Reviewerly/exHarmony huggingface dataset:
Radical empiricism and machine learning research

Abstract I contrast the “data fitting” vs “data interpreting” approaches to data science along three dimensions: Expediency, Transparency, and Explainability. “Data fitting” is driven by the faith that the secret to rational decisions lies in the data itself. In contrast, the data-interpreting school views data, not as a sole source of knowledge but as an auxiliary means for interpreting reality, and “reality” stands for the processes that generate the data. I argue for restoring balance to data science through a task-dependent symbiosis of fitting and interpreting, guided by the Logic of Causation.
Part-Based Semantic Transform for Few-Shot Semantic Segmentation

Few-shot semantic segmentation remains an open problem for the lack of an effective method to handle the semantic misalignment between objects. In this article, we propose part-based semantic transform (PST) and target at aligning object semantics in support images with those in query images by semantic decomposition-and-match. The semantic decomposition process is implemented with prototype mixture models (PMMs), which use an expectation–maximization (EM) algorithm to decompose object semantics into multiple prototypes corresponding to object parts. The semantic match between prototypes is performed with a min-cost flow module, which encourages correct correspondence while depressing mismatches between object parts. With semantic decomposition-and-match, PST enforces the network’s tolerance to objects’ appearance and/or pose variation and facilities channelwise and spatial semantic activation of objects in query images. Extensive experiments on Pascal VOC and MS-COCO datasets show that PST significantly improves upon state-of-the-arts. In particular, on MS-COCO, it improves the performance of five-shot semantic segmentation by up to 7.79% with a moderate cost of inference speed and model size. Code for PST is released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Yang-Bob/PST</uri> .
Deep Speaker Recognition: Process, Progress, and Challenges

Speaker recognition is related to human biometrics dealing with the identification of speakers from their speech.Speaker recognition is an active research area and being widely investigated using artificially intelligent mechanisms.Though speaker recognition systems were previously constructed using handcrafted statistical means of machine learning, currently it is being shifted to state-of-the-art deep learning strategies.Further, deep learning being a fast-paced domain, an absence of comprehensive survey is observed in the current deep speaker recognition technologies.In this paper, we focus on deep speaker recognition technologies.The paper particularly introduces a taxonomy, explains the progress, architectural strategies and processes of some distinctive approaches.Further, the manuscript classifies and enlists the currently available datasets and programming tools.Finally, the paper investigates the challenges and future directives of deep speaker recognition technology.
X-Fact: A New Benchmark Dataset for Multilingual Fact Checking

In this work, we introduce X-FACT: the largest publicly available multilingual dataset for factual verification of naturally existing realworld claims.The dataset contains short statements in 25 languages and is labeled for veracity by expert fact-checkers.The dataset includes a multilingual evaluation benchmark that measures both out-of-domain generalization, and zero-shot capabilities of the multilingual models.Using state-of-the-art multilingual transformer-based models, we develop several automated fact-checking models that, along with textual claims, make use of additional metadata and evidence from news stories retrieved using a search engine.Empirically, our best model attains an F-score of around 40%, suggesting that our dataset is a challenging benchmark for evaluation of multilingual fact-checking models.
An Underwater Image Enhancement Algorithm Based on Generative Adversarial Network and Natural Image Quality Evaluation Index

When underwater vehicles work, underwater images are often absorbed by light and scattered and diffused by floating objects, which leads to the degradation of underwater images. The generative adversarial network (GAN) is widely used in underwater image enhancement tasks because it can complete image-style conversions with high efficiency and high quality. Although the GAN converts low-quality underwater images into high-quality underwater images (truth images), the dataset of truth images also affects high-quality underwater images. However, an underwater truth image lacks underwater image enhancement, which leads to a poor effect of the generated image. Thus, this paper proposes to add the natural image quality evaluation (NIQE) index to the GAN to provide generated images with higher contrast and make them more in line with the perception of the human eye, and at the same time, grant generated images a better effect than the truth images set by the existing dataset. In this paper, several groups of experiments are compared, and through the subjective evaluation and objective evaluation indicators, it is verified that the enhanced image of this algorithm is better than the truth image set by the existing dataset.
Sound Detection Monitoring Tool in CNC Milling Sounds by K-Means Clustering Algorithm

Computer numerical control (CNC) is a machine used in the manufacturing industry to produce components quickly for the engineering field or the desired shape. In the milling process carried out by CNC machines, sometimes vibrations occur that cause unwanted cracks or damage, which if left unchecked, will cause more severe damage. For this reason, this study describes how to monitor and analyze the sound produced by CNC during the milling process. This study uses six sound sample videos from YouTube, and there are two modes: (1) the operating mode is three different shapes with XY, XZ, and XYZ axes, and the second (2) is based on material differences. Namely, wood, Styrofoam, and plastic. The sound generated from all samples of the CNC milling processes will be detected using a sound detection program that has been designed in the LabVIEW using a simple microphone. The resulting sound frequency will be analyzed using the fast Fourier transform (FFT) process in spectral measurements, which will produce the amplitude and frequency of the detected sound in real time in the form of a graph. All frequency results that have been obtained from the sound detection monitoring tool in the CNC milling machine will be imported into the K-means clustering algorithm where the different frequencies between the resonant frequency and noise will be classified. Based on the experiments conducted, the sound detection program can detect sounds with a significant level of sensitivity.
INVESTIGATING ENGLISH TEACHING STRATEGIES TO REDUCE ONLINE TEACHING OBSTACLES IN THE SECONDARY SCHOOL

The teachers became the key in teaching-learning activities. Teachers had a relation to the students’ success in learning English. So, the English teachers should know the best strategies should be implemented to teach English. It included the strategies to reduce the obstacles that they experienced in online teaching process. This study generally investigated English teaching strategies to reduce online teaching obstacles in teaching English using E-Learning Madrasah Platform at MAN 1 Bandar Lampung. This research was carried out through a case study. The subjects were four English teachers at MAN 1 Bandar Lampung. The data were obtained from a questionnaire, an interview, and an observation. The data were analyzed through data collection, data reduction, data display, and conclusion. The findings showed that English teachers applied several strategies to reduce online teaching obstacles in teaching English using E-Learning Madrasah Platform. The strategies were applying asynchronous strategy, using an alternative media to support online teaching process, designing lesson plans, material, and online learning activities properly, explaining the materials through video conferences, instructing the students to do the assignments clearly, assessing the students’ works properly, and giving oral and written feedback to the students’ work to make the students were enthusiastic in boosting their learning achievements. In conclusion, all English teachers could reduce the obstacles aligned with each obstacle found.
A New Noisy Random Forest Based Method for Feature Selection

Abstract Feature selection is an essential pre-processing step in data mining. It aims at identifying the highly predictive feature subset out of a large set of candidate features. Several approaches for feature selection have been proposed in the literature. Random Forests (RF) are among the most used machine learning algorithms not just for their excellent prediction accuracy but also for their ability to select informative variables with their associated variable importance measures. Sometimes RF model over-fits on noisy features, which lead to choosing the noisy features as the informative variables and eliminating the significant ones. Whereas, eliminating and preventing those noisy features first, the low ranked features may become more important. In this study we propose a new variant of RF that provides unbiased variable selection where a noisy feature trick is used to address this problem. First, we add a noisy feature to a dataset. Second, the noisy feature is used as a stopping criterion. If the noisy feature is selected as the best splitting feature, then we stop the creation process because at this level, the model starts to over-fit on the noisy features. Finally, the best subset of features is selected out of the best-ranked feature regarding the Geni impurity of this new variant of RF. To test the validity and the effectiveness of the proposed method, we compare it with RF variable importance measure using eleven benchmarking datasets.
Graph Collaborative Filtering Based on Dual-Message Propagation Mechanism

The recommender system is a popular research topic in the past decades, and various models have been proposed. Among them, collaborative filtering (CF) is one of the most effective approaches. The underlying philosophy of CF is to capture and utilize two types of relationships among users/items, that is, the user–item preferences and the similarities among users/items, to make recommendations. In recent years, graph neural networks (GNNs) have gained popularity in many research fields, and in the recommendation field, GNN-based CF models have also been proposed, which are shown to have impressive performance. However, in our research, we observe a crucial drawback of these models, that is, while they can explicitly model and utilize the user–item preferences, the other necessary type of relationship, that is, the similarities among users/items, can only be implied and then utilized, which seems to hinder the performance of these models. Motivated by this, in this article, we first propose a novel dual-message propagation mechanism (DPM). The DPM can explicitly model and utilize both preferences and similarities to make recommendations; thus, it seems to be a better realization of CF’s philosophy. Then, a dual-message graph CF (DGCF) model is proposed. Different from the existing models, in the DGCF, each user’s/item’s embedding is processed by two GNNs, with one handling the preferences and the other handling the similarities. Extensive experiments conducted on three real-world datasets demonstrate that DGCF substantially outperforms state-of-the-art CF models, and the small amount of sacrifice of time efficiency is tolerable considering the substantial improvement of model performance.
Generative Adversarial Networks for Anonymized Healthcare of Lung Cancer Patients

The digital twin in health care is the dynamic digital representation of the patient’s anatomy and physiology through computational models which are continuously updated from clinical data. Furthermore, used in combination with machine learning technologies, it should help doctors in therapeutic path and in minimally invasive intervention procedures. Confidentiality of medical records is a very delicate issue, therefore some anonymization process is mandatory in order to maintain patients privacy. Moreover, data availability is very limited in some health domains like lung cancer treatment. Hence, generation of synthetic data conformed to real data would solve this issue. In this paper, the use of generative adversarial networks (GAN) for the generation of synthetic data of lung cancer patients is introduced as a tool to solve this problem in the form of anonymized synthetic patients. Generated synthetic patients are validated using both statistical methods, as well as by oncologists using the indirect mortality rate obtained for patients in different stages.
Dynamic Optimization of Chemical Processes Based on Modified Sailfish Optimizer Combined with an Equal Division Method

The optimal solution of the chemical dynamic optimization problem is the basis of automatic control operation in the chemical process, which can reduce energy consumption, increase production efficiency, and maximize economic benefit. In this paper, a modified sailfish optimizer (MSFO) combined with an equal division method is proposed for solving chemical dynamic optimization problems. Based on the basic sailfish optimizer, firstly, the tent chaotic mapping strategy is introduced to disturb the initialization of sailfish and sardine populations to avoid the loss of population diversity. Secondly, an adaptive linear reduction strategy of attack parameters is proposed to enhance the exploration and exploitation ability of sailfish. Thirdly, the updating formula of sardine position is modified, and the global optimal solution is used to attract all sardine positions, which can avoid the premature phenomenon of the algorithm. Eventually, the MSFO is applied to solve six classical optimization cases of chemical engineering to evaluate its feasibility. The experimental results are analyzed and compared with other optimization methods to prove the superiority of the MSFO in solving chemical dynamic optimization problems.
Seeking Patterns, Not just Memorizing Procedures: Contrastive Learning for Solving Math Word Problems

Math Word Problem (MWP) solving needs to discover the quantitative relationships over natural language narratives. Recent work shows that existing models memorize procedures from context and rely on shallow heuristics to solve MWPs. In this paper, we look at this issue and argue that the cause is a lack of overall understanding of MWP patterns. We first investigate how a neural network understands patterns only from semantics, and observe that, if the prototype equations are the same, most problems get closer representations and those representations apart from them or close to other prototypes tend to produce wrong solutions. Inspired by it, we propose a contrastive learning approach, where the neural network perceives the divergence of patterns. We collect contrastive examples by converting the prototype equation into a tree and seeking similar tree structures. The solving model is trained with an auxiliary objective on the collected examples, resulting in the representations of problems with similar prototypes being pulled closer. We conduct experiments on the Chinese dataset Math23k and the English dataset MathQA. Our method greatly improves the performance in monolingual and multilingual settings.
Personalized and Adaptive Context-Aware Mobile Learning: Review, challenges and future directions

Due to the outbreak of COVID 19, digital learning has become the most efficient learning and teaching technique adopted across the world. The pervasiveness of Personalized and Adaptive Context-Aware Mobile Learning (PACAML) technologies is improving the academic performances of learners by providing an efficient learning platform that supports social interactivity, context sensitivity, connectivity, and individuality in a ubiquitous manner. Several studies have demonstrated the efficacy of PACAML in a modern and innovative educational environment. Based on the recent studies and development of mobile learning technologies, there is clearly a gap in the research that provides a comprehensive body of knowledge on PACAML. In this paper, a review has been conducted on the existing PACAML, analyzing the recent research and development progress using Kitchenham et al. (2009) for systematic reviews. The review was conducted on 25 papers which were selected using the PRISMA technique to put forward the quality criteria that are based on the research aims, objectives and knowledge relevant to the study of PACAML. The results identified the contextual information used in the PACAML studies, the infrastructural requirements of PACAML, the application of PACAML in functional educational settings and the major methodological approaches applied in the studies of PACAML. Finally, the paper presents challenges and future directions that will be of interest to researchers in the educational technologies in the context of PACAML.
Negation and uncertainty detection in clinical texts written in Spanish: a deep learning-based approach

Detecting negation and uncertainty is crucial for medical text mining applications; otherwise, extracted information can be incorrectly identified as real or factual events. Although several approaches have been proposed to detect negation and uncertainty in clinical texts, most efforts have focused on the English language. Most proposals developed for Spanish have focused mainly on negation detection and do not deal with uncertainty. In this paper, we propose a deep learning-based approach for both negation and uncertainty detection in clinical texts written in Spanish. The proposed approach explores two deep learning methods to achieve this goal: (i) Bidirectional Long-Short Term Memory with a Conditional Random Field layer (BiLSTM-CRF) and (ii) Bidirectional Encoder Representation for Transformers (BERT). The approach was evaluated using NUBES and IULA, two public corpora for the Spanish language. The results obtained showed an F-score of 92% and 80% in the scope recognition task for negation and uncertainty, respectively. We also present the results of a validation process conducted using a real-life annotated dataset from clinical notes belonging to cancer patients. The proposed approach shows the feasibility of deep learning-based methods to detect negation and uncertainty in Spanish clinical texts. Experiments also highlighted that this approach improves performance in the scope recognition task compared to other proposals in the biomedical domain.
Extract, transform, load framework for the conversion of health databases to OMOP

Common data models standardize the structures and semantics of health datasets, enabling reproducibility and large-scale studies that leverage the data from multiple locations and settings. The Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) is one of the leading common data models. While there is a strong incentive to convert datasets to OMOP, the conversion is time and resource-intensive, leaving the research community in need of tools for mapping data to OMOP. We propose an extract, transform, load (ETL) framework that is metadata-driven and generic across source datasets. The ETL framework uses a new data manipulation language (DML) that organizes SQL snippets in YAML. Our framework includes a compiler that converts YAML files with mapping logic into an ETL script. Access to the ETL framework is available via a web application, allowing users to upload and edit YAML files via web editor and obtain an ETL SQL script for use in development environments. The structure of the DML maximizes readability, refactoring, and maintainability, while minimizing technical debt and standardizing the writing of ETL operations for mapping to OMOP. Our framework also supports transparency of the mapping process and reuse by different institutions.
Technology enhanced learning environments in higher education: A cross-discipline study on teacher and student perceptions

Teacher and student perceptions of using technology enhanced learning (TEL) in higher education have received growing attention, particularly during COVID-19, however existing studies are mainly disciplinespecific. This study adopts a holistic cross-disciplinary approach. It compares teacher and student perceptions on defining TEL, promotors and barriers for its use, and solutions offered for better use of TEL in the future. Both qualitative and quantitative data were collected from an Australian university. A total of 75 teachers and 48 students completed an online survey, and of these participants, 24 teachers and 29 students participated in follow-up focus group interviews that included Kahoot! surveys. Quantitative results show that teacher and student perceptions on TEL were generally aligned except that self-reported technology savviness and confidence was rated higher than how students and staff rated each other. Qualitative analyses reveal that both teachers and students identified the main promoters for TEL as being: modern and expected in higher education, while being equalising, efficient, engaging, authentic, collaborative and flexible. The common barriers for using TEL were identified as fear, time, organisational culture, knowledge and technical/support issues, along with the perceived pitfalls of distraction, and superficial student learning. Solutions offered for TEL in the future from staff focused on the institution and a desire for strategic, pedagogical and holistic approaches, while students focussed on the accessibility, flexibility and collaborative potential of TEL. This cross-discipline pre-COVID-19 study of TEL perceptions offered by teachers and students has contributed to knowledge in this area by identifying barriers and solutions for TEL common to all disciplines that have the potential to be applied to whole of institution strategic approaches for the more effective use of TEL in teaching and learning in higher education. Student accessibility to TEL and the development of pedagogically sound digital learning resources bringing together educational developers and discipline experts are of particular relevance during and post-COVID-19.
Hybrid design for cloud data security using combination of AES, ECC and LSB steganography

The ever-growing popularity of cloud systems is prompting a revolutionary change in information technology field. Parallel and flexible services offered by cloud technology are making it the ultimate solution for individuals as well as for organisations of all size. The grave security concerns present in cloud must be addressed to protect the data and privacy of huge number of cloud users. We present a hybrid solution to tackle the key management problem. The data in the cloud is encrypted with AES encryption with private key. The AES 256-bits key is then encrypted with ECC. The ECC encrypted key will be embedded in the user's image with LSB steganography. If the user decides to share cloud data with a second user, he only needs to embed the AES key in the second user's image. Using steganography, ECC and AES we can achieve strong security posture and efficient key management and distribution for multiple users.