Reviewerly/exHarmony huggingface dataset:
MedGraph: A semantic biomedical information retrieval framework using knowledge graph embedding for PubMed

Here we study the semantic search and retrieval problem in biomedical digital libraries. First, we introduce MedGraph, a knowledge graph embedding-based method that provides semantic relevance retrieval and ranking for the biomedical literature indexed in PubMed. Second, we evaluate our approach using PubMed's Best Match algorithm. Moreover, we compare our method MedGraph to a traditional TF-IDF-based algorithm. Third, we use a dataset extracted from PubMed, including 30 million articles' metadata such as abstracts, author information, citation information, and extracted biological entity mentions. We pull a subset of the dataset to evaluate MedGraph using predefined queries with ground truth ranked results. To our knowledge, this technique has not been explored before in biomedical information retrieval. In addition, our results provide some evidence that semantic approaches to search and relevance in biomedical digital libraries that rely on knowledge graph modeling offer better search relevance results when compared with traditional methods in terms of objective metrics.
Camera Motion Agnostic Method for Estimating 3D Human Poses

Although the performance of 3D human pose and shape estimation methods has improved considerably in recent years, existing approaches typically generate 3D poses defined in a camera or human-centered coordinate system. This makes it difficult to estimate a person's pure pose and motion in a world coordinate system for a video captured using a moving camera. To address this issue, this paper presents a camera motion agnostic approach for predicting 3D human pose and mesh defined in the world coordinate system. The core idea of the proposed approach is to estimate the difference between two adjacent global poses (i.e., global motion) that is invariant to selecting the coordinate system, instead of the global pose coupled to the camera motion. To this end, we propose a network based on bidirectional gated recurrent units (GRUs) that predicts the global motion sequence from the local pose sequence consisting of relative rotations of joints called global motion regressor (GMR). We use 3DPW and synthetic datasets, which are constructed in a moving-camera environment, for evaluation. We conduct extensive experiments and prove the effectiveness of the proposed method empirically.
Local search five‐element cycle optimized reLU‐BiLSTM for multilingual aspect‐based text classification

Summary Aspect‐based sentiment analysis has gained wide popularity due to its benefits of text extraction, classification, and ranking the overall sentiments of each feature extracted. However, the aspect‐based feature extraction techniques often result in acquiring more number aspects that refer to the same feature which arises the need for aspect‐based text classification. Since most of the existing techniques focus on monolingual aspect‐based sentimental analysis, we planned to develop a multilingual aspect‐based text classification for Indian languages. We perform the multilingual aspect‐based text classification on different morphologically rich and complex languages such as Hindi, Tamil, Malayalam, Bengali, Urdu, Telugu, and Sinhalese. To achieve this objective, in this article we present an optimized rectified linear unit (reLU) layer‐based bidirectional long short‐term memory (reLU‐BiLSTM) deep learning tool is developed. The parameters of the reLU‐BiLSTM architecture are optimized using the local search‐based five‐element cycle optimization algorithm (LSFECO) optimization algorithm. Initially, the proposed model preprocesses the multilingual texts obtained from the reviews using different techniques such as tokenization, special character removal, text normalization and so forth. The discrete and categorical features from the different languages are initially extracted by applying the bidirectional encoder representations from transformers (BERT) model which processes the sentences in the text in a layer‐by‐layer manner. The context learning and word embeddings (aspects) present in the text are identified using different approaches such as word mover's distance, continuous Bag‐of‐Words (CBOW), and Cosine similarity. The LSFECO optimized reLU‐BiLSTM architecture classifies the different aspects present in the embedding document to its corresponding classes (flowers, plants, animals, sports, politics, etc). The efficiency of the proposed methodology is evaluated using the text obtained from different text documents such as semantic relations from Wikipedia, Habeas Corpus (HC) Corpora, Sentiment Lexicons for 81 Languages, IIT Bombay English‐Hindi Parallel Corpus, and Indic Languages Multilingual Parallel Corpus. When compared to conventional techniques, the proposed methodology outperforms them in terms of entropy, coverage, purity, processing time, accuracy, F1‐score, recall, and precision.
Enhancing Design Activity and Review Experience Through Hybridizing Desktop and Virtual Environments

Although virtual reality (VR) applications can provide immersive experiences, the design process is mainly executed in a desktop environment (DE). The omniscient point-of-view of the desktop allows the user to explore shapes and objects and their relationships with each other conveniently and efficiently. However, unlike a VR environment (VE), it is difficult for the user to perceive the space through their natural spatial perception from the first-person point-of-view in a DE. Although VEs and DEs have advantages and limitations, research exploring the design activity and review experience using these two mediums is limited. Thus, a method of integrating the characteristics and advantages of existing VEs and DEs is proposed as a hybrid design environment (HE). Empirical comparisons of the three environments (VE, DE, and HE) were performed according to the indicators of system usability, perceived workload, and satisfaction through a series of user experiments ( n = 12), showing that the HE was superior on all indicators to the VE. Specifically, participants evaluated the usability of the VE method as the worst and found no significant difference between the usability of the DE and the HE. Perceived workload demand was significantly greater in the VE, and satisfaction levels were also significantly lower than in the other environments. Although the HE allowed both desktop and VR use, participants spent most of their time in the DE and used the VE primarily for design review.
Sentiment Classification of Chinese Tourism Reviews Based on ERNIE-Gram+GCN

Nowadays, tourists increasingly prefer to check the reviews of attractions before traveling to decide whether to visit them or not. To respond to the change in the way tourists choose attractions, it is important to classify the reviews of attractions with high precision. In addition, more and more tourists like to use emojis to express their satisfaction or dissatisfaction with the attractions. In this paper, we built a dataset for Chinese attraction evaluation incorporating emojis (CAEIE) and proposed an explicitly n-gram masking method to enhance the integration of coarse-grained information into a pre-training (ERNIE-Gram) and Text Graph Convolutional Network (textGCN) (E2G) model to classify the dataset with a high accuracy. The E2G preprocesses the text and feeds it to ERNIE-Gram and TextGCN. ERNIE-Gram was trained using its unique mask mechanism to obtain the final probabilities. TextGCN used the dataset to construct heterogeneous graphs with comment text and words, which were trained to obtain a representation of the document output category probabilities. The two probabilities were calculated to obtain the final results. To demonstrate the validity of the E2G model, this paper was compared with advanced models. After experiments, it was shown that E2G had a good classification effect on the CAEIE dataset, and the accuracy of classification was up to 97.37%. Furthermore, the accuracy of E2G was 1.37% and 1.35% ahead of ERNIE-Gram and TextGCN, respectively. In addition, two sets of comparison experiments were conducted to verify the performance of TextGCN and TextGAT on the CAEIE dataset. The final results showed that ERNIE and ERNIE-Gram combined TextGCN and TextGAT, respectively, and TextGCN performed 1.6% and 2.15% ahead. This paper compared the effects of eight activation functions on the second layer of the TextGCN and the activation-function-rectified linear unit 6 (RELU6) with the best results based on experiments.
Employing a Multilingual Transformer Model for Segmenting Unpunctuated Arabic Text

Long unpunctuated texts containing complex linguistic sentences are a stumbling block to processing any low-resource languages. Thus, approaches that attempt to segment lengthy texts with no proper punctuation into simple candidate sentences are a vitally important preprocessing task in many hard-to-solve NLP applications. To this end, we propose a preprocessing solution for segmenting unpunctuated Arabic texts into potentially independent clauses. This solution consists of: (1) a punctuation detection model built on top of a multilingual BERT-based model, and (2) some generic linguistic rules for validating the resulting segmentation. Furthermore, we optimize the strategy of applying these linguistic rules using our suggested greedy-like algorithm. We call the proposed solution PDTS (standing for Punctuation Detector for Text Segmentation). Concerning the evaluation, we showcase how PDTS can be effectively employed as a text tokenizer for unpunctuated documents (i.e., mimicking the transcribed audio-to-text documents). Experimental findings across two evaluation protocols (involving an ablation study and a human-based judgment) demonstrate that PDTS is practically effective in both performance quality and computational cost. In particular, PDTS can reach an average F-Measure score of approximately 75%, indicating a minimum improvement of roughly 13% (i.e., compared to the performance of the state-of-the-art competitor models).
Leveraging Artificial Intelligence to Analyze Consumer Sentiments within Their Context: A Case Study of Always #LikeAGirl Campaign

Because practitioners and scholars are increasingly using artificial intelligence (AI) to analyze consumer sentiments toward social media–based campaigns, we compared various supervised machine-learning (ML) algorithms (four traditional ML-based algorithms and two proprietary deep-learning-based models from Amazon and Google) in terms of their performances in classifying user comments into a sentiment category. We adopted the #LikeAGirl campaign by Always feminine hygiene brand as a case and analyzed the sentiments of 19,198 YouTube comments on the campaign using different supervised ML algorithms. Results indicate that the two proprietary models from Amazon and Google performed better than traditional supervised ML algorithms. We then used unsupervised ML to reveal the hidden topics from positive and negative consumer comments. The study has important methodological implications for advertising practitioners and scholars.
Competitive Higher Education Teacher for the Digital World

The world we live in today holds some uncertainties. The processes of automation, big data, the digital environment, global crises, and the interconnectedness of international society are changing common technological patterns and the educational system. Among the essential features of competitiveness of teachers in higher education are mobility, digital competencies, adaptability, participation in scientific networks and projects, and continuous acquisition of new technological skills. For this reason, teachers must continuously develop their professional skills to remain competitive and create professionals in higher education. Current conditions are fostering global demand for teachers who produce pedagogical and scientific developments.&lt;br /&gt; This article aims to solve the problem of defining the structure, content, and requirements of scientific and methodological support for higher education teachers’ competitiveness in a digital world in the context of global challenges and risks. Theoretically, the problem is defined by the need for new approaches to explaining the concept of teacher competitiveness and the need to develop a psychological and teaching methodology for training and developing teacher competitiveness. In practice, we conclude that researchers must develop personal, professional, procedural, and technological enhancements to support higher education faculty competitiveness in a digital world.
Parametric non-parallel support vector machines for pattern classification

This paper proposes Parametric non-parallel support vector machines for binary pattern classification. Through an intelligent redesigning of the Support vector machine optimisation, not only do we bring noise resilience into the model, but also retain its sparsity. Our model exhibits properties similar to Support vector machines, hence many SVM related learning algorithms can be extended to make it scalable for large scale problems. Experimental results on several benchmark UCI datasets validate our claims.
Evaluation of Perceptual Difference in Dynamic Projection Mapping with and without Movement of the Target Surface

High-frame-rate visual feedback has been proven effective for projection mapping on quickly moving targets. However, the quantitative conditions of frame rates have not been elucidated. In particular, considering that the human dynamic visual acuity generally declines as the moving target velocity increases, it may be argued that the tracking accuracy required for projection mapping on moving targets can be lower than that required for fixed targets. To examine the above-mentioned conditions of frame rates, this study compares them based on two types of projection mapping systems having equivalent relative movements to each other: projection mapping from a fixed projector/camera system to a moving target and projection mapping from a moving projector/camera system to a fixed target. We examined the effects of the target movements by having stationary observers visually recognize and evaluate them. Using the weighted up-down method, we measured the frame time that provides a just noticeable difference (JND) to the projection mapping. We found that when the visual feedback is generated at a frame time of 2 ms, the frame time that provides a JND is 3.72 ms on average for fixed targets and 3.94 ms on average for moving targets: a 1%-level significant difference. Taking the individual differences and experimental errors into account, this is not a very large variation. This suggests that, in projection mapping to moving targets, we should realize a tracking accuracy as precise as that in projection mapping to fixed targets.
Precise Subtyping for Asynchronous Multiparty Sessions

Session subtyping is a cornerstone of refinement of communicating processes: A process implementing a session type (i.e., a communication protocol) T can be safely used whenever a process implementing one of its supertypes T ′ is expected, in any context, without introducing deadlocks nor other communication errors. As a consequence, whenever T ≤ T ′ holds, it is safe to replace an implementation of T ′ with an implementation of the subtype T , which may allow for more optimised communication patterns. We present the first formalisation of the precise subtyping relation for asynchronous multiparty sessions. We show that our subtyping relation is sound (i.e., guarantees safe process replacement, as outlined above) and also complete : Any extension of the relation is unsound. To achieve our results, we develop a novel session decomposition technique, from full session types (including internal/external choices) into single input/output session trees (without choices). We cover multiparty sessions with asynchronous interaction, where messages are transmitted via FIFO queues (as in the TCP/IP protocol), and prove that our subtyping is both operationally and denotationally precise. Our session decomposition technique expresses the subtyping relation as a composition of refinement relations between single input/output trees and provides a simple reasoning principle for asynchronous message optimisations.
A Framework for Online Hate Speech Detection on Code-mixed Hindi-English Text and Hindi Text in Devanagari

Social Media has been growing and has provided the world with a platform to opine, debate, display, and discuss like never before. It has a major influence in research areas that analyze human behavior and social groups, and the phenomenon of social interactions is even being used in areas such as Internet of Things. This constant stream of data connecting individuals and organizations across the globe has had a tremendous impact on the functioning of society and even has the power to sway elections. Despite having numerous benefits, social media has certain issues such as the prevalence of fake news, which has also led to the rise of the hate speech phenomenon. Due to lax security throughout these social media platforms, these issues continue to exist without any repercussions. This leads to cyberbullying, defamation, and presents grave security concerns. Even though some work has been done independently on native scripts, hate speech detection, and code-mixed data, there exists a lack of academic work and research in the area of detecting hate speech in transliterated code-mixed data and in-text containing native language scripts. Research in this field is inhibited greatly due to the multiple variations in grammar and spelling and in general a lack of availability of annotated datasets, especially when it comes to native languages. This article comes up with a method to automate hate speech detection in code-mixed and native language text. The article presents an architecture containing a Tabnet classifier-based model trained on features extracted using MuRIL from transliterated code-mixed textual data. The article also shows that the same model works well on features extracted from text in Devanagari despite being trained on transliterated data.
Maximal Mixed-Drove Co-occurrence Patterns

Abstract One of the interesting types of spatio-temporal patterns is the Mixed-Drove Co-occurrence Pattern (MDCOP), which represents types of objects frequently located together in both space and time. To discover such patterns, methods based on the well-known Apriori strategy are widely used. They involve determining multi-element MDCOPs by building them up iteratively starting with the two-element patterns and then successively adding another element in each iteration. This approach can be very costly, particularly when the data is dense enough to form patterns of significant size. In this paper, we introduce a definition of a new pattern type called a Maximal Mixed-Drove Co-occurrence Pattern. We also propose a new algorithm MAXMDCOP-Miner, which resigns from popular Apriori strategy of generating candidates and, therefore, can discover long pattern without processing all their subsets. Experiments performed on synthetic and real datasets show that MAXMDCOP-Miner has high performance, in particular for dense datasets or tasks with low user-defined thresholds of spatial or time prevalence.
Detecting Spam SMS Using Self Attention Mechanism

Short Message Service (SMS) is swiftly emerging as the most secure method of communication due to its extensive coverage, dependability, and power efficiency. When compared to application to person (A2P) communications, person to person (P2P) texting is less secure, allowing anyone to send messages, which could result in an assault. Spammers take use of this chance to transmit dangerous content, engage in destructive actions, and harass others. Furthermore, such messages might waste a lot of time, and vital messages can be missed. Therefore, reliable spam identification in SMS has emerged as a critical issue. More recent extension of self–attention mechanism in transformer increases the ability of context in natural language processing. Transformer such as Bidirectional Encoder Representations from Transformers (BERT) works better than Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) for its scanning ability in both order (left to right, right to left) and generating context. This paper interpreted a spam detection model based on self mechanism using BERT on kaggle dataset. Our proposed model outperforms than the machine learning algorithms and deep learning with accuracy 98.80%.
Fall Detection System Based on Pose Estimation in Videos

Fall is a serious public health issue, especially for the elderly. There are many studies on fall detection, with the goal of ever-increasing the accuracy, flexibility, and feasibility of implementation in real life. In the last 5 years, the release of many highly-accurate, real-time, open-source pose estimation models offers a unique opportunity to develop effective and easy-to-deploy fall detection systems. In this study, we build two threshold-based fall detection algorithms respectively using the OpenPose and MoveNet pose estimation model. We evaluate the algorithms on two public datasets, namely UR Fall Detection and Charfi2012 dataset. The algorithm using OpenPose achieves an accuracy rate of 68.04% and the one using MoveNet 70.79%. We discuss the benefits of using pose estimation models in detecting falls, as well as the limitations of threshold-based algorithms, and suggest future research directions.
Mining Statistically Significant Patterns with High Utility

Abstract Statistically significant pattern mining (SSPM) is to mine patterns with significance based on hypothesis test. Under the constraint of statistical significance, our study aims to introduce a new preference relation into high utility patterns and to discover high utility and significant patterns (HUSPs) from transaction datasets, which has never been considered in existing SSPM problems. Our approach can be divided into two parts, HUSP-Mining and HUSP-Test. HUSP-Mining looks for HUSP candidates and HUSP-Test tests their significance. HUSP-Mining is not outputting all high utility itemsets (HUIs) as HUSP candidates; it is established based on candidate length and testable support requirements which can remove many insignificant HUIs early in the mining process; compared with the traditional HUIs mining algorithm, it can get candidates in a short time without losing the real HUSPs. HUSP-Test is to draw significant patterns from the results of HUSP-Mining based on Fisher’s test. We propose an iterative multiple testing procedure, which can alternately and efficiently reject a hypothesis and safely ignore the hypotheses that have less utility than the rejected hypothesis. HUSP-Test controls Family-wise Error Rate (FWER) under a user-defined threshold by correcting the test level which can find more HUSPs than standard Bonferroni’s control. Substantial experiments on real datasets show that our algorithm can draw HUSPs efficiently from transaction datasets with strong mathematical guarantee.