of freedom (df) is 44, it showed that the t-counted 1,11 was lower than t-table 2,02. In conclusion, the use of authentic materials is not affected to improve the students’ listening comprehension.
Bayesian spatiotemporal modeling for inverse problems

Inverse problems with spatiotemporal observations are ubiquitous in scientific studies and engineering applications. In these spatiotemporal inverse problems, observed multivariate time series are used to infer parameters of physical or biological interests. Traditional solutions for these problems often ignore the spatial or temporal correlations in the data (static model), or simply model the data summarized over time (time-averaged model). In either case, the data information that contains the spatiotemporal interactions is not fully utilized for parameter learning, which leads to insufficient modeling in these problems. In this paper, we apply Bayesian models based on spatiotemporal Gaussian processess (STGP) to inverse problems with spatiotemporal data and show that the spatial and temporal information provides more effective parameter estimation and uncertainty quantification (UQ). We demonstrate the merit of Bayesian spatiotemporal modeling for inverse problems compared with traditional static and time-averaged approaches using a time-dependent advection–diffusion partial different equation (PDE) and three chaotic ordinary differential equations (ODE). We also provide theoretic justification for the superiority of spatiotemporal modeling to fit the trajectories even if it appears cumbersome (e.g. for chaotic dynamics).
Patient Data Work with Consumer Self-tracking: Exploring Affective and Temporal Dimensions in Chronic Self-care

Emerging studies are reporting on the implications of self-tracked data in patients’ everyday life and how it influences self-care activities in chronic care. The increased uptake of consumer wearable activity trackers in healthcare contexts and the wider application of advanced analytics is changing the temporal scope from ‘past-centric’ to ‘future-centric’ personal informatics. At the same time, a stream of research is making clear that experiences of emotion are constitutive of patient data work suggesting that the micro practices of engaging with personal data has an important affective dimension. We conducted an exploratory interview study with five chronic heart patients with an implanted cardiac device to conceptualize the data work, which is involved in making sense of self-tracked data from a consumer wearable activity tracker (Fitbit Alta HR). In this paper, we contribute to understanding patient data work as seven forms of micro practices: Verifying, Questioning, Motivating, Reacting, Accepting, Distancing, and Sharing. We discuss how these practices relate to temporal and affective dimensions of engaging with self-tracked data in chronic care and point to future research.
Identification of mixtures of dynamic discrete choices

This paper provides new identification results for finite mixtures of Markov processes. Our arguments yield identification from knowledge of the cross-sectional distribution of three (or more) effective time-series observations under simple conditions. We explain how our approach and results are different from those in previous work by Kasahara and Shimotsu (2009) and Hu and Shum (2012). Most notably, outside information, such as monotonicity restrictions that link conditional distributions to latent types, is not needed.
A surrogate evolutionary neural architecture search algorithm for graph neural networks

Due to the unique construction module and design of graph neural networks (GNNs), neural architecture search (NAS) methods specifically for GNNs have become a promising research hotspot in recent years. Among the existing methods, one class of methods microscopically searches for the constituent components of network layers. However, most of them ignore the topology connections between network layers or the feature fusion strategies. Another class of methods, called differentiable architecture search methods, has the advantage of searching topology connections and feature fusion strategies. However, constrained by the requirement of predefining all candidate operations, these methods can only sample a limited number of network layers. In this paper, we propose a surrogate evolutionary graph neural architecture search (GNAS) algorithm whose search space contains not only the microscopic network layer components but also topology connections and feature fusion strategies (called CTFGNAS). The GNN sampled in CTFGNAS is represented by a simple one-dimensional vector and does not fix the network depth. To address the problem that traditional crossover and mutation operators applied to GNAS may produce illegal solutions, we design a repair operation to guarantee the legitimacy of the solutions. The network depth is also increased with a large probability in the mutation operation to alleviate the oversmoothing problem. In addition, to cope with the challenge of computational resources due to the increased search space, we form a surrogate model with three classical regression models, where only a small number of solutions are truly evaluated for their fitness, and the remaining large number of solutions are predicted for their fitness by the surrogate model. Finally, experiments are executed on six widely used real-world datasets. The experimental results illustrate that CTFGNAS obtains more effective results than the state-of-the-art handcrafted GNNs and GNAS methods on all datasets. CTFGNAS is now available on the following website: https://github.com/chnyliu/CTFGNAS.
Secure Web Gateway on Website in Cloud

Developing interest for transfer work from home associations needs to permit numbers of specialists to get to confidential organizations on the business nearby web. This leads to greater expenses to authorize the homeworker admittance to the association's assets secretly and safely through assigned gadgets and administration. The primary purpose of additionally supports the association execution with various administrations for telecommuters. SASE utilizes Zero Trust Engineering as its spine, without confiding in any gadget or client, yet validate and approve at each solicitation. The main purpose is to apply designated spot capabilities such as Secure Web Passage and Cloud Access Security Representative to additionally uphold the security of the association's resources that may not be trusted in the cloud. Toward this paper's end, we will comprehend how those strategies referenced for doing the work on secure systems for work with associations’ system associations and security. We conduct a systematic literature review of security challenges of cloud computing. In addition to security issues, the benefits of cloud computing security were also studied. Whenever a website is hacked by an attacker, businesses and organizations lose their valuable data. Thus, we choose the best platform to organize and secure the data online in a valuable way. The main point of attack in the website is the use of weak passwords and sharing authentication to other users.
Distributed observers for LTV systems: A distributed constructibility gramian based approach

The paper addresses the problem of designing distributed observers for discrete linear time-varying (LTV) systems with distributed sensor nodes. It is shown, under the conditions of collective observability, strong connectivity of the sensor communication network, and invertibility of the state transition matrix, that the resulting observer yields exponential stability of the estimation errors with a pre-defined convergence rate and, in certain situations, requires the exchange of fewer data than other methods. It is shown that for linear time-invariant (LTI) systems this method yields fixed gains that can be computed in a few steps of a distributed algorithm with the underlying communication network. A design example is given where the asymptotic performance of the proposed observer is shown to be similar to that obtained using a distributed Kalman filtering approach.
Performance Analysis of Distributed Algorithms for Big Data Classification

In present data age, massive amounts of data are being produced at breakneck speed, and such data, usually obtained from multiple sources, can be in various formats and in most cases must be analyzed in near real time. Data may be interpreted, classified, or forecasted using a variety of machine learning methods. As far as efficiency and outcomes go, these algorithms are unique. It is necessary to do a side-by-by-side comparison of the various approaches in order to determine which one is best for a certain situation. Also, an enormous dataset contains a tremendous amount and spectrum of information. Traditional tools are incapable to processing it. Several alternate methods can be considered to process it, including constructing a distributed environment or contracting cloud-based isolation. As a result, improved approaches and instruments are necessary to make information credible. The practical usefulness and relative performance of a variety of classification algorithms are evaluated with respect to different types of large datasets. Different criteria are used to evaluate algorithms, including accuracy, precision, speedup, scale-up, and data scalability. The study indicates that there are no universally best classification tools; a better tool will depend on the size and characteristics of the dataset. This work presents a comparative study of multiple distributed classification algorithms analyzed using the Apache Spark Data Analytics platform for various categories of datasets.
Processing of Chinese language and text information system under the background of speech recognition

With the popularization of computers, artificial intelligence technology has become more and more mature, among which speech recognition technology in artificial intelligence is favored by people. In the past few years, the acoustic model combined with the Gaussian mixture model and the hidden Markov model has always been in the leading position. In the field of speech recognition technology, because the development of speech data has gradually expanded, and the complexity of the data has also increased. The larger the data, the traditional data network model is slowly showing inadequacy. However, the deep neural network model is easy to deal with large and complex data modeling. This article combines the advantages of basic learning theory and speech recognition technology, and launches in-depth research on embedding learning theoretical knowledge into the field of speech recognition. Nowadays, large-scale text information databases relying on computers are becoming more and more important in linguistic research, and a large-scale corpus that fully reflects language facts and contains rich language information has been constructed. The establishment of the text information database system is long, from word segmentation, part-of-speech tagging to syntactic tagging to semantic tagging. Therefore, the characteristic of information system processing is that the systematic description depends on the application environment of understanding vocabulary and reasoning. According to different scenarios, the realization methods of semantic description roles are also different, and the description of semantic roles in correct scenarios is clearer, more detailed and systematic. Therefore, this article is of great significance for solving the semantic problem of using Chinese frame network for Chinese information processing in the context of speech recognition.
Millimeter Wave Radar Sensing Technology for Filipino Sign Language Recognition

Filipino Sign Language (FSL) is the primary language used by the Deaf and Hard-of-Hearing (DHH) community in the Philippines. The lack of support for FSL from the government has led to a huge communication gap between the DHH and the hearing society. A substantial amount of research has been done to develop sign language recognition systems based on computer vision or wearable technology. However, most such systems are limited to controlled settings, while wearable systems also raise issues such as inconvenience to users. Millimeter wave (mmWave) technology has recently seen potential in gesture recognition applications as it allows the system to be non-contact and resistant to environmental factors while ensuring high resolution for recognizing small movements. An mmWave-based FSL recognition system that can translate isolated signs into their equivalent gloss was developed. Data from a TI IWR1443 radar sensor was fed into a preprocessing algorithm and a deep learning model composed of multi-view 2D CNNs and LSTM. 4 models were trained based on a dataset of 24 FSL signs gathered with 3 native Deaf signers in 3 different environments. A total of 3240 samples were collected, resulting in a model that attained an overall peak accuracy of 94.9% and an average real-time recognition latency of about 2.01 s. The model's performance is comparable to both existing FSL and mmWave systems, showing immense potential for future work on FSL recognition using mmWave.
Deep learning-based application for multilevel sentiment analysis of Indonesian hotel reviews

In this study, we present a web-based application that retrieves hotel review documents in Indonesian languages from an online travel agent (OTA) and analyses their sentiments from the coarse-grained document to the fine-grained aspect level./Methodology/Approach: There are four main stages in this study: development of sentiment analysis model at the document level based on a convolutional neural network (CNN), development of sentiment analysis model at the aspect level based on an improved long short-term memory (LSTM), model deployment for multilevel sentiment analysis in a web-based application, and its performance evaluation. The developed application uses several sentiment visualizations types at coarse-grained and fine-grained levels, such as pie charts, line charts, and bar charts.The application's functionality was demonstrated in practice based on three datasets from three OTA websites, which were analyzed and evaluated based on several matrices, namely, the precision, recall, and F1-score. The results revealed that the performance for the F1-score was 0.95 ± 0.03, 0.87 ± 0.02, and 0.92 ± 0.07 for document-level sentiment analysis, aspect-level sentiment analysis, and aspect-polarity detection, respectively.The developed application (Sentilytics 1.0) can analyze sentiment at document and aspect levels. The two levels of sentiment analysis are based on two models generated by fine-tuning CNN and LSTM models using specific architectures and domain data (Indonesian hotel reviews).
An enhanced noise-tolerant hashing for drone object detection

Drone, a.k.a. Unmanned aerial vehicle (UAV), has been pervasively applied in geological hazard monitoring, smart agriculture, and urban planning in the past decade. In this work, we fuse multiple attributes into a noise-tolerant hashing framework that can detect objects from drone pictures extremely fast. Our method can intrinsically and flexibly encode various topological structures from each target object, based on which multi-scale objects can be discovered in a view- and altitude-invariant way. Moreover, by leveraging lF and l1 norms collaboratively, the calculated hash codes are robust to low quality drone pictures and noisy semantic labels. More specifically, for each drone-borne picture, we extract visually/semantically salient object parts inside it. To characterize their topological structure, we construct a graphlet by linking the spatially adjacent object patches into a small graph. Subsequently, a binary matrix factorization (MF) is designed to hierarchically exploit the semantics of these graphlets, wherein three attributes: i) deep binary hash codes learning, ii) contaminated pictures/labels denoising, and iii) adaptive data graph updating are seamlessly incorporated. Such multi-attribute binary MF can be solved iteratively, and in turn each graphlet is transformed into the binary hash codes. Finally, the hash codes corresponding to graphlets within each drone photo are utilized for ranking-based object discovery. Comprehensive experiments on the DAC-SDC, MOHR, and our self-compiled data set have demonstrated the competitively speed and accuracy of our method. As a byproduct, we employ an elaborately-designed FPGA architecture to calculate our hash codes. On average, a 57 frames per second (fps) object detection speed is achieved on 4K drone videos (without temporal modeling).
Pemanfaatan Big Data dalam Pembelajaran Jarak Jauh (PJJ) selama Pandemi pada Daerah 3T (Terdepan, Terpencil dan Tertinggal)

The COVID-19 pandemic has prompted the adoption of distance education worldwide, including in 3T regions facing challenges of limited infrastructure and accessibility. The utilization of Big Data holds great potential in enhancing the quality and accessibility of education in these 3T regions. This study aims to analyze the utilization of Big Data in distance education during the pandemic in 3T regions, identify its benefits in personalized learning, monitoring the learning process, and predicting student needs, and provide recommendations for further research. The research approach employed data analysis from a survey involving 500 schools in 3T regions. Data was collected through questionnaires that encompassed the aspects of Big Data usage in distance education and its impact on educational quality in 3T regions. Data analysis reveals that the utilization of Big Data in distance education in 3T regions has significantly contributed to student data collection and analysis, monitoring the learning process, and predicting student needs. These findings are consistent with previous research that highlights the benefits of Big Data in personalized learning and educational performance management. The utilization of Big Data in distance education in 3T regions during the pandemic holds immense potential in improving the quality and accessibility of education. Adequate training and education should be provided to teachers and school staff regarding the utilization of Big Data.
Enhancing fiber security using a simple state of polarization analyzer and machine learning

The paper focuses on the security of fiber-optic cable infrastructures by detecting vibrations using an optical state of polarization analyzer. The developed system can detect various security breaches. The system only detects abnormal events without any event classification. The proposed system relies on an analyzer evaluating optical polarization differences caused by mechanical or acoustic vibrations analyzed by a machine-learning model for real-time anomaly detection. The main goal of experiments is to find the best combination of the normalization method and anomaly detector. The proposed system achieves an F1-score over 95.65%, which proves the solution's suitability for protecting fiber-optic infrastructures.
A test paper generation algorithm based on diseased enhanced genetic algorithm

With the continuous progress of society, tests, and exams appear more and more frequently in people's lives. Faced with the ever-increasing demand for test papers, efficient test paper generation algorithms have become more important. In this paper, we improved and proposed a Diseased Enhanced Genetic Algorithm (DEGA) based on the Genetic Algorithm (GA), and applied it to the test paper generation algorithm. I the crossover operator, the crossover probability that will change in different situations of the population is adopted. According to the characteristics of the test paper generation algorithm, we use the method based on the hamming distance to calculate the distance between individuals in the population. Aiming at the shortcoming that the mutation operator is too random, we designed and used a disease operator that includes three modules: natural disease, infection, and mutation. It effectively guarantees the distance between individuals in the population and improves the shortcoming that GA is easy to fall into a locally optimal solution. Finally, using the College English Test Band 4 (CET-4) questions from 2014 to 2021 as the data set, comparative experiments were carried out on the test paper generation algorithm based on Random Sampling Algorithm (RSA), GA, Enhanced Genetic Algorithm (EGA) and DEGA. The results show that when using the test paper generation algorithm based on DEGA, the generation of test papers is faster, the number of iterations is less, and the algorithm results are significantly better than other algorithms.