Reviewerly/exHarmony huggingface dataset:
Reporting as a Strategy in Facilitating the Communicative Competence of English Learners

This study aimed at determining the procedures followed in reporting strategy; how reporting strategy helped students enhance their communicative competence; and suggestions on how to improve the reporting as strategy in enhancing students’ communicative abilities. Five Focus Group Discussion (FGDs) were considered composed of tertiary English major students. Results showed that class reporting was usually done through voluntary basis and groupings. Also, class reporting was found to be beneficial because it enhanced not only the students’ language competence, self-confidence and soft skills but also developed their hidden talents. Moreover, it enabled students to discover their language strengths and weaknesses. It was implicated that orientation may be done before a strategy can be used; levelling of students’ may be done; group members may be given specific task; and rubrics may be explained before the strategy may be applied.
Semantic-Aware Triplet Loss for Image Classification

Successful image classification requires a discriminative representation learning model for images. To approach this idea, deep metric learning (DML), serving as building a basic feature space with a pre-defined metric, has demonstrated compelling performance over the years. DML is often implemented with a carefully crafted loss function, such as the representative triplet loss, which encourages a positive sample to be by a fixed margin closer to the anchor than the negative. Despite its efficacy, the negative samples are treated uniformly, rendering the feature space less informative since different negative samples can be largely different from the anchor. In this work, we, for the first time, propose to exploit the semantic information inherent in discrete class labels as an aid for the triplet loss. Specifically, we build a bi-level negative sampling strategy, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</i> . <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</i> ., strong negative and weak negative sampling, with the guidance of an external knowledge source, from which rich class semantics can be extracted. With several fine-grained and complementary triplet losses based on this strategy, our method is enhanced with semantic awareness for image classification. In addition, to coordinate with the complicated training dynamics, we devise an ad-hoc Semantic Relation Weighting module, which consistently inspects model states and dynamically adjusts the importance of each triplet loss. It is worth noting that our method is plug-and-play, and we thus test its validity over various backbones and knowledge sources. Both qualitative and quantitative experimental results on benchmark datasets demonstrate the effectiveness of employing semantics for image classification.
Anomaly Detection Model for Key Places Based on Improved YOLOv5

In recent years, key places such as underground stations and train stations, which are crowded and highly mobile, have become key targets for abnormal behaviour such as violence by some extremists or violent elements. The public safety risks in key places cannot be ignored, and the need to detect abnormal behaviour in key places is urgent in order to protect the personal safety of the people in such key places. When abnormal people and abnormal events occur in key places, timely detection and early warning are required to prevent and protect the safety of the people in a timely manner. Therefore, a real-time anomaly detection system based on the improved YOLOv5 key place video is proposed for such key places with dense personnel, intricate and complex identities, low accuracy of anomalous behaviour detection and slow detection speed. The method improves the target recognition effect by improving the loss function and optimising the resolution. Test results show that under the same training conditions, the improved YOLOv5 network has a significantly higher correct rate of anomalous behaviour detection and a faster detection speed of anomalous behaviour compared with the original YOLOv5 network.
Music Note Segmentation Recognition Algorithm Based on Nonlinear Feature Detection

In the current note recognition methods, different optimal kernel functions are used for different note features, and the weight of each kernel function in the classification is obtained through learning, but the baseband structural features of note signals can not be obtained, and the deviation of baseband extraction results is large, which has the problem of large error of recognition results, and recognition takes a long time. In this paper, a method of segmented recognition of music notes based on nonlinear feature detection is proposed. The vector space matrix is used to analyze and process the music notes, so as to reflect the correlation of music notes and obtain the music retrieval results; The segmentation line is determined by setting the constraint conditions. The kernel function method in nonlinear feature detection is introduced into music note recognition. According to the definition of kernel function and kernel matrix, FFT is used to convert the low-pass component in audio frequency domain, so as to realize fundamental frequency recognition. The baseband extraction result obtained by this method is more accurate, the recognition accuracy is high, and it is practical.
Focusing on Features with Higher Importance Degree: A Significance-Wise Mechanism for Image Restoration

With the aim of retrieving high-quality images from corrupted versions, image restoration meets the extensive demand of application scenarios. State-of-theart methods solve this problem by means of designing convolution blocks in multistage architectures. However, the existing methods blindly extract and fuse features without considering which parts of the features are more effective for image restoration. In this paper, we propose a significance-wise mechanism with the goal of identifying the importance degree of each region in the feature representation and providing crucial feature information. Through the calculation of nonlinear function in the significance-wise mechanism, the region which is more conducive to image restoration will get higher importance values. Two important types of information are generated within the attention mechanism, including: (i) feature-sufficient maps with abundant feature representations, and (ii) significance-wise maps with the importance degree of patch information in the corrupted images. With the coordination of featuresufficient maps and significance-wise maps, the network architecture can focus higher attention on crucial parts of feature information. Furthermore, we design a multistage feature fusion block with the significance-wise mechanism. Compared with existing attention mechanisms, our significance-wise mechanism has the ability to identify and generate crucial feature representation for multiple image restoration tasks. Due to the novel design of the network, we successfully implement multiple restoration tasks only by fine-tuning the number of channels once on a network. Abundant quantitative and qualitative experiments demonstrate that the effective image restoration network (EIRN) outperforms existing state-of-the-art algorithms on eleven datasets across a series of image restoration tasks, including image deblurring, denoising, super-resolution, and deraining. The source code and pretrained models will be available at https://github.com/XinyueZhangqdu/EIRN.
Video Summarization Using Hybrid Statistical Approach

Video summarization is not only the key to effective cataloging and browsing video, but also works as an implanted signal to discover the video object. This paper automatically evaluates the concepts of the video, and creates a synopsis of the most important concepts of the video. The video summarization process generates a synopsis with the maximum amount of information and least amount of frames. According to the survey the top 25 shots are extracted from the test set as key frames and the summarized video is produced. The resulting summarized video is of minimized length and covers the key frames from the original video that have been extracted. The video length is reduced by 87.04% making it much more effective.
Quantitative Research on Cumulative Effect of Transformer Winding and its Influence Degree Based on Time-Frequency Analysis of Vibration Signal

Winding deformation caused by short-circuit is one of the main causes of transformer damage. Even if the transformer successfully withstands one short-circuit impact, cumulative effect of multiple short-circuit impacts will cause sudden permanent deformation of the winding. Internal winding deformation is a hidden fault due to the visual blocking of the shell, so it is extremely important to measure the cumulative effect and analyze its influence. In this paper, a SFSZ7-31500/110 decommissioned transformer was used for short-time multiple short-circuit impact tests. The measurability of cumulative effect was determined by calculating the difference between the measured value and the reference value of the acceleration of the time-domain vibration acceleration signal, and then a time-frequency matrix was constructed based on Wigner-Ville Distribution(WVD) time-frequency analysis. Based on Fuzzy C-Means（FCM）clustering and the singular values of the matrix, the membership degree of winding mechanical state was calculated, and the influence degree of the cumulative effect was quantitatively analyzed. Results show the method can reflect the deformation trend and degree of winding before the impedance change rate. Research provides some reference for the mechanical state evaluation and fault early warning of windings, and also has a guiding significance for the safe and stable operation of transformers.
Testing Expected Utility

In this paper, we provide two novel representation theorems for the expected utility model by suitably adjusting independence and continuity. Our representation results suggest that an analyst can refute choice by expected utility whenever violations of weak forms of independence are observed, as well as confirm choice by expected utility by verifying some relatively mild forms of independence.
Tech to TakeCare: IoT-Based Smart Solution for Real-Time Supervision

In spite of the ubiquitous evolution of the Internet and the continuous improvements in technology, a miniscule bit has been done to simplify the lives of the key caregivers of infants, senior citizens, and the physically disabled. Recovery from an accident or mishap in golden-ager is sluggish. A simple fall can be life threatening. Likewise, a protracted wet bed for infants may lead to ailments. Young mothers and custodians of the elderly and the incapacitated often have to lay out their whole lives for everyday monitoring errands. Through this work, we propose a dependable, real-time smart supervision system for basic supervision tasks of infants, senior citizens, and the physically disabled. With the aid of the Internet of Things and ThingSpeak cloud, we recommend to construct a solution that records and tracks numerous diverse vitals of babies, elderly, and the incapacitated like blood pressure, body temperature, and heart rate. Furthermore, we also proposition to utilize sound and moisture sensors to amass a regular record of the activities of infants, elderly, and the incapacitated, thus tracking their routine and communicating weekly analysis of the same to the custodians, aiding them in the regulation of healthy lifestyle patterns. Moreover, crucial warnings would be issued and communicated to the custodian’s mobile from anywhere in alarming situations. This study is particularly advantageous for the employed class for supervision of their babies and aged while alleviating uncertainties of the mounting competitive employment situation and upholding monetary stability.
Technological Sustainability in the Twenty-First Century

Sustainability is all about saving the planet for future generations, and technology has become an integral part of its assistance. For the researchers, the use of legitimate secondary sources has reduced the study time and allowed them to analyze data from many authentic secondary sources. The researcher in this study has decided to employ qualitative data to perform the investigation and reached the conclusion that the world needs both scientific know-how and creative thinking to combat today’s global challenges. For example, textiles’ future growth and use will be profoundly impacted by nanotechnology. Ideas and information must be disseminated by using social media. Nursing education and practice should make use of this potential. Blockchain technology may be used to improve the internet’s centralization, transparency, egalitarianism, and accountability. Future emissions and energy usage may be reduced by using electric vehicles in the transportation industry. There are many more dangers and risks linked with artificial intelligence than there are with our existing conveniences. Data transparency may be improved by using the blockchain in an area where sharing information is required. Thus, technological sustainability is not merely a phrase but a purpose to improve the economy, society, and the environment.
Shock Filtering based Additive White Gaussian Noise Removal in Radiographic Scans

Image processing techniques play crucial role in medical applications especially in diagnosis of ailments and diseases. The obtained images have to be segmented and carefully analysed to diagnose the minor as well as serious ailments. Therefore, noise suppression in the medical images becomes crucial. In this paper, Shock Filter is implemented to denoise the medical images. The method proposed in this paper uses Shock Filter to diffuse the noisy part of the image while preserving the details of the original image. Shock filter deploys the morphological operators and aids in edge enhancement and segmentation of Image. In addition to implementation of proposed method, experimental analysis is discussed to claim the efficiency of method based on parameters like PSNR etc.
Wearables for Health Promotion: An Interdisciplinary Review

This review synthesizes the findings from 250 research papers that study the application of wearable technology for health purposes. We surfaced 18 distinct cross-disciplinary themes reflecting the current status of literature on health wearables. Using the input-mechanism-output framework, we organized these themes in an array that can help identify the gaps in research. Studies that covered input factors have largely been techno-centric, exploring the design of various health wearables. While studies on user acceptance and engagement are increasing, there remains room for growth in these user-centric aspects. Relatively few studies explored theoretically-backed explanations of the role of health wearables. These mainly focused on mechanisms of adoption, while post-adoption use and health behavior change were less explored. Output factors were studied in both individuals and organizational settings, with the former receiving more attention than the latter. We discuss the progress of research on health wearables from an interdisciplinary angle and highlight the role of social scientists for the advancement of research on wearable health.
WSAN: An Effective Model of Weakly Supervised Similarity Analysis Network for the Lung CT Images

With the rapid development of medical imaging technologies, the high-resolution CT image data is of great value for medical research as well as a clinical diagnosis. The paper takes lung CT image as an example. Retrieving images similar to the input one is helpful to assist physicians in clinical diagnosis. Compared with the traditional content-based image retrieval, the similarity retrieval of lung CT images requires higher retrieval accuracy, with similar requirements in external shape as well as internal vascular and lesion location similarity. In the state-of-the-art supervised deep learning networks, the learning of the network relies on labeling. The labeling of medical images, however, requires time and effort for the professionals to label each image, which is too costly. In this paper, we propose a weakly supervised deep learning network model for similarity analysis of lung CT images that is called a Weakly Supervised similarity Analysis Network(WSAN). Extensive experiments show that the WSAN model achieves satisfactory results in measuring the similarity between lung CT images and can be used for similarity retrieval tasks.
Cloud Object Storage Synchronization: Design, Analysis, and Implementation

Cloud storage synchronization among different computing terminals has attracted large-scale uses among enterprise and individual users. It enables users to maintain the same copy of data in real time, which eases users the tedious yet error-prone data management burden. However, existing cloud storage synchronization systems are in a closed form. Users are fixed to a certain cloud service provider, which makes it hard to transfer from one provider to another when balancing factors such as performance, cost, security, etc. To bridge this gap, this article proposes a new synchronization system based on standard cloud <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">object</i> storage. Specifically, we first formulate the cloud object storage synchronization problem by defining some useful concepts. We then use the idea of state encoding and a push-pull paradigm to propose a cloud object storage synchronization system. The proposed system supports real-time, multiple-terminal, and cloud-independent storage synchronization. We also prototyped the proposed system. The experimental results show that the proposed system is promising for practical usages.
Performing Easley Blackwood’s Twelve Microtonal Etudes: An Open-Source Software Development Project

This paper outlines an open-source development project dedicated to the performance of microtonal music, specifically Easley Blackwood's Twelve Microtonal Etudes (1980). Despite the piece's fixed format, Blackwood stated a specific desire to have the work performed live. This project incorporates multiple elements to this effect, including a standalone software synthesizer and a web application. The paper details prior efforts for microtonal performance on traditional keyboard instruments by Joel Mandelbum and Robert Hasegawa, describes the emergent issues inherent in such endeavors to translate between standard performance practices and microtonal tunings, and proposes methods that will allow for the accurate and easily replicable performance of Blackwood's etudes. The software synthesizer, proposed as a pedagogical design project for computer science students at the University of Central Florida, is currently under active development. The web application, available at the project website, includes source code and documentation for the use of future development teams.
A Hierarchical Color Pattern Excavation Approach

The middle layer feature mining of the image can be used to find the relationship between different visual words, the mining model can be used instead of the original visual word for image representation. At present, most of the mid-level feature mining is carried out for all image blocks, without taking into account the local pattern mining. The local pattern mining is conducive to the discovery of different areas of the object model, the accuracy of image classification is ultimately improved. An effective color hierarchical mining method is proposed in this paper. The judgment of the discriminant color is used in this method as the standard of the hierarchical hierarchy, then the image blocks are minied with these colors in each layer, and finally the excavated pattern is used to represent the image, it is applied for image classification. The experimental results show that the proposed method can achieve good classification effect on Soccer, Flower 17 and Flower 102.
Recognition of Pedestrians’ Crossing Intentions with a Conv-Transformer Network

Recognizing pedestrians’ crossing intentions is critical for self-driving cars to avoid vehicle-pedestrian collisions. However, it is challenging to recognize pedestrians’ crossing intentions based on their appearance or actions under severe conditions, including occlusions, dim illumination, and pedestrians standing far away. In this work, a Conv-transformer network is proposed to recognize pedestrians’ crossing intentions. Pedestrians’ appearance is modeled by a Resnet network, which will output a feature sequence describing pedestrians at different time steps. A Transformer network is then introduced to model the feature sequence to output the recognition result, which is crossing or not crossing. Evaluations on benchmarking databases, including JAAD and PIE, are performed. Results show that the proposed method can recognize pedestrians’ crossing intentions precisely.
Behavior Steganography in Social Network via Secret Sharing

In this paper, we propose a new steganographic scheme based on social network behavior. Different from existing multimedia files based steganographic schemes, our scheme embeds the secret data within the behaviors of individuals in social network. To be specific, we firstly employ the (t, n) secret sharing strategy based on matrix multiplication to divide the secret data into multiple shares, and then transmits them by making “love” mark on the published news of each friend. For the receiver, since each share only contains a small part of valid secret data and a lot of redundant data, secret data can be correctly extracted from the published news of t friends, even if most of them are lost. Experimental results demonstrate the effectiveness of the proposed scheme. Compared with Zhang’s scheme, proposed scheme can obtain higher robustness and larger embedding capacity.
HSCRD: Hybridized Semantic Approach for Knowledge Centric Requirement Discovery

There is a necessity for a requirement recommendation system that can eliminate the old tedious recommendation discovery process. In this paper, a hybrid semantic approach for knowledge-centric requirement discovery has been proposed. The proposed HSCRD framework takes stakeholders’ interactions and preprocesses it. The individual keywords obtained are input into the TF-IDF model to yield the documents from the Requirement Specification Document Repository. The index words of these documents are extracted and are linked with Upper Domain Ontologies. The ontologies are grown by computing the semantic similarity measures, namely, Jaccard similarity and SemantoSim similarity. These grown ontologies are submitted to the Wikidata API, Freebase API, and DBPedia API to yield the Enriched Domain Ontologies. The Enriched Domain Ontologies, as features, are passed into Bi-gram and Tri-gram models. Using Bi-gram and Tri-gram of these features, input is given to the Bagging model for classification. Bagging is chosen with SVM and a highly complex Decision Tree classifier. Finally, the recommended documents and ontologies features are passed individually with respect to the classified documents through TF-IDF and semantic similarity pipeline in order to recommend the individual requirements. The proposed HSCRD achieves the highest average Accuracy with the Precision of 93.18%, Recall of 95.69%, Accuracy of 94.43%, F-Measure of 94.42%, a low FDR of 0.07, and a very high nDCG of 0.96.