Reviewerly/exHarmony huggingface dataset:
Flat-Aware Cross-Stage Distilled Framework for Imbalanced Medical Image Classification

Medical data often follow imbalanced distributions, which poses a long-standing challenge for computer-aided diagnosis systems built upon medical image classification. Most existing efforts are conducted by applying re-balancing methods for the collected training samples, which improves the predictive performance for the minority class but at the cost of decreasing the performance for the majority. To address this paradox, we adopt a flat-aware cross-stage distilled framework (FCD), where we first search for flat local minima of the base training objective function on the original imbalanced dataset, and then continuously finetune this classifier within the flat region on the re-balanced one. To further prevent the performance decreasing for the majority, we propose a cross-stage distillation regularizing term to promote the optimized features to remain in the common optimal subspace. Extensive experiments on two imbalanced medical image datasets demonstrate the effectiveness of our proposed framework and its generality in improving the performance of existing imbalanced methods. The code of this work will be released publicly.
An Overview &amp; Analysis of Sequence-to-Sequence Emotional Voice Conversion

Emotional voice conversion (EVC) focuses on converting a speech utterance from a source to a target emotion; it can thus be a key enabling technology for human-computer interaction applications and beyond.However, EVC remains an unsolved research problem with several challenges.In particular, as speech rate and rhythm are two key factors of emotional conversion, models have to generate output sequences of differing length.Sequence-to-sequence modelling is recently emerging as a competitive paradigm for models that can overcome those challenges.In an attempt to stimulate further research in this promising new direction, recent sequence-to-sequence EVC papers were systematically investigated and reviewed from six perspectives: their motivation, training strategies, model architectures, datasets, model inputs, and evaluation methods.This information is organised to provide the research community with an easily digestible overview of the current state-of-the-art.Finally, we discuss existing challenges of sequence-to-sequence EVC.
A novel automated morphological analysis of Iba1+ microglia using a deep learning assisted model

There is growing evidence for the key role of microglial functional state in brain pathophysiology. Consequently, there is a need for efficient automated methods to measure the morphological changes distinctive of microglia functional states in research settings. Currently, many commonly used automated methods can be subject to sample representation bias, time consuming imaging, specific hardware requirements and difficulty in maintaining an accurate comparison across research environments. To overcome these issues, we use commercially available deep learning tools Aiforia® Cloud (Aifoira Inc., Cambridge, MA, United States) to quantify microglial morphology and cell counts from histopathological slides of Iba1 stained tissue sections. We provide evidence for the effective application of this method across a range of independently collected datasets in mouse models of viral infection and Parkinson's disease. Additionally, we provide a comprehensive workflow with training details and annotation strategies by feature layer that can be used as a guide to generate new models. In addition, all models described in this work are available within the Aiforia® platform for study-specific adaptation and validation.
A Secure and Traceable Vehicles and Parts System Based on Blockchain and Smart Contract

As society advances, so does the total number of vehicles on the road, creating a massive consumer market for automobiles. According to statistics, a major portion of today's traffic difficulties are caused by accidents caused by subpar cars and auto parts. As a result, each country has, over time, enacted equivalent rules and regulations to prevent such tragedies. However, in the face of profit, some people are desperate enough to employ illegal parts and illegally modified cars, and auto fraud is rampant. As a result, we employ the blockchain of the symmetrical Blockchain's digital ledger and smart contract technology to build a decentralized supply chain system that can identify specific parts. In this study, we design and discuss the proposed system framework by user functions and the flow of parts based on blockchain, and we discuss communication protocols that use the symmetry and asymmetry cryptography, algorithms, properties, and security of the mechanism while providing related analysis and comparing the properties and costs of the system with other studies. Overall, the proposed method has the potential to successfully address the issue of automobile fraud.
A Systematic Review of the Design of Augmented Reality Applications for Young Children

Of late, Augmented Reality (AR) has become one of the important tools in teaching and learning, which is made evident by an increasing number of studies focusing on such technology over the past few years. However, studies focusing on the design of AR learning tools or applications for young children have been seriously lacking. Therefore, this paper provides a detailed account of the findings of a study carried out by the authors in which the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) method was used to select and critically review related articles from two leading databases, namely Scopus and Web of Science (WoS) databases. Overall, 16 articles relating to the design and development of AR learning applications were selected from such databases, which revealed five themes and nine subthemes as follows: (1) information design (with three sub-themes), (2) interface design (with one subtheme), (3) interaction design (with two subthemes), (4) imagination (with two subthemes), and (5) immersion (with one subtheme). As such, all five themes relating to the design aspects of AR learning applications need to be carefully addressed and considered in developing efficacious AR learning applications for young children. In summation, the findings of this study based on the systematic review of the current AR literature can be used as a guideline for developers of AR learning applications to ensure their products can be both efficacious and entertaining, the impact of which can surely improve children’s learning performance, motivation, and interest.
Smartphone application use in commercial wild capture fisheries

Abstract Smartphones are increasingly the most common type of mobile phone used throughout the world, offering users the ability to browse the internet and access mobile applications. Smartphones are also often equipped with high definition digital cameras, accelerometers, gyroscopes, and GPS. They can, therefore, facilitate the collection and dissemination of data, often through purpose designed applications (apps). As a result, numerous apps have been developed for use in wild capture fisheries. These apps have been designed for a number of purposes including for data collection, providing information to fishers, being linked to value chains and post-harvest practices and for uses linked to employment, legislation and safety. These apps are used across the world from large commercialized fisheries to small scale fisheries. In the latter, apps have the potential to bridge a technology gap, possibly replicating the functions of multiple pieces of hardware such as are used on larger vessels. This paper presents results from a narrative literature review to provide a synthesis of smart phone apps currently available for use by commercial fishers. 84 smartphone apps were identified as being currently available for use in commercial wild capture fisheries throughout the world. Smartphone apps were available for a number of uses but they were predominantly used for data collection and reporting. While this review provides an important overview of the extent of app use in commercial wild capture fisheries there remains potential for future work to improve understanding of how beneficial app use is and what it is that encourages app use and determines an app as being ‘successful’.
A Secure Batch Authentication Scheme for Multiaccess Edge Computing in 5G-Enabled Intelligent Transportation System

With the introduction of 5G technology in the intelligent transportation system (ITS), data bandwidth over communication channel is significantly increased.In the existing cloud-based ITS, a largescale data is collected, stored, and analyzed to provide precise services to vehicle user, which creates a burden on the cloud service provider that can cause service delays or the inability to provide complex services.Multi-access edge computing (MEC) technology, which extends cloud capabilities to edge servers, is attracting attention in order to reduce the constraint of the cloud-based infrastructure.However, in ITS constructed on MEC, since vehicles exchange information through a wireless communication channel.It is vulnerable to various security attacks.This paper proposes a secure batch authentication scheme for multi-access edge computing in 5G-enabled ITS.The proposed scheme withstands common security attacks including impersonation, replay, man-in-the-middle, physical capture, offline password guessing attacks, and provides secure batch authentication, anonymity and confidentiality.We also consider the vehicle can cross the boundary between MEC servers for continuity of service.We analyze the security of the proposed scheme to show that the proposed scheme is secure against various security attacks, and performance analysis.Lastly, we conduct comparison with other related works.
First-year nursing students’ digital literacy: A cross-sectional study

Background: The digital world is rapidly changing, and so do the required digital skills. As physical devices and software are adapted to meet new possibilities and demands, individuals’ skills must adapt to technological advancement. Digital literacy is increasingly used in the public discourse, becoming a core requirement of students, academics, patients and healthcare professionals. Assessing nursing students’ digital literacy at the entry-level is vital to ascertain their abilities to use digital technologies. This study aimed to assess basic digital literacy among first-year nursing students at a selected university in South Africa.Methods: This cross-sectional study was conducted from 1st March 2019 to 31st May 2019 at a university in South Africa. The population consisted of 82 nursing students from the first-year students in the 4-year nursing programme. The convenience sampling technique was used to determine the participants of this study, and 76 respondents completed a structured questionnaire. Data were analysed descriptively using the Statistical Package for Social Sciences (SPSS®) software (version 25) from the International Business Machines Corporation (IBM®). A reliability test of the instrument was conducted, and Cronbach Alpha was 0.85.Results: The students’ digital literacy included: (i) basic computer skills — performing basic computer operations, MS Word and PowerPoint; (ii) internet skills — using e-mail, Moodle®, social media platforms, accessibility to the internet; and internet searches (iii) digital device usage — desktop, laptops, tablets and smartphones. Overall, the mean for internet skills was 3.61 (SD = ±0.62), 3.11 (SD = ±0.85) for computer skills, and 3.00 (SD = ±0.47) for digital device usage. Computer skills was positively correlated with internet skills (r = 0.278, p = .012) and computer skills corrected with digital device usage (r = 0.384, p &lt; .001). The overall score for internet skills was higher in the female group than in males, with the mean of 4.00 (SD = ±0.62) and 3.61 (SD = ±0.54), respectively (t74 = -0.405, p = .019). Furthermore, the overall score for digital device usage was higher in the age group of 20 years and above with a mean of 3.19 (SD = ±0.38) than in the age group under 20 with a mean of 2.90 (SD = ±0.48), and those differences were statistically significant (t74 = -2.420, p = .018).Conclusions: An adequate digital literacy at the entry-level of the nursing programme is a foundation and a critical factor to academic success and future use of technology in nursing education and practice. Having adequate digital literacy among nursing students would positively impact their ability to perform electronic documentation, communicate and collaborate, and search for information to support evidence-based practice.
Kformer: Knowledge Injection in Transformer Feed-Forward Layers

Recent days have witnessed a diverse set of knowledge injection models for pre-trained language models (PTMs); however, most previous studies neglect the PTMs' own ability with quantities of implicit knowledge stored in parameters. A recent study [2] has observed knowledge neurons in the Feed Forward Network (FFN), which are responsible for expressing factual knowledge. In this work, we propose a simple model, Kformer, which takes advantage of the knowledge stored in PTMs and external knowledge via knowledge injection in Transformer FFN layers. Empirically results on two knowledge-intensive tasks, commonsense reasoning (i.e., SocialIQA) and medical question answering (i.e., MedQA-USMLE), demonstrate that Kformer can yield better performance than other knowledge injection technologies such as concatenation or attention-based injection. We think the proposed simple model and empirical findings may be helpful for the community to develop more powerful knowledge injection methods $$^{1}$$ (Code available in https://github.com/zjunlp/Kformer ).
Voice spoofing countermeasure for voice replay attacks using deep learning

Abstract In our everyday lives, we communicate with each other using several means and channels of communication, as communication is crucial in the lives of humans. Listening and speaking are the primary forms of communication. For listening and speaking, the human voice is indispensable. Voice communication is the simplest type of communication. The Automatic Speaker Verification (ASV) system verifies users with their voices. These systems are susceptible to voice spoofing attacks - logical and physical access attacks. Recently, there has been a notable development in the detection of these attacks. Attackers use enhanced gadgets to record users’ voices, replay them for the ASV system, and be granted access for harmful purposes. In this work, we propose a secure voice spoofing countermeasure to detect voice replay attacks. We enhanced the ASV system security by building a spoofing countermeasure dependent on the decomposed signals that consist of prominent information. We used two main features— the Gammatone Cepstral Coefficients and Mel-Frequency Cepstral Coefficients— for the audio representation. For the classification of the features, we used Bi-directional Long-Short Term Memory Network in the cloud, a deep learning classifier. We investigated numerous audio features and examined each feature’s capability to obtain the most vital details from the audio for it to be labelled genuine or a spoof speech. Furthermore, we use various machine learning algorithms to illustrate the superiority of our system compared to the traditional classifiers. The results of the experiments were classified according to the parameters of accuracy, precision rate, recall, F1-score, and Equal Error Rate (EER). The results were 97%, 100%, 90.19% and 94.84%, and 2.95%, respectively.
Latent feature representation learning for Alzheimer’s disease classification

Early detection and treatment of Alzheimer's Disease (AD) are significant. Recently, multi-modality imaging data have promoted the development of the automatic diagnosis of AD. This paper proposes a method based on latent feature fusion to make full use of multi-modality image data information. Specifically, we learn a specific projection matrix for each modality by introducing a binary label matrix and local geometry constraints and then project the original features of each modality into a low-dimensional target space. In this space, we fuse latent feature representations of different modalities for AD classification. The experimental results on Alzheimer's Disease Neuroimaging Initiative database demonstrate the proposed methods effectiveness in classifying AD.
Recommended Wavelet Based Practices for the Estimation of Electromagnetic Noise in Different Operational Contexts

Signals acquired from an industrial environment with many sources of electromagnetic interferences may be polluted by white noise. The decompositions based on the Stationary and Discrete Wavelet Transformations, respectively the Wavelet Packet Decomposition can be used to estimate the power of noise affecting a certain waveform. Original linear combinations of powers of vectors of details hosted by the nodes of the unbalanced trees generated by the Stationary and Discrete Wavelet Decompositions were conceived in order to evaluate the power of noise which is used afterward by thrashing trees. Tests were made on currents and voltages acquired in different operational contexts. “Smoothed” versions of denoised signals, obtained in an original way with Wavelet Packet Trees were used as reference. The paper is meant to provide a usefull tool for deciding which is the best thrashing practice considering criteria like: minimum relative percentage difference (estimated vs reference) between the powers of noise, maximum deviation, mean square error and runtime. Two wavelet mothers (symlet with filter of 8 components and Daubechies with filter of 28 components) were studied for signals with 512 components per period and trees with 7 levels.
Conflict management techniques for model merging: a systematic mapping review

Model merging conflicts occur when different stakeholders aim to integrate their contradicting changes that are applied concurrently to update software models. We conduct an extensive systematic mapping study on conflict management techniques and relevant collaboration attributes to the versioning and merging models from 2001 to the middle of 2021. This study follows the standard guidelines within the software engineering domain. We analyzed a total of 105 articles extracted from an initial pool of more than 1800 articles to infer a taxonomy for conflict management techniques. We use this taxonomy to classify existing approaches to understand characteristics, shortcomings, and challenges on conflict management techniques in merging models. It also provides a solid foundation for future work in this area. We show that syntactic conflicts are the most studied type and that the top three popular conflict detection techniques are constraint violation, change overlapping, and pattern matching. We observe the lack of a comprehensive state-of-the-art comparison between academic or industrial tools, as well as the need for real-world case studies. Finally, we show that recent trends have focused on online collaboration, where teams of stakeholders work on large-scale models.
A New Approach for Reducing Virtual Reality Sickness in Real Time: Design and Validation Study

Recently, technology that provides virtual reality (VR) content based on streaming services has been rapidly developed. However, there have been few studies to reduce VR sickness that occurs while the user watches VR content while wearing a head-mounted display (HMD) in real time.Based on this background, we propose a new approach to measure and reduce VR sickness that occurs while the user watches VR content while wearing an HMD in real time.The proposed approach is to apply VR sickness reduction methods in accordance with the user's real-time VR sickness level. Three methods that are known to be effective in reducing VR sickness and a single type of VR content were used to examine the effectiveness of the proposed approach, which was confirmed by the experimental results.Our results show that VR sickness significantly decreased when a new approach was applied to VR content (in all cases, P<.05).From our results, it was confirmed that VR sickness could be measured without wearing additional equipment, and its reduction method could be applied in real time in accordance with the user's condition by the proposed approach in this paper.
SupMPN: Supervised Multiple Positives and Negatives Contrastive Learning Model for Semantic Textual Similarity

Semantic Textual Similarity (STS) is an important task in the area of Natural Language Processing (NLP) that measures the similarity of the underlying semantics of two texts. Although pre-trained contextual embedding models such as Bidirectional Encoder Representations from Transformers (BERT) have achieved state-of-the-art performance on several NLP tasks, BERT-derived sentence embeddings have been proven to collapse in some way, i.e., sentence embeddings generated by BERT depend on the frequency of words. Therefore, almost all BERT-derived sentence embeddings are mapped into a small area and have a high cosine similarity. Hence, sentence embeddings generated by BERT are not so robust in the STS task as they cannot capture the full semantic meaning of the sentences. In this paper, we propose SupMPN: A Supervised Multiple Positives and Negatives Contrastive Learning Model, which accepts multiple hard-positive sentences and multiple hard-negative sentences simultaneously and then tries to bring hard-positive sentences closer, while pushing hard-negative sentences away from them. In other words, SupMPN brings similar sentences closer together in the representation space by discrimination among multiple similar and dissimilar sentences. In this way, SupMPN can learn the semantic meanings of sentences by contrasting among multiple similar and dissimilar sentences and can generate sentence embeddings based on the semantic meaning instead of the frequency of the words. We evaluate our model on standard STS and transfer-learning tasks. The results reveal that SupMPN outperforms state-of-the-art SimCSE and all other previous supervised and unsupervised models.
A Study on the Impact of Various Meteorological Data on the Design Performance of Rooftop Solar Power Projects in Vietnam: A Case Study of Electric Power University

People are increasingly using clean energy sources, contributing to environmental protection according to the general trend of the world. In the form of renewable energy, solar energy has contributed to solving current pressing problems, such as environmental pollution and air pollution, improving people’s quality of life. The design of solar power projects in Vietnam is mainly based on meteorological data sources from Meteonorm and NASA. However, the accuracy assessment of two data sources compared to the actual solar power data in Vietnam is not available, so there is no basis to determine better meteorological data source quality to serve the design of rooftop solar power projects. The content of this paper analyzes the simulation results of a typical rooftop solar power station at the Electric Power University, Hanoi city based on meteorological data sources from Meteonorm and NASA. After that, the simulation results will be compared with the actual operating data of a rooftop solar power station near the Electric Power University and other real PV systems in the world. The study results showed that the amount of electricity production using the Meteonorm meteorological data was closer to the actual data than the NASA data source. Therefore, solar power projects in Vietnam should use Meteonorm data source for the design process to determine the best economic and technical efficiency for investors.