Reviewerly/exHarmony huggingface dataset:
Tuning of Elasticsearch Configuration: Parameter Optimization Through Simultaneous Perturbation Stochastic Approximation

Elasticsearch is currently the most popular search engine for full-text database management systems. By default, its configuration does not change while it receives data. However, when Elasticsearch stores a large amount of data over time, the default configuration becomes an obstacle to improving performance. In addition, the servers that host Elasticsearch may have limited resources, such as internal memory and CPU. A general solution to these problems is to dynamically tune the configuration parameters of Elasticsearch in order to improve its performance. The sheer number of parameters involved in this configuration makes it a complex task. In this work, we apply the Simultaneous Perturbation Stochastic Approximation method for optimizing Elasticsearch with multiple unknown parameters. Using this algorithm, our implementation optimizes the Elasticsearch configuration parameters by observing the performance and automatically changing the configuration to improve performance. The proposed solution makes it possible to change the configuration parameters of Elasticsearch automatically without having to restart the currently running instance of Elasticsearch. The results show a higher than 40% improvement in the combined data insertion capacity and the system's response time.
Alpha Parameter Modeling of Nakayasu Synthetic Unit Hydrograph Based on the Watershed Shape Factor

The limitation of hydrograph data availability becomes an obstacle for the design of water resources. The obstacle makes the Synthetic Unit Hydrograph give the big enough benefit for the design of water resources. Ideally, every watershed has its unit hydrograph. The Nakayasu Synthetic Unit Hydrograph is one of the synthetic unit hydrographs frequently used in the analysis of design flood discharge in Indonesia. However, the Nakayasu Synthetic Unit Hydrograph concept has been widely used and evaluated in some areas in Indonesia, and there is bias in determining the watershed characteristic coefficient. This research intends to investigate the characteristic of the observed hydrograph in every watershed and the whole watershed in the South Sulawesi Province-Indonesia. The research aims to build the alpha formula using alpha modeling with the affected parameters of watershed area, river length, river bed slope, shape factor, and the time concentration. Based on the ArcGIS version 10.7, most of the watershed’s shapes are elongated. The methodology consists of analyzing observed unit hydrograph by using the Collins method and Nakayasu Synthetic Unit Hydrograph. Then the model is built by using the statistical analysis of SPSS version 23 software for obtaining the affected level of each parameter that is determined. However, the modeling analysis uses the non-linear regression after there is carried out the descriptive test. The alpha formula for the elongate watershed in the Jeneberang River is proposed.
Evaluating model serving strategies over streaming data

We present the first performance evaluation study of model serving integration tools in stream processing frameworks. Using Apache Flink as a representative stream processing system, we evaluate alternative Deep Learning serving pipelines for image classification. Our performance evaluation considers both the case of embedded use of Machine Learning libraries within stream tasks and that of external serving via Remote Procedure Calls. The results indicate superior throughput and scalability for pipelines that make use of embedded libraries to serve pre-trained models. Whereas, latency can vary across strategies, with external serving even achieving lower latency when network conditions are optimal due to better specialized use of underlying hardware. We discuss our findings and provide further motivating arguments towards research in the area of ML-native data streaming engines in the future.
SmarTy: An Intelligent Voice Response System Model for Indian Local Languages

There is a lacunae in interactive voice response systems/chatbots that deal with Indian Languages in terms of scope and usability. This paper proposes a model by name SmarTy which is an intelligent voice response model for Indian Local Languages. The model considers speech/voice query given in a local language comprising a dialect and responds to the user with suitable information in the corresponding language. The model emphasizes on understanding the dialects of different languages through ML algorithms and generating the suitable response with high accuracy using ML algorithms. The strength of the model is, it considers all the available datasets in local languages for training the model and the voice recorded during the real-time implementation of the model would be added to the existing collection of datasets. This generates responses with high accuracy through iterative training of the model with updated collection of datasets.
An Exploratory Study of Cognitive Sciences Applied to Cybersecurity

Cognitive security is the interception between cognitive science and artificial intelligence techniques used to protect institutions against cyberattacks. However, this field has not been addressed deeply in research. This study aims to define a Cognitive Cybersecurity Model by exploring fundamental concepts for applying cognitive sciences in cybersecurity. For achieving this, we developed exploratory research based on two steps: (1) a text mining process to identify main interest areas of research in the cybersecurity field and (2) a valuable review of the papers chosen in a systematic literature review that was carried out using PRISMA methodology. The model we propose tries to fill the gap in automatizing cognitive science without taking into account the users’ learning processes. Its definition is supported by the main findings of the literature review, as it leads to more in-depth future studies in this area.
An Artificial Neural Network Technique of Modern Cryptography

An enormous volume of data transmitted through the internet in every second nowadays, so data protection is still a big challenge as communication channels are mostly public. Several approaches are there for protection of data when transmitted through the network channel, cryptography is one of them. Cryptography is a technique to hide original and confidential information from intruder. Considering information security principles i.e. confidentiality, integrity, and availability (CIA-triad) an efficient, portable, dynamic, and simple, artificial neural network (ANN) based cryptography algorithm have been presented in this paper. A multi-substitution tree parity machine (TPM) approach is used here to generate dynamic single digit secret key. The secret key created by the tree parity machine will be different in each phase of execution. This single digit dynamic secret key is used in the proposed encryption and decryption techniques. The complexity and execution time taken by the proposed algorithm is improved comparing to the existing algorithms. The proposed algorithm provides higher reliability and robustness in terms of security because of it dynamic nature.
Vision and natural language for metadata extraction from scientific PDF documents

The challenge of automatically extracting metadata from scientific PDF documents varies depending on the diversity of layouts within the PDF collection. In some disciplines such as German social sciences, the authors are not required to generate their papers according to a specific template and they often create their own templates which yield a high appearance diversity across publications. Overcoming this diversity using only Natural Language Processing (NLP) approaches is not always effective which is reflected in the metadata unavailability of a large portion of German social science publications. Therefore, we propose in this paper a multimodal neural network model that employs NLP together with Computer Vision (CV) for metadata extraction from scientific PDF documents. The aim is to benefit from both modalities to increase the overall accuracy of metadata extraction. The extensive experiments of the proposed model on around 8800 documents proved its effectiveness over unimodal models, with an overall F1 score of 92.3%.
THE EFFECTIVENESS OF IMPROVING STUDENT MATHEMATICS LITERACY THROUGH THE USE OF THE FACEBOOK APPLICATION

This study is informed by the poor level of mathematics literacy among Indonesian students. One strategy to improve mathematics literacy is to leverage innovations in Information and Communication Technology (ICT), which are now undergoing rapid development, as seen by the availability of different types of social media applications. The aim of this study was to investigate the effectiveness of the Facebook application in improving mathematics literacy. This research was conducted in the odd semester of the 2021/2022 academic year at the State Senior High School 1 of DolokSigompulon (SMAN 1 DolokSigompulon), North Padang Lawas Regency. All the students in class XI were included in this study. Students in class XI-Science 1 served as the sample in this study. The descriptive quantitative methodology was adopted in this investigation. Questionnaires and tests are used to collect data. The descriptive analysis technique was included in this study. The results of this research indicate that using the Facebook application to improve students' mathematical literacy is quite effective. As evidenced by the questionnaire instrument's results, using the Facebook application for mathematical literacy is "very good." Similarly, the test instrument for mathematical literacy skills, which utilizes the Facebook application, yields "good" results.
Construction and Research of Constructive English Teaching Model Applying Multimodal Neural Network Algorithm

This article draws on previous research on constructive English teaching models and uses multimodal neural network algorithm theory and constructive English teaching as the theoretical basis, experimental research method, questionnaire survey method, and evaluation method. In this article, we propose a multimodal neural network consisting of a multiscale FCN module and an LSTM module. The network focuses on both the multiscale geometric spatial features and the numerical time-dependent features of the time series curves, and with the comprehensive knowledge of their characteristics, it can better distinguish the classes to which the series belong. A large-scale perceptual field is achieved by null convolution in the model to ensure that the training pressure does not increase significantly. A series of experiments on the UCR dataset verifies the effectiveness and superiority of the model. Simulation experiments were conducted to build the proposed constructive English teaching model based on a multimodal neural network algorithm, and a test environment was built for use case testing. The experimental results showed that the algorithm can be better applied to constructive English teaching and has better adaptability and accuracy in various scenarios. At the end of the experiment, a posttest of grammar level was conducted in two classes to test whether the constructive English teaching model based on the multimodal neural network model could help students improve their English grammar skills. The results of the data analysis showed that the mean score of the experimental class was significantly higher than that of the control class, and the experimental class showed a more significant improvement, indicating that this new constructive English teaching model was beneficial to improving students' English grammar skills. The interaction strategy proposed under the constructive English teaching model can effectively improve the interaction between teachers and students. This positive feedback effect can provide front-line teachers with corresponding teaching references.
Teachers' collaborative reflective discussions on technology-mediated teaching: Envisioned and enacted transformative agency

This study explores teachers' reflections on the competences that come into play in technology-mediated teaching activities. Underpinned by educational design research, the collaboration between researchers and teachers followed a methodological design involving three iterative phases: (1) workshops in which teachers and researchers collaborated to develop instructional scenarios involving digital technologies, (2) lessons enacting instructional scenarios, and (3) reflective discussions based on video sequences from the instructional scenarios. The researchers selected video clips of instructional sequences involving so-called critical incidents where the teachers encountered some kind of challenge in the technology-mediated teaching activities. The unit of analysis comprised transitional episodes identified in the reflective discussions, where temporal shifts took place as the teachers elaborated on challenges and oriented towards future actions. Theoretically, the study is based on sociocultural perspectives, acknowledging social interaction as collective thinking. To analytically scrutinise temporal shifts in the interaction, interaction analysis was employed. The findings show that while the elicited video clips supported retrospective reflections, the collaborative context with colleagues and researchers interacting supported prospective reflections. These findings are discussed in terms of how the temporal shifts in the reflections can analytically be understood as teachers' envisioned or enacted transformative agency.
Multi-Task Attention Network for Digital Context Classification from Internet Traffic

Understanding multi-aspect of user's digital context over Internet is a challenging due to the increasing network data encryption, privacy protection and complexity of the context concept. Based on the emerging attention mechanism capturing models’ focus points of input data, we proposed in this paper the attention neural network for digital context classification from Internet traffic: firstly, we introduced a digital context model and an extended version of multi-label dataset of Internet traffic to support our classification work. In the following, single task models and a multi-task model were proposed, and a series of experiments on five classification tasks of digital context has been conducted to evaluate the performance of recurrent models, attention models and the multi-task model. Experiments’ results have shown that different attention mechanisms are able to improve the outcome of recurrent classification models for Internet traffic, while multi-task attention network is able to further improve the overall accuracy of all tasks in the model by mutualizing data and exploiting implicit relations among tasks.
UNICON: A UNIform CONstraint Based Graph Layout Framework

We propose UNICON, a UNIform CONstraint based graph layout framework that supports both soft and hard constraints. We extend the stress model to accommodate soft constraints by incorporating them in the objective functions, optimized by stochastic gradient descent. For hard constraints, such as inequalities or equalities in the layout space, we utilize a gradient projection method to satisfy them. A visualization prototype system is implemented based on this framework for the user to interactively add or remove constraints to generate the desired layouts. We demonstrate the efficiency, quality, and flexibility of the framework and the system on a number of datasets with a wide range of user-defined constraints.
Binary Logistic Regression Model of Stroke Patients: A Case Study of Stroke Centre Hospital in Makassar

This paper aimed to determine factors that affect significantly types of stroke for stroke patients in Dadi Stroke Center Hospital. The binary logistic regression model was used to analyze the association between the types of stroke and some covariates namely age, sex, total cholesterol, blood sugar level, and history of diseases (hypertension/stroke/diabetes mellitus). Maximum Likelihood Estimation was used to estimate parameters. Combinations of covariates were compared using goodness-of-fit measures. Comparisons were made in the context of a case study, namely stroke patients (2017-2020). The results showed that a binary logistic model combining the history of diseases and blood sugar level provided the most suitable model as it has the smallest AIC and covariates included are statistically significant. The coefficient estimation of the history of diseases variable is -0.92402 with an odds ratio value exp(-0.92402)=0.4. This means that stroke patients who have a history of diseases experience a reduction of 60% in the odds of having a hemorrhagic stroke compared to stroke patients that do not have a history of diseases. In other words, stroke patients who have a history of diseases tend to have a non-hemorrhagic stroke. Furthermore, the coefficient estimation of blood sugar level is 0.74395 with an odds ratio value exp(0.74395)=2. It means that stroke patients who do not have normal blood sugar levels tend to have a hemorrhagic stroke 2 times greater than stroke patients with normal blood sugar levels. A history of diseases and blood sugar level were factors that significantly affect the types of stroke.
Query-specific subtopic clustering

We propose a Query-Specific Siamese Similarity Metric (QS3M) for query-specific clustering of text documents. Our approach uses fine-tuned BERT embeddings to train a non-linear projection into a query-specific similarity space. We build on the idea of Siamese networks but include a third component, a representation of the query. QS3M is able to model the fine-grained similarity between text passages about the same broad topic and also generalizes to new unseen queries during evaluation. The empirical evaluation for clustering employs two TREC datasets and a set of academic abstracts from arXiv. When used to obtain query-relevant clusters, QS3M achieves a 12% performance improvement on the TREC datasets over a strong BERT-based reference method and many baselines such as TF-IDF and topic models. A similar improvement is observed for the arXiv dataset suggesting the general applicability of QS3M to different domains. Qualitative evaluation is carried out to gain insight into the strengths and limitations of the model.
Detection of hatefulness in Memes using unimodal and multimodal techniques

Memes have become a common way to spread humor, information, and ideas. However, some objectionable content in memes might transmit hatefulness among people. Identification of hatefulness in memes is thus essential and even challenging as it simultaneously involves analyzing two modes of information. We propose two different methods to classify hateful memes through this research work. The first method focuses on using BERT to improve the efficiency of existing text based models. The second method introduces a novel Multimodal technique which detects objects using pre-trained models like InceptionV3, ResNet50, and Xception and combines the modalities of text and image. The models created are lightweight and perform on par with complex transformer models.
XNAS: A Regressive/Progressive NAS for Deep Learning

Deep learning has achieved great and broad breakthroughs in many real-world applications. In particular, the task of training the network parameters has been masterly handled by back-propagation learning. However, the pursuit on optimal network structures remains largely an art of trial and error. This prompts some urgency to explore an architecture engineering process, collectively known as Neural Architecture Search (NAS). In general, NAS is a design software system for automating the search of effective neural architecture. This article proposes an X-learning NAS (XNAS) to automatically train a network’s structure and parameters. Our theoretical footing is built upon the subspace and correlation analyses between the input layer, hidden layer, and output layer. The design strategy hinges upon the underlying principle that the network should be coerced to learn how to structurally improvethe input/output correlation successively (i.e., layer by layer). It embraces both Progressive NAS (PNAS) and Regressive NAS (RNAS). For unsupervised RNAS, Principal Component Analysis (PCA) is a classic tool for subspace analyses. By further incorporating teacher’s guidance, PCA can be extended to Regression Component Analysis (RCA) to facilitate supervised NAS design. This allows the machine to extract components most critical to the targeted learning objective. We shall further extend the subspace analysis from multi-layer perceptrons to convolutional neural networks, via introduction of Convolutional-PCA (CPCA) or, more simply, Deep-PCA (DPCA). The supervised variant of DPCA will be named Deep-RCA (DRCA). The subspace analyses allow us to compute optimal eigenvectors (respectively, eigen-filters) and principal components (respectively, eigen-channels) for optimal NAS design of multi-layer perceptrons (respectively, convolutional neural networks). Based on the theoretical analysis, an X-learning paradigm is developed to jointly learn the structure and parameters of learning models. The objective is to reduce the network complexity while retaining (and sometimes improving) the performance. With carefully pre-selected baseline models, X-learning has shown great successes in numerous classification-type and/or regression-type applications. We have applied X-learning to the ImageNet datasets for classification and DIV2K for image enhancements. By applying X-learning to two types of baseline models, MobileNet and ResNet, both the low-power and high-performance application categories can be supported. Our simulations confirm that X-learning is by and large very competitive relative to the state-of-the-art approaches.
Deep Generative Mixture Model for Robust Imbalance Classification

Discovering hidden pattern from imbalanced data is a critical issue in various real-world applications. Existing classification methods usually suffer from the limitation of data especially for minority classes, and result in unstable prediction and low performance. In this paper, a deep generative classifier is proposed to mitigate this issue via both model perturbation and data perturbation. Specially, the proposed generative classifier is derived from a deep latent variable model where two variables are involved. One variable is to capture the essential information of the original data, denoted as latent codes, which are represented by a probability distribution rather than a single fixed value. The learnt distribution aims to enforce the uncertainty of model and implement model perturbation, thus, lead to stable predictions. The other variable is a prior to latent codes so that the codes are restricted to lie on components in Gaussian Mixture Model. As a confounder affecting generative processes of data (feature/label), the latent variables are supposed to capture the discriminative latent distribution and implement data perturbation. Extensive experiments have been conducted on widely-used real imbalanced image datasets. Experimental results demonstrate the superiority of our proposed model by comparing with popular imbalanced classification baselines on imbalance classification task.