Reviewerly/exHarmony huggingface dataset:
Fall event detection using the mean absolute deviated local ternary patterns and BiLSTM

Fall detection in elder persons may result in long-lasting injury that can have severe consequences for the rest of their lives. Additionally, prolonged delay in emergency treatment after the fall event escalates the chances of mortality. Hence, fall detection at early stage is critical in terms of providing timely aid with little complications and minimize hospitalization expenses. This work aims to provide an effective and efficient healthcare solution to determine the event of fall detection for elderly persons. We aim to address this fall detection problem for elder persons living lonely and encounter issues in case they fall and are unable to call for assistance. In this paper, we present a fall detection framework by proposing a novel feature space mean absolute deviated-local ternary patterns (MAD-LTP) to examine the environmental sounds and used these features to train the BiLSTM for fall events detection. Our proposed MAD-LTP features successfully address the limitations of existing features i.e., non-robust over dynamic pattern detection, brute force optimization, intolerance over non-uniform noise, etc., for fall detection. Performance of our system is evaluated on three diverse datasets i.e., The daily sounds, A3 Fall 2.0, and our in-house developed MSP-UET fall detection dataset. We compared the performance of the proposed framework against the state-of-the-art methods. We obtained an accuracy of 93.5%, 98.29%, and 98% for the daily sounds, A3 Fall 2.0, and our in-house developed MSP-UET fall detection dataset. Experimental findings indicate the reliability of our method for fall event detection.
On the Influence and Correction of Water Content on pXRF Analysis of Lateritic Nickel Ore Deposits in the Context of Open Pit Mines of New-Caledonia

In a number of applications, the use of portable X-ray fluorescence (pXRF) instruments offers a time and cost-saving alternative to standard laboratory instruments. This is particularly true in a mining context where decisions must be taken quickly in the field. However, pXRF is a technique known to be efficient, provided that samples are well prepared, i.e., dried and finely ground. On the mine face, little-to-no sample preparation is conceivable as mining vehicles must be able to operate continuously. Therefore, solutions have to be found even for raw materials and one of the most critical problems is the sample water content, in particular in the context of open pit mines in a tropical area. A large number of analysis shows that knowledge of humidity enables the measured concentration to be effectively corrected for the three instruments used (Niton, X-met, Titan). It is possible to overcome the difficulty of measuring water content in the field by fixing it to its maximum value (saturation). The results show that the saturation method is reliable, or at least, promising.
Energy-Efficient High-Speed ASIC Implementation of Convolutional Neural Network Using Novel Reduced Critical-Path Design

Convolutional Neural Network (CNN) plays an important role in several machine learning tasks related to speech, image, and video processing applications.The increasing demand for faster processing in real-time applications requires high-speed implementation of CNN.However, in general, CNN involves higher latency due to the computationally intensive behavior of the convolutional layer.While state-of-the-art architecture provides efficient dataflow of the convolutional operations, this paper proposes a hardwareefficient, high-speed convolution block for ASIC implementation of the CNN algorithm.The proposed convolution block is designed using a novel bit-level-multiply-accumulator (BLMAC) with a modified Booth encoder and a Wallace reduction tree.The critical path of the overall architecture is significantly shortened due to the time-optimized implementation of the proposed BLMAC, which is a main component of the convolution process.Critical path analysis and dataflow strategy are also provided to demonstrate the acceleration of the proposed design.The proposed architecture was synthesized using Synopsys Design Compiler to prove its accelerated processing.The ASIC synthesis results of the proposed architecture using a 65nm standard cell library show at least 53% reduction in latency, 52.2% reduction in area-delay product, and 54.2% reduction in power-delay product compared to the state-of-the-art architecture.
SGAE: Stacked Graph Autoencoder for Deep Clustering

Unsupervised clustering is a crucial issue in data mining and pattern recognition. Based on deep learning paradigms, deep clustering algorithms have been studied extensively and obtained superior performance in various applications. However, most of previous methods did not use helpful information from neighborhood relations to form group-separated space, and the feature embedding is usually distorted during the training process. To tackle the former limitation, we develop a graph convolution based unsupervised learning algorithm named Stacked Graph Autoencoder (SGAE). Specifically, SGAE utilizes the message passing mechanism to aggregate information from neighbors and obtain a meaningful and separated latent representation. Since the adjacency matrix is unavailable in clustering tasks, a graph construction approach with two pruning strategies is introduced to generate a transition matrix. To reduce the distortion caused by the multi-layered network training process, we further propose a topological structure preservation mechanism. It uses the constructed adjacency graph as supervised information, to maintain the relationship between nodes in the original space. Experiments on several popular benchmark datasets show that SGAE achieves significant improvements compared to unsupervised and semi-supervised deep clustering methods.
A Novel Framework for Open-Set Authentication of Internet of Things Using Limited Devices

The Internet of Things (IoT) is promising to transform a wide range of fields. However, the open nature of IoT makes it exposed to cybersecurity threats, among which identity spoofing is a typical example. Physical layer authentication, which identifies IoT devices based on the physical layer characteristics of signals, serves as an effective way to counteract identity spoofing. In this paper, we propose a deep learning-based framework for the open-set authentication of IoT devices. Specifically, additive angular margin softmax (AAMSoftmax) was utilized to enhance the discriminability of learned features and a modified OpenMAX classifier was employed to adaptively identify authorized devices and distinguish unauthorized ones. The experimental results for both simulated data and real ADS-B (Automatic Dependent Surveillance-Broadcast) data indicate that our framework achieved superior performance compared to current approaches, especially when the number of devices used for training is limited.
University Piano Education Visualization System under the Background of Distance Education Based on 5G Network

University piano education online learning courses supplement learning and have quickly advanced distance education and the essence of improving Internet innovation. Experts of piano education visualization system and distance piano education visualization system are a world specification, which offers various university and online open-source courses, for those who seek an online expert for piano education events. The individual online piano learning course is another choice for learning the education visualization system via progressive Internet correspondence. Past techniques depend on the profound learning piano education system courses of learning and information mining distance education. Existing strategies present specialized difficulties, which include the difficulty of the piano learning via an online network. As such, this study aims to establish a university piano education visualization system framework via a distance education background utilizing the fifth-generation (5G) network; the distance education learning for piano education visualization system course using the 5G network is proposed. The remote sensor shows an assortment of methods for grownups to partake in the online learning preparation. The idea of online communication within a network of instructors and understudies is extraordinary. When considering training-based or execution-based courses, there is a likelihood that the learning system may exhibit an inferior quality of sound, and the sound of the piano notes may lag with time over the network connection. Online learning provides adaptability and internationalization and has the capacity to interface with countless individuals and bring them together in the online learning environment. In this situation, the straightforward transportation and installment of remote sensors, the user of the distance educator 5G network is advised to view it from a commonsense perspective. Furthermore, there is a need to advance online piano education training in instructive exercises.
The intelligent academic library: review of AI projects &amp; potential for Caribbean libraries

Purpose The purpose of this paper is to propose the use of artificial intelligence (AI) in aspects of Caribbean academic libraries for greater levels of efficacy as well as overall widespread reach and access. This paper will further emphasize the importance of AI in fostering a smooth workforce, reducing opportunities for error, while maximizing on resource output for Caribbean academic libraries. Design/methodology/approach Through an extensive literature and content analysis, the research assessed various literatures from across the world, in wide ranging sectors, to attest to the plausibility of AI implementation in Caribbean academic libraries. Findings Revealing the possibilities for fostering great levels of efficacy as well as saves time and reduces the possibility for inefficiencies in the system through smoother check in and out as well as more accountability.[AQ3] Research limitations/implications This research focused primarily on proposing the use of AI in the daily operations of the academic library. It further attested to the implementations and application efficacy for intelligent academic libraries. Originality/value To the best of the authors’ knowledge, this is the first study of the kind for Caribbean academic libraries and brought light to the fact that presently academic libraries can benefit from greater levels of efficacy and accuracy through the careful employment of AI in certain key areas in academic libraries. This study further reports about the impact of AI on academic libraries.
An Analysis of Anki Usage and Strategy of First-Year Medical Students in a Structure and Function Course

It is well known that undergraduate medical education demands learners to master large amounts of material. To help cope with learning, medical students have resorted to e-learning resources that include video lectures, visual mnemonics, and flashcard systems. The purpose of the current study was to determine the usage of Anki (Damien Elmes, https://apps.ankiweb.net/) among first-year medical students in an integrated first-year module consisting of anatomy and physiology. Additionally, this study aimed to elucidate how students used Anki in conjunction with their lectures. Sixty first-year medical students were recruited in a twelve-week longitudinal study consisting of three surveys (Weeks 1, 7, and 13) about usage of Anki and their perceptions of how to use it with formal lecture. About 70% of participants utilized Anki in the course and the usage increased significantly from Week 1 to Weeks 7 and 13. There was variation to how many days a week students used Anki. Survey data shows that students value formal lectures and prefer to supplement their studies with Anki. A large proportion of first-year medical students use Anki to supplement their studies. Faculty should consider ways to incorporate Anki into their teaching to facilitate student learning through active recall and spaced repetition.
Evaluation of magnetite as an indicator mineral for porphyry Cu exploration: a case study using bedrock and stream sediments at the Casino porphyry Cu–Au–Mo deposit, Yukon, Canada

The trace element composition of detrital magnetite grains recovered from six local streams around the Casino high-grade porphyry Cu-Au-Mo deposit, west-central Yukon, is compared with igneous and magmatic-hydrothermal magnetite recovered from mineralized and unmineralized host rocks at the deposit. Linear discriminant analysis of 12 elements (Mg, Al, Ti, V, Mn, Co, Cr, Ni, Cu, Zn, Ga and Ge) and plots of Ti versus Ni/Cr are used to discriminate between magmatic-hydrothermal magnetite from the potassic alteration zone and igneous magnetite from granodiorite and quartz monzonite hosting the deposit. Magmatic-hydrothermal magnetite with a trace element composition similar to that from the potassic alteration zone at Casino is identifiable in stream sediments draining the deposit. Copper in magmatic-hydrothermal magnetite, present as minute inclusions of sulfide minerals such as chalcopyrite or substituted within the magnetite crystal lattice, is a strong indicator of Cu mineralization. We show that the chemical compositions of magnetite recovered from stream sediments can be used to explore for porphyry systems. Thematic collection: This article is part of the Applications of Innovations in Geochemical Data Analysis collection available at: https://www.lyellcollection.org/cc/applications-of-innovations-in-geochemical-data-analysis Supplementary material: https://doi.org/10.6084/m9.figshare.c.5896900
Data Envelopment Analysis of linguistic features and passage relevance for open-domain Question Answering

Question Answering (QA) systems play an important role in today’s human–computer interaction systems. QA performance can be significantly improved using effective answer passage retrieval and ranking techniques. Our focus in this paper is on both non machine learning-based and deep learning-based passage retrieval and ranking systems for QA to leverage linguistic features within the text of questions and passages and improve passage ranking effectiveness. We propose a decoupled linguistic and linear programming-based approach for passage ranking using the Data Envelopment Analysis (DEA) technique to improve over well-established answer passage retrieval techniques. Our method scores passages using information retrieval and deep learning relevance metrics, represents retrieved passages using their relevance scores and several linguistic features, and finally makes use of DEA to re-rank the retrieved list of passages. The high effectiveness and significance of our proposed passage ranking method is demonstrated based on several experiments that we have conducted on a standard benchmark data set.
Teaching Presence, Self-Regulated Learning and Learning Satisfaction on Distance Learning for Students in a Nursing Education Program

The novel coronavirus pandemic has dramatically affected how nursing students are educated. Distance learning has become the norm, and an evaluation of learning achievement is needed. This is a mixed-method study of teaching presence, self-regulated learning, and learning satisfaction in distance learning to evaluate the learning achievement of students in a nursing education program. Ninety-four students for quantitative and seven students for qualitative research were sampled. All the sampled students attend the nursing education program in Seoul and Gyeonggi Province and were enrolled during the first semester of 2020. Quantitative data were analyzed using SPSS/WIN 21.0, and qualitative data were analyzed via content analysis in NVivo 12. Teaching presence and self-regulated learning were identified as the factors affecting learning satisfaction. In a focus group interview, teaching presence increased when the students received feedback and saw the faces of their professors. Self-regulated learning occurred when they had opportunities to practice self-study and leadership and when they formed relationships between professors and colleagues. These methods have also been recognized to increase learning satisfaction. Considering the results of this study, it is necessary to develop teaching methods that enhance the learning satisfaction of students in distance learning nursing education programs.
Deep Learning-based Hate Speech Detection in Code-mixed Tamil Text

Social media is a great source of communication. People use various social media platforms, such as Twitter, Facebook, and Instagram, for sharing their ideas, opinions, and feelings. Users of different age groups, cultures, education backgrounds manipulate these powerful mediums of communication. Even though it gives all the benefits of knowledge sharing among the users, it has a dark side too. Despite setting restrictions from the corresponding sites, many users use abusive language to blemish the status and image of someone. So it is highly the need of the hour for the government or the particular social media platform to sift out those unwanted hate texts before diffusing them. Finding the hate text is one of the emerging research topics in Natural Language Processing where the model predicts the given text as hate text or not. This automated hate text detection becomes tedious when we consider the Indian languages due to a lack of data. Moreover, Indian people are multilingual and use code-mixed patterns to express their thoughts. The unavailability of the annotated Tamil-English dataset and the lack of a standard model make this task more challenging. In our paper, to handle such code-mixed data, a dataset is created with 10000 Tamil-English code-mixed texts collected from Twitter. These are annotated as hate text/non-hate text. In this paper, we use a synonym-based Bi-LSTM model for classifying hate non-hate text in tweets.
Breast cancer diagnosis using feature selection techniques

This study focuses on feature selection for breast cancer diagnosis. Since the feature selection became a crucial task in machine learning, we will experiment some filter, wrapper approach and embedded approach on Wisconsin breast cancer dataset, which is commonly used by researchers who use machine-learning methods for breast cancer diagnosis. The performance of the feature selection method is evaluated by classification accuracy using two kinds of classifiers SVM and KNN.
Audio-lossless robust watermarking against desynchronization attacks

Audio-lossless robust watermarking (ALRW) is a new research issue in the field of information hiding, which can resist those content-preserving manipulations while can restore the cover audio files completely in case of no attacks. Most ALRW countermeasures proposed in the literature usually focus on additive noise-like manipulations such as MP3 compression, low-pass filtering and Gaussian additive noise, but few are resistant to challenging desynchronization attacks such as random cropping and time-scale modification (TSM). The main reason is that the exploited robust features in the existing ALRW algorithms are related to the sample position. In this paper, we propose an ALRW method by efficiently embedding a watermark into the histogram shape of the low-frequency component after the discrete wavelet transform (DWT) and reversibly hiding the distortion due to the robust watermark embedding as the compensation information for restoration of the cover audio file. The use of the histogram shape in the DWT low-frequency component as the exploited robust feature is: 1) mathematically invariant to TSM; 2) robust to random cropping attacks since an audio clip before and after the cropping often has similar distribution, and 3) resistant to interpolation errors during time scaling and those common audio processing operations. To reduce the compensation information, the robust watermarking process is elaborately and luminously designed to represent the difference between the original and the robust watermarked audio. As a result, an ALRW system against desynchronization attacks is achieved with satisfactory performance. Experimental results show that the proposed ALRW scheme can effectively reduce the compensation information and provide strong robustness to those common manipulations including TSM, random cropping, MP3 lossy compression and other noise-like manipulations. In case of no attacks, the cover audio signal can be recovered without any loss.
<scp>QGMS</scp>: A query growth model for personalization and diversification of semantic search based on differential ontology semantics using artificial intelligence

Abstract The inclusion of collective intelligence through a semantic focused affective computing can incorporate intelligence to web search and ensure its compliance with the Web 3.0. In this article, a query growth model with inclusive and exclusive ontology semantics has been proposed for diversification of query recommendation in semantic search. The ontology semantics include query augmented ontology generation, agent‐driven attractor‐distractor generation to yield a merged ontology, and endowment of merged ontology by using hybridization of a series of knowledge bases. The strategy further includes the formulation of a semantic network and entity leveraging based on description logics (DLs) to improve the quality of query recommendation. A novel hierarchical entropy cognitive similarity covariance model has been proposed for yielding the most appropriate recommendable query words. The strategy also encompasses the user‐click information for capturing the current user intents to improve the quality queries recommended in semantic search, and thereby incorporate personalization. Experimentations are conducted for the CHiC dataset and the Spring 2006 Query Log dataset and an average accuracy of 96.27% and 92.01%, respectively, with a very low false discovery rate of 0.06 and 0.1 for the respective datasets.
Innovator or collaborator? A cognitive network perspective to vision formation

Purpose Having a shared vision is crucial for innovation. The purpose of this paper is to investigate the effect of individual propensity to collaborate and innovate on the development of a shared vision. Design/methodology/approach The authors build a network in which each node represents the vision of one individual and link the network structure to individual propensity of collaboration and innovativeness. During organizational workshops in four multinational organizations, the authors collected individual visions in the form of images as well as text describing the approach to innovation from 85 employees. Findings The study maps individual visions for innovation as a cognitive network. The authors find that individual propensity to innovate or collaborate is related to different network centrality. Innovators, individuals who see innovation as an opportunity to change and grow, are located at the center of the cognitive network. Collaborators, who see innovation as an opportunity to collaborate, have a higher closeness centrality inside a cluster. Research limitations/implications This paper analyses visions as a network linking recent research in psychology with the managerial longing for a more thorough investigation of group cognition. The study contributes to literature on shared vision creation, suggesting the role which innovators and collaborators can occupy in the process. Originality/value This paper proposes how an approach based on a cognitive network can inform innovation management. The findings suggest that visions of innovators summarize the visions of a group, helping the development of an overall shared vision. Collaborators on the other hand are representative of specific clusters and can help developing radical visions.