Reviewerly/exHarmony huggingface dataset:
Data Transmission in Wearable Sensor Network for Human Activity Monitoring Using Embedded Classifier Technique

Abstract: The recent development of wireless wearable sensor networks has opened up a slew of new possibilities in industries as diverse as healthcare, medicine, activity monitoring, sports, safety, human-machine interface, and more. The battery-powered sensor nodes' longevity is critical to the technology's success. This research proposes a new strategy for increasing the lifetime of wearable sensor networks by eliminating redundant data transmissions. The proposed solution is based on embedded classifiers that allow sensor nodes to determine whether current sensor readings should be sent to the cluster head. A strategy was developed to train the classifiers, which takes into account the impact of data selection on the accuracy of a recognition system. This method was used to create a wearable sensor network prototype for human monitoring of activity Experiments were carried out in the real world to assess the novel method in terms of network lifetime, energy usage, and human activity recognition accuracy. The proposed strategy allows for a large increase in network lifetime while maintaining excellent activity detection accuracy, according to the results of the experimental evaluation. Experiments have also demonstrated that the technology has advantages over state-of-the-art data transmission reduction strategies. Keywords: wireless sensor network; wearable sensors; activity recognition; lifetime; energy con- sumption; transmission suppression; embedded machine learning
Light Weight Encoder-Decoder for Underwater Images in Internet of Underwater Things (IoUT)

Internet of Underwater Things (IoUT) explores the applications of Internet of Things (IoT) to monitor the sea animal habitat, observe atmosphere, and predict defense and predict defense and disaster. Raw underwater images are affected by absorption and dispersal of light due to underwater environment. Low power computational devices are preferred to cut down the cost of IoUT devices. Because of underwater environment nature, transmission of underwater images captured by underwater devices is considered as a big challenge. There is a need to provide solutions to amplify color, contrast and brightness aspects of captured underwater images to provide good visual understanding. Conventional compression techniques used for terrestrial environment, causes ringing artefacts due to the variable characteristics of underwater images. Deep image compression techniques consume more computational power and time, making them least efficient for low power computational devices. In this study, a low computational power and less time-consuming image compression technique is proposed to achieve high encoding efficiency and good reconstruction quality of underwater images. The proposed technique suggests using Convolutional Neural Network (CNN) at encoder side, which compresses and retains the structural data of the underwater image. And relative global histogram stretching based technique has been used at the decoder side to enhance the reconstructed underwater image. The proposed methodology is compared with conventional methods like Joint Pictures Experts Group (JPEG), Better Portable Graphics (BPG), Contrast Limited Adaptive Histogram Equalization (CLAHE) and deep learning techniques like Super Resolution Convolutional Neural Network (SRCNN) and Residual encoder-decoder methods to evaluate the reconstructed image quality. The presented work provides high quality image in comparison with both conventional and SRCNN method.
Research on safety monitoring and early warning technology of intelligent auxiliary live working

Aiming at the lack of effective safety distance monitoring means in the live working process, the intelligent auxiliary live working safety monitoring and early warning technology is studied. A real-time live working safety distance monitoring and police method based on 3D point cloud technology and Beidou positioning technology is proposed. Through the unmanned air vehicle equipped with the lidar point cloud data acquisition system and the multi-lens oblique photography sensor, the point cloud data of the tower and line in the live working site are collected to establish the three-dimensional map of the working environment. The laser point cloud technology is used to draw the three-dimensional electronic fence on the three-dimensional map to simulate the early warning area of the working site. The human body positioning model of field workers is established based on multi-antenna Beidou high precision positioning technology. By calculating the distance between the human body positioning model and the boundary of the three-dimensional electronic fence in real time, the safety distance monitoring and early warning of live work are realized. Through the accuracy test on the safety distance monitoring and early warning device tower, it is found that the head and foot positioning accuracy is the highest, and the deviation is kept within 0.16 m. The experimental results verify the accuracy and reliability of the safety distance monitoring and warning system, which can be applied to the actual live operation.
Long-Term Prediction of Solar Radiation Using XGboost, LSTM, and Machine Learning Algorithms

The rapid development and application of different solar energy-based technologies have prompted research into the accurate prediction of solar energy availability. In the existing literature, different models and methods have been proposed for the accurate prediction of solar energy, however, there is still a need for the development of more accurate algorithms to forecast solar energy. Also, it is unclear in existing literature if a model can accurately and consistently predict solar energy resources when applied to different data from various locations (with varying parameters). Therefore, in this study, four different artificial intelligence models are compared for long-term solar radiation prediction. These models include long-short term memory, XGboost, multilinear regression, and decision tree regression. The models have been selected based on their high performance for similar tasks in existing works of literature. The performance of these models when applied for solar radiation prediction considering 1-minute time intervals in a location in Senegal, West Africa is presented. Furthermore, these models are applied to two different datasets (measured with two different instruments) with varying input parameters. The overarching goal of this research is to determine if a model can consistently outperform others (when predicting solar radiation), despite the variation in input parameters and locations. The prediction of global horizontal irradiance and diffused irradiance is estimated in this study. The evaluation metric used in observing the performance of the models are R, root mean square error (RMSE), and mean absolute error (MAE).
Using Machine Learning to Locate Evidence More Efficiently

Evidence that machine learning can assist article selection and minimize manual screening burden for scholarly research has been documented in the peer-reviewed literature for more than 20 years. Despite the robust evidence and continual technological advances, uptake has been slow among research teams. This chapter discusses the benefits of using machine learning (ML) and other automation tools on bibliographic data and argues that academic librarians are well-positioned to partner with research teams around this application of ML. An overview of the automation approaches used at UNC's Health Sciences Library (HSL) is discussed along with detailed accounts of multiple success stories of when HSL librarians partnered with research teams to locate evidence more efficiently. Finally, a discussion of likely barriers and possible solutions to increase uptake of this technology among academic librarians is provided.
Self-Supervised Audio-and-Text Pre-training with Extremely Low-Resource Parallel Data

Multimodal pre-training for audio-and-text has recently been proved to be effective and has significantly improved the performance of many downstream speech understanding tasks. However, these state-of-the-art pre-training audio-text models work well only when provided with large amount of parallel audio-and-text data, which brings challenges on many languages that are rich in unimodal corpora but scarce of parallel cross-modal corpus. In this paper, we investigate whether it is possible to pre-train an audio-text multimodal model with extremely low-resource parallel data and extra non-parallel unimodal data. Our pre-training framework consists of the following components: (1) Intra-modal Denoising Auto-Encoding (IDAE), which is able to reconstruct input text (audio) representations from a noisy version of itself. (2) Cross-modal Denoising Auto-Encoding (CDAE), which is pre-trained to reconstruct the input text (audio), given both a noisy version of the input text (audio) and the corresponding translated noisy audio features (text embeddings). (3) Iterative Denoising Process (IDP), which iteratively translates raw audio (text) and the corresponding text embeddings (audio features) translated from previous iteration into the new less-noisy text embeddings (audio features). We adapt a dual cross-modal Transformer as our backbone model which consists of two unimodal encoders for IDAE and two cross-modal encoders for CDAE and IDP. Our method achieves comparable performance on multiple downstream speech understanding tasks compared with the model pre-trained on fully parallel data, demonstrating the great potential of the proposed method.
Privacy Protection In Learning Fair Representations

In this paper, we develop a framework to achieve a desirable trade-off between fairness, inference accuracy and privacy protection in the inference as service scenario. Instead of sending raw data to the cloud, we conduct a random mapping of the data, which will increase privacy protection and mitigate bias but reduce inference accuracy. To properly address the trade-off, we formulate an optimization problem to find the optimal transformation map. As the problem is non-convex in general, we develop an iterative algorithm to find the desired map. Numerical examples show that the proposed method has better performance than gradient ascent in the convergence speed, solution quality and algorithm stability.
NLP-SEA 2021 Workshop Summary

Inherent strengths and capabilities in Natural Language Processing (NLP), such as topics modeling, content categorization, context extraction, sentiment analysis, and text-speech conversion, have helped in rapid growth. At the same time, this rapid growth has led to numerous challenges for software development engineers both on a technical and managerial level. NLP has changed the way we interact with computers, and it'll continue doing so in the future. These technologies, mainly based on artificial intelligence (AI) and Machine Learning (ML), would be the underlying force for transformation from data-driven to intelligence-driven endeavors, as they shape and improve communication technology in the years to come. The literature and research communities are observing the same technological advancements in the domain of software engineering introduced by the use of NLP techniques in conjunction with Artificial Intelligence. Thus, the NLP-SEA (Software Engineering Automation: A Natural Language Perspective) series of workshops is a means to contribute to the advancement and furthering of knowledge in this direction. The focus of this workshop is to provide an opportunity for the researchers and practitioners in academia and industry to provide a diverse, but significant body of research contributions on every aspect related to the application of NLP in the automated software and system engineering domain.
Improving approximate vacuum prepared by the adiabatic quantum computation

According to the quantum adiabatic theorem, we can in principle obtain a true vacuum of a quantum system starting from a trivial vacuum of a simple Hamiltonian. In actual adiabatic digital quantum simulation with finite time length and non-infinitesimal time steps, we can only obtain an approximate vacuum that is supposed to be a superposition of a true vacuum and excited states. We propose a procedure to improve the approximate vacuum.
Tertiary precious-metal deposition in time and space in the Great Basin, USA

Tertiary precious-metal deposits in the Great Basin, USA, fall into two general groups, epithermal deposits (primarily volcanic-hosted) and sedimentary-rock-hosted deposits (including Carlin-type deposits). The epithermal and sedimentary-rock-hosted deposits apparently are related to three igneous assemblages, which differ with regard to petrology, geochemistry, eruptive style, and associated mineral deposits. Distribution of deposit types forms coherent patterns in time and space, but differences in grade and tonnage between the epithermal groups are minor. Most epithermal deposits have gross values between $30 million and $2 billion, but only two of them are comparable in size and value to giant porphyry Cu deposits. The origin of sedimentary-rock-hosted and some epithermal deposits is linked by their coherent pattern in time and space. Low-sulphidation deposits in the bimodal basalt-rhyolite assemblage have a different origin that is related to the inception of the Yellowstone hot spot.
Lithological and geochemical characteristics and paleoclimatic conditions of the Turonian-Santonian deposits of the epicontinental basin of the Russian plate in the region of the Voronezh anteclise

The results of geochemical research of the cyclic section of the Upper Cretaceous deposits near Staryi Oskol town at Belgorod province and paleogeographical and paleoclimatic interpretation of these data were given for the first time.
New data on the formation conditions of the Drazhnoye deposit gold mineralization (Republic Sakha, Yakutia)

New results of thermal, baric, and geochemical studies indicate that quartz veins within the deposit were formed in mesozonal conditions at depths 3–9 km from heterogeneous CO 2 –H 2 O fluids containing water and carbon dioxide with low concentrations of salts and high concentrations of CO 2 , which is typical for ore-forming fluids of orogenic vein gold deposits. According to the phase composition, there are three types of fluid inclusions: 1) carbon dioxide- water; 2) gas-filled with dense carbon dioxide; 3) biphasic gas-liquid water-salt solutions. There are Na, Mg, and Fe chlorides predominated in the ore-forming fluids. It is assumed that the fluids migrated along the regional Adycha-Taryn shear zone, uplifting to the Earth’s surface at supra-lithostatic pressures, followed by the deposition of ore elements and the vein quartz formation during tectonic movements along shear zones.
Video surveillance-based identification of abnormal behavior of people in key areas

In this paper, from the demand of intelligent security monitoring of gas metering facilities, abnormal behavior monitoring and identification techniques for video terminals in key areas are proposed, mainly including personnel intrusion detection based on Faster-Rcnn deep neural network model, personnel wandering detection based on DeepSORT multi-target tracking model, and personnel holding foreign objects detection based on multi-target detection and area overlap. This method was tested in real scenarios with fast and accurate recognition and good results.
THE EFFECT OF PHYPHOX APPLICATION ASSISTANT GUIDED INQUIRIES ON ABILITY STUDENT CREATIVE THINKING

This study aims to determine the effect of using a guided inquiry-based phyphox smartphone sensor application on creative thinking skills and improvement. The sample of this research is the students of class X IPA 3 SMA N 1 Kotabumi. The research design used was One Group Pretest-Posttest. The treatment is in the form of partially elastic collision learning assisted by a guided inquiry-based phyphox application that can be processed using Microsoft Excel. Data on students' creative thinking skills were obtained using a description test instrument. The technique of analyzing student learning outcomes data is using Paired Sample T-Test. Based on the results of the study, the average score of creative thinking skills increased by 35%, the average N-gain was 0.52 sig. (2-tailed) the value was less than 0.05. The results of partially elastic collision learning using a phyphox smartphone sensor based on guided inquiry have a significant effect on increasing students' creative thinking skills.
Towards Sustainable Living through Sentiment Analysis during Covid19

Artificial intelligence is the process of the machine to perform with the simulation of human intelligence. Computing within the field of emotions paves the recognitions to sentiment analysis. Sentiment analysis is the method of capturing the emotions behind a text whether or not it's positive, negative or neutral. Sentiment Analysis (SA) or Opinion Mining (OA) is the process to provide computational treatment to unstructured data to categorize and identify the sentiments or emotions expressed in a piece of text. It combines Natural Language Processing Techniques and Machine Learning Techniques. This technology is additionally referred to as opinion mining or feeling computing. Sentiment Analysis uses the ideas of machine learning alongside an AI based process called NLP to extract and analyze the data, emotions, information from the text. This work explores the impact of social media during covid 19 and possible link between sustainable living and health care with the usage of sentiments. This paper address the sustainable development goal 3 (good health and wellbeing) of SDG 2030 and a possible way towards sustainable living through sentiment analysis.
Automatic Text Summarization of Hindi Text Using Extractive Approach

Text summarization is one of the applications of NLP (Natural Language Processing) that is having a large impact on users’ lives. The resultant text must convey the important information to intended users. A lot of users face the problem of content repetition while reading from the internet. Most of the time same information is repeated in majority of documents. It wastes valuable time of users. This problem raises the need of automatic text summarization. Automatic text summarization belongs to the NLP area of Machine Learning. This technique generates the shorter version of text with the help of some automated tool. The shorter version of text conveys relevant and important information to users. In the proposed work, the author is introducing an extractive approach for summarization of text. This approach is based on cosine similarity technique, which automatically generates the summarized text while keeping the relevant and meaningful information intact.
Cataloging Bad Smells in Use Case Descriptions and Automating Their Detection

Use case modeling is popular to represent the functionality of the system to be developed, and it consists of two parts: a use case diagram and use case descriptions. Use case descriptions are structured text written in natural language, and the usage of natural language can lead to poor descriptions such as ambiguous, inconsistent, and/or incomplete descriptions. Poor descriptions lead to missing requirements and eliciting incorrect requirements as well as less comprehensiveness of the produced use case model. This paper proposes a technique to automate detecting bad smells of use case descriptions, i.e., symptoms of poor descriptions. At first, to clarify bad smells, we analyzed existing use case models to discover poor use case descriptions concretely and developed the list of bad smells, i.e., a catalog of bad smells. Some of the bad smells can be refined into measures using the Goal-Question-Metric paradigm to automate their detection. The main contributions of this paper are the developed catalog of bad smells and the automated detection of these bad smells. We have implemented an automated smell detector for 22 bad smells at first and assessed its usefulness by an experiment. As a result, the first version of our tool got a precision ratio of 0.591 and a recall ratio of 0.981. Through evaluating our catalog and the automated tool, we found additional six bad smells and two metrics. Then, we obtained the precision of 0.596 and the recall of 1.000 by our final version of the automated tool.
Automating Bad Smell Detection in Goal Refinement of Goal Models

Goal refinement is a crucial step in goal-oriented requirements analysis to create a goal model of high quality. Poor goal refinement leads to missing requirements and eliciting incorrect requirements as well as less comprehensiveness of produced goal models. This paper proposes a technique to automate detecting bad smells of goal refinement, symptoms of poor goal refinement. At first, to clarify bad smells, we asked subjects to discover poor goal refinement concretely. Based on the classification of the specified poor refinement, we defined four types of bad smells of goal refinement: Low Semantic Relation, Many Siblings, Few Siblings, and Coarse Grained Leaf, and developed two types of measures to detect them: measures on the graph structure of a goal model and semantic similarity of goal descriptions. We have implemented a supporting tool to detect bad smells and assessed its usefulness by an experiment.
Computerized Classification Method for Histological Classification of Masses on Breast Ultrasonographic Images Using Convolutional Neural Networks with ROI Pooling

It can be difficult for clinicians to correctly determine histological classifications of masses on breast ultrasonographic images. The purpose of this study was to develop a computerized classification method for histological classification of masses on breast ultrasonographic images using convolutional neural networks (CNN) with a ROI pooling that analyzes feature maps focusing on the mass region. Our dataset consisted of 585 breast ultrasonographic images obtained from 585 patients. It included 288 malignant masses (218 invasive and 70 noninvasive carcinomas) and 297 benign masses (115 cysts and 182 fibroadenomas). In this study, we developed a modified CNN model based on ResNet-18, in which the ROI pooling and two fully connected layers with a softmax function were introduced after the second and fourth residual block on ResNet-18, respectively. The proposed CNN model was employed to distinguish among four different types of histological classifications for masses. A three-fold cross validation method was used for training and testing the proposed CNN model. The average accuracy, sensitivity, specificity, positive predictive value and negative predictive value for the proposed CNN model were 81.7%, 91.0%, 91.2%, 91.0%, and 91.2%, respectively. Those results were substantially greater than those with ResNet-18 (70.3%, 83.0%, 87.2%, 86.3%, and 84.1%).